how life got complex
TRANSCRIPT
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2014 vol 28
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ldquoVerily at the rst Chaos came to be but next wide-bosomed
Earth the ever-sure foundations of all the deathless ones whohold the peaks of snowy Olympus and dim Tartarus in the
depth of the wide-pathed Earth and Eros fairest among the
deathless gods who unnerves the limbs and overcomes the
mind and wise counsels of all gods and all men within them
From Chaos came forth Erebus and black Night but of Night
were born Aether and Day whom she conceived and bare
from union in love with Erebus And Earth rst bare starry Heaven equal to herself to cover her on every side and to be
an ever-sure abiding-place for the blessed godsrdquo mdash Hesiod
from the Theogony Part 2 translated by HG Evelyn-White
THE CLASSICAL UNIVERSE is made up brick by brick starting inthe void and culminating with the earth rom the emptinesso night that gave rise to day to the day that produces the
outward order o the heavens and finally to lie upon the groundTeTeogony is a story describing the origins o energy and matter andinormation in the orm o lie Te Teogony exemplifies humanityrsquosgreat surprise that the universe should have emerged rom chaosthat emptiness has not reigned eternal and that the earth shouldbe hospitable and supportive o multiorm sentience
Perspectives
TheComplexity of LifeBY DAVID KRAKAUER
DIRECTOR WISCONSIN INSTITUTEFOR DISCOVERY UNIVERSITY
OF WISCONSIN-MADISON
CO-DIRECTOR CENTER FOR COMPLEXITY
amp COLLECTIVE COMPUTATION
WISCONSIN INSTITUTE FOR DISCOVERY
UNIVERSITY OF WISCONSIN-MADISON
EXTERNAL PROFESSOR SANTA FE INSTITUTE
About the Santa Fe Institute
Founded in 1984 the Santa Fe Institute
is an independent nonprofit research
and education center that has pioneered the science of complex systems Its
missions are supported by philanthropic
individuals and foundations forward-
thinking partner companies and
government science agencies
The SFI Bulletin is published periodically to
keep SFIrsquos community informed of its scientific
work The Bulletin is available online either
as a web-based (html5) tablet publication
or as a pdf at wwwsantafeedubulletin
To receive email notifications of future
online issues please subscribe at
wwwsantafeedubulletin To request a
subscription to the printed edition please
email the editor at jdgsantafeedu
Editorial Staff
SFI Chair of the Faculty Jennifer Dunne
SFI Director of Communications John German
Art Director Paula Eastwood Eastwood Design
Design amp Function 8 Arms Creative
Printing Starline Printing
Published by the Santa Fe Institute
1399 Hyde Park Road
Santa Fe New Mexico 87501 USA
Phone 5059848800
Printed On
100 recycled fiber
100 post-consumer waste
Green-e certified
Low-VOC vegetable-based ink
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COVER CHARLES DARWIN ADOC-PHOTOSART RE-SOURCE NEW YORK CIRCUIT SHUTTERSTOCKCOMCOMPOSITE PAULA EASTWOOD
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April 2014 Santa Fe Institute Bulletin 1
ldquo Inorganic chemistry is essentiallysilent on the topic of biology We donot exist The theory of everything
is a theory of everything exceptof those things that theorize
rdquo
David Krakauer
David Krakauer
Afer almost three millennia our concerns are essentially the sameas those o this celebrated Greek armer and poet In just less than 14billion years the universe has generated rom nothing more than100 billion galaxies each o which contains on average 100 billionstars and around many o these stars a system o planets In our ownMilky Way tucked away in a local bubble o the Orion-Cygnus armo the galaxy 27000 light years rom the galactic center spins oursolar system home to eight planets ndash our small and dense and ourlarge and gaseous On one o these planets the third nearest the sun
we find lie o the best o our knowledge it is the only planet inour solar system supporting adaptive matter
From physical law we can derive essential properties o the sunthe elements and the planets Te incredible machinery o thetheories o gravity quantum mechanics and the standard model
give us significant insights into the observable structure in theuniverse Optimistically we can even deduce simple moleculesrom inorganic chemistry And then the theory machine stopsPhysics runs out o gas Chemistry dries up From the perspectiveo physics our own solar system or galaxy are not in any waydifferent rom those anywhere else in the universe Inorganicchemistry is essentially silent on the topic o biology We do notexist Te theory o everything is a theory o everything except othose things that theorize
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2 Santa Fe Institute Bulletin Vol 28
Te projects described in this issue o the Bulletin are allefforts to grapple with some o the key ideas and conceptsrequired to understand living systems with an emphasis onevolution both biological and cultural We ask how overlong stretches o time successively more effective mechanismsor storing and processing inormation have been adaptivelyengineered and how these biological computing systems areused to predict model and control relevant states o noisy andliving environments We consider complexity through the lenso inormation processing we seek to quantiy how inormationis encoded in living systems (genomes and brains) and wesuggest estimates or upper bounds in adaptive inormation
capacity Some o the concepts relevant to understandingbiological complexity include hierarchy individuality criti-cality inormationuncertainty computation and sociality
Stanislaw Lem in his science fiction che-drsquoœuvre Solaris (1961) considers a planet swaddled by an inscrutable oceancapable o astonishing acts o reasoning So vastly more intel-ligent and powerul is the Ocean to the human explorers andscientists (Solarists) who dedicate their lives to its analysisand explication that humanity is orced to resign itsel toignorance over its ultimate mechanisms and motives I haveofen wondered whether Solaris is not a metaphor or lie onearth where the methods o the Solarists are the traditional
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Cronus mutilates his father Uranus at the behest
of his mother Gaia in this scene from Greek
mythology featured in Hesiodrsquos Theogonymethods o science Solaris is a huge interconnected system oenergy and inormation flows that dey traditional methodso reduction Perhaps Solaris is waiting on complexity scienceinormation theory scaling network theory evolutionarydynamics and computation Perhaps we only stand a chanceo understanding complex lie when we approach it throughthe sciences o complexity ndash in which case consider thisissue o the Bulletin a temporary visa granting access to allo those restless explorers intent on the understanding o ourterrestrial Solaris
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4 Santa Fe Institute Bulletin Vol 28
R ESEARCH on the origins development and dynam-ics o complexity in biological systems has been a coretopic o inquiry at the Santa Fe Institute since its
ounding 30 years ago in 1984 SFIrsquos scientists have workedto develop an understanding o a dizzying array o biological
phenomena rom the origins o lie to transitions rom single-to multi-cellularity to evolutionary innovation at different
levels o biological organization to the relationship betweenecological complexity and dynamical stabilityTe intertwined concepts o energy and inormation are
undamental to any understanding o biological complexityBiological systems are ar rom equilibrium requiring a constantflow o energy to maintain their organization and unction-ality Te processing and encoding o inormation providesa means or lie to manage and maintain energy acquisitionuse and dissipation
Te work o ormer resident proessors and current externalaculty members Jessica Flack and David Krakauer (and their manycolleagues) highlighted in this issue o the SFI Bulletin addressesthe intimate dance between energy and inormation processing inbiological systems ndash a dance they suggest gives rise to the complexmulti-scale structure we observe
Tis computational-thermodynamic view o biology provides
a powerul potentially generic ramework or understanding the properties o any kind o complex adaptive system including socio-economic systems
Sincerely
Jennier Dunne Chair o the Faculty Santa Fe Institute
Biological Systems Energy and Information
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April 2014 Santa Fe Institute Bulletin 5
IMAGE CREDITS THIS PAGE FROM TOP BRAIN DRAWING HESSDESIGNWORKSCOM EARTH FROM SPACE NASA ROUSSEAU THE DREAM WIKIMEDIA COMMONSFACING PAGE PORTRAIT OF JENNIFER DUNNE MINESH BACRANIA PHOTOGRAPHY
SFI BULLETIN
VOL 28 APRIL 2014Contents
0 Perspectives The Complexity of Life
By DAVID KRAKAUER
4 Biological Systems Energy and Information
By JENNIFER DUNNE
6 Why Nature Went to the Trouble of CreatingYouBy NATHAN COLLINS
12 Lifersquos Information Hierarchy
By JESSICA FLACK
ldquo There is nothing that living things do that cannot be
understood from the point of view that they are made
of atoms acting according to the laws of physicsrdquo ndash R I C H A R D F E Y N M A N
12
6
0
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April 2014 Santa Fe Institute Bulletin 7April 2014 Santa Fe Institute Bulletin 7
Y pestis the bacterium that causes bubonic
plague (aka The Black Death of the Middle Ages)
Lie on earth began some 35 billion
years ago not all that long afer the planet itsel first ormed and or 15
billion years it chugged along single-celledcreatures sel-replicating dividing diversi-ying Remarkably it took that long ndash 7500times longer than all o human history ndash orthe first multicellular lie to emerge and stilllonger or it to evolve into lie as we know it
Despite that the question manyresearchers ask isnrsquot what took so long
but rather why complex lie would haveevolved in the first place Consider thissingle-celled organisms make up more thanhal o the biomass on earth and even oneo the tiniest organisms ndash Y pestis betterknown as bubonic plague ndash can effortlesslythoughtlessly kill you
Nature it seems doesnrsquot need youIndeed there isnrsquot any obvious reasonit would go to the trouble o creatingsomething as complex as a human beingcomplete with its differentiated organs and
top-down control systems And yet despiteour billion years o Naturersquos great ldquomehrdquohere you are alive multicellular complex
ndash even intelligent enough to ponder yourown existence
What was Nature thinking Accordingto one argument bacteria first boundtogether in colonies that enhanced cooper-ation and hence survival Eventually thosebacteria bound together physically as wellcreating the first multicellular lie Andso on
ldquoYou could say thatrsquos an answer but then you could go a bit urther and ask what isit exactly that makes you a better compet-
itorrdquo says David Krakauer who with SFIExternal Proessor Jessica Flack co-directs
the Wisconsin Institute or DiscoveryrsquosCenter or Complexity and CollectiveComputation or C4 and SFIrsquos Johnempleton Foundation-unded ldquoEvolutiono Complexity and Intelligence on Earthrdquoresearch project
ldquoWell yoursquore outsmarting everyone elserdquoKrakauer says
Simple versus complex
Despite its seeming indifference Naturedoes seem to have thought highly enough
Why Nature Went to the Trouble
of CreatinghellipYou BY NATHAN COLLINS
In a complex world where plants and animals and everything else are duking
it out to survive an organism stands to gain from becoming more complex
o complex structures to produce a ew othem and to have ratcheted up the complex-ity urther by embedding complex structures
within complex structures ndash animals withhearts and lungs and circulatory systems orgroups o people capable o building theirown social institutions
But why What purpose does it serveldquoWhy lie is hierarchically organized isnot at all obviousrdquo Flack says and howan organismrsquos or a societyrsquos complexity
relates to the complexity o its environ-ment remains unclear
Our anthropomorphized Nature mighthave started with one very simple idea
what Krakauer calls the reflection princi- ple which presupposes that living thingscanrsquot be more complex than their environ-ments an idea rooted in experiments ldquoI
you take organisms and you place themin simpler environments they just throweverything [superfluous] away Tey losegenesrdquo Krakauer says
At the same time the world does seem toavor an intelligent creature Even the tiniestliving things need to be able to comprehend predict and react to their environmentsthatrsquos what allows them to outsmart eachother he says In a complex world where
plants and animals and everything else areduking it out to survive an organism standsto gain rom becoming more complex
Tat tension between simplicity andcomplexity is the starting point or C4
postdoctoral ellow Christopher Ellison
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8 Santa Fe Institute Bulletin Vol 28
Watch David Krakauerrsquos interview at
wwwsantafeedulife
What does Nature care about hearts brains and other organs or for that
matter political parties ndash in other words structures within structures
ldquoWersquod like to understand the implica-tions this has or the environmentrdquo hesays ldquoFor example do simple or complexorganisms experience and live in simple orcomplex environmentsrdquo
Working with Flack and KrakauerEllison developed ldquoMarkov organismsrdquo
computer-simulated creatures that merge
insights rom biology with inormation processing techniques rom computerscience to help figure out how lie balancesthese trade-offs Rather than modeling real
organisms themselves he ocuses on howinormation flows in the ecological system
Itrsquos early days Ellison says but his simula-tions suggest that lie will ofen evolve tomatch its environmentrsquos complexity ndash findingsthat are in line with the reflection principlebut with some interesting caveats For one
thing evolving Markov organisms tend toovershoot their worldsrsquocomplexity and mighttake a long time to pruneunnecessary complexities
Teyrsquore also suscepti-ble to ldquobasis mismatchrdquo a
problem you know welli yoursquove ever tried to explain to a tourist howto get around in your hometown o youthere are just a ew steps but to the noviceitrsquos a complex process with many twists and
turns and every intersection represents a possible misdirection in sprawling cities likeLos Angeles a direction as simple as ldquotakeSunset to Vermont and turn lefrdquo becomesinfinitely complex Markov organisms arethe same i their way o solving a problem
doesnrsquot line up with how their environmentsconstructed it Ellisonrsquos simulations showMarkov organismsrsquo complexity keeps evolv-ing upward orever
But Ellisonrsquos inormation-centricapproach has some benefits One he says
ldquois that it attempts to answer the questiono how complexity evolves in an organ-ism-independent ashionrdquo meaning thatthe ideas apply equally well to anythingrom bacteria to politics Similarly Ellisonrsquosmethod allows the team to describe both anorganism and its environmentrsquos complexity
in the same terms because both derive romthe same underlying models o inormation
processing Surprisingly thatrsquos somethingew i any other researchers have done
Constructing predictability
So it appears Nature might avor multicel-lular lie i it affords a certain computational power not readily available to single-celledorganisms But what does Nature care abouthearts brains and other organs or or thatmatter political parties ndash in other words
structures within structureshe answer Flack says is that living
things like their worlds to be predictableand what makes cells and people more likelyto survive Nature avors
Much o the structure we observe in the world Flack says probably evolved becausestructure begets stability hence predictabilityGroups o genes cells or animals changetheir collective behaviors slowly compared
with individual genes cells or animals givingthe aster-moving individual components a
chance to anticipate changes more easilyBiologists call the idea that plants andanimals ndash and genes and organs and so on
ndash structure their environments to be morestable and predictable ldquoniche constructionrdquoand it usually applies to physical structurelike ants building nests But it also can beapplied to temporal structure
Politics offers perhaps a simple exampleEarly US politicians were explicit aboutdesigning Congress and the rest o govern-ment so that it would change gradually and
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April 2014 Santa Fe Institute Bulletin 9April 2014 Santa Fe Institute Bulletin 9
According to the reflection principle organisms are reflections of their environments Here the environment is represented as a prose excerpt from
Darwinrsquos Origin of Species wherein he contemplates an entangled bank filled with ldquoendless forms most beautiful and most wonderfulrdquo each evolved through natural selection Organism A matches the environmentrsquos complexity while Organism B is less complex than the environment and Organism C is
more complex than the environment The research team is asking when evolution satisfies or v iolates the reflection principle
be more stable Even when we complainabout our slow-moving government weare undoubtedly comorted by that verycharacteristic because slow is predictableAnd when our environment is predictable we know how to make sound decisions
So people construct relatively slow-mov-ing predictable institutions At the sametime those institutions help shape thebehavior o the individuals who createdthem points out C4 postdoctoral ellowPhilip Poon who is examining eedbackrom institutions to explain (in the govern-ment case) why or example Democrats andRepublicans seem to trade off controllingthe White House and Congress every ewterms A critical issue is o course howthat eedback works rom a mechanical
perspective ndash that is how the ways individ-uals perceive and understand institutionsinfluence their decision-making
Drawing on the theory o phase transi-tions the same one that explains whychanging a seemingly minor variable cansuddenly shif an entire system rom onestate to another Flack Krakauer and C4researcher and ormer SFI PostdoctoralFellow Bryan Daniels argue that hierarchi-cal structures bestow another advantageeicient inormation low rom the
collective to its individual parts Systems perched on the edge o a phase transitionare exquisitely sensitive so that a small orlocalized change ldquoleads to a large change inthe global dynamicsrdquo Daniels says Toughthat might seem unstable or chaotic systems
near the critical point where a phase transi-
tion begins are actually quite predictableGroups o macaque monkeys ndash one o
Flack and the teamrsquos earliest sources o data
and inspiration ndash are one system that appearsto be resting near a phase transition Whenmonkeys arenrsquot eeling especially aggres-sive Daniels says things are stable and ldquoi Isuddenly act out nothingrsquos going to happenhellipbut i [the group is] sitting at the critical
point then [my] contribution is always moreimportantrdquo and one extra monkey picking afight is enough to kick off a large-scale brawl
Below the critical point individualmonkeys act airly independently but rightat the transition their behavior is tightly
ldquoWhen monkeys arenrsquot feeling especially aggressive
things are stable but if the group is sitting at the
critical point one extra monkey picking a fight
is enough to kick off a large-scale brawlrdquo
coordinated and individual monkeys acttogether as one Tat Krakauer empha-sizes eases the flow o inormation romthe system as a whole to its constituent
parts making it all the more predictableNature is rie with examples one birdrsquos
sudden course correction changing the
direction o an entire flock alternatingDemocrat and Republican control oCongress and so on
Social circuitry
In the abstract Nature has good reasonto avor complexity and hierarchy ndash eachin its own way makes the tasks o compre-hension prediction and strategizingeasier But its a third concept circuitsthat grounds Flack and Krakauerrsquos teamand lays the practical oundation ormuch o their work
Usually the word ldquocircuitrdquo conjuresimages o the transistors microprocessors
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Time series representation of the dis tribution of fight sizes in a macaque society Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once An outburst by an individual wonrsquot normally tip the scales But when the system is perched at
a phase transition a brawl among many individuals can erupt
and lengths o copper wire that make upa computer Te analogy is apt Like anelectronic circuit individual components
ndash genes organs people ndash inormational-ly bind living things to orm a kind obiological or social computer In act the
circuit approach to describing a systemstems rom a hunch that the hierarchicalscales present throughout nature ldquoarise
Time
F i g h
t 1
F i g h
t 2
Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once
Vd
Pa
Je
Cb
Yv
Ta
Sg
Qs
Po
Mv
Ip
Fp
Eo
Dh
Tr
Pr
Fc
Zu
Vf
Fp
Ec
Zu
Zr
Iw
Is
Fp
Fc
Cc
Yv
Cd
Ta
Cc
Qv
Ip
Cb
Pa
Eb
Sb
Jc
Je
Hp
Lr
Jc
Zu
Po
Dh
Vf
Eo
Eo
Cd
Pa
Dh
Hh
At
Zu
Yv
Th
Ta
Fn
through a process o collective computa-tionrdquo creating slow-changing predictablesocial and biological structures Flack says
Circuits are more than just an analo-gy though ndash theyrsquore the tools that bridgethe gap between individual and collective
behavior And in a scientific field whereitrsquos easy to avoid real data circuits are one
way Flack and Krakauerrsquos research group
makes sure their theories are a good matchto the real world ldquoOur group is committedto an empirical approachrdquo Flack says ldquoWebelieve that only when these measures aredeveloped with an understanding o thedata generated by real systems will they
be useulrdquoTe process o building circuits begins
by analyzing how a systemrsquos individual parts work together Using the macaquefight data Flack Krakauer and ormerSFI Omidyar Fellow Simon DeDeo (nowat Indiana University Bloomington) devel-oped a statistical method they dubbedinductive game theory to analyze howthe monkeys reacted to othersrsquo fights Teresulting social circuit Flack says servesas a detailed model ldquoor how the micro-
scopic behavior maps to the unctionallyimportant macroscopic eatures o socialstructurerdquo such as the distribution o fightsizes In other words to construct a socialor biological circuit is to understand how agroup builds and maintains stable predict-able inormation hierarchies
Te final step is to produce a simplifiedsocial circuit what the researchers call a
ldquocognitive effective theoryrdquo that accurate-ly predicts how groups behave Te aimis to extract the key sorts o interactions
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April 2014 Santa Fe Institute Bulletin 11
responsible or power structures fight-sizedistributions or other macroscopic eaturesusing ldquowhat we know about individual orcomponent cognition to coarse grain orcompressrdquo social circuits Flack says Suchcompression is essential she says because
living things canrsquot base their decisions on what ever y other living thing is doing instead theyrsquore orced to pay attention to
just a ew patterns or details o whatrsquos goingon around them
Te key question here as collaboratorand Princeton University graduate studentEleanor Brush puts it is how little inor-mation individuals need to successullyoutsmart others
Accidental or inevitable
Answering questions like that one ndash or testingsome o the teamrsquos more abstract predic-tions ndash remains a central challenge Poonor example describes his studies o electioncycles and policy change as ldquotoy modelsrdquo ndashthey capture qualitative eatures o the datasuch as party switching but donrsquot stand upto more precise quantitative tests
Meanwhile Krakauer and others sayitrsquos not always clear how to test particularhypotheses such as the prediction that basismismatch leads to ever-increasing complexity
in living things ldquoWersquore still looking or somecompelling examplerdquo Ellison says ldquoPart othe issue is that one can ofen play the devilrsquosadvocate and call into question the examplerdquoone reason why his work to construct ormal
precise measures o complexity is so import-ant he says
New techniques or rapidly analyzinggenetic data Krakauer says might improvethe situation Combining those techniques
with laboratory-based ldquoexperimental evolutionrdquo
in which researchers study the effects o preciseenvironmental changes on small organismssuch as bacteria could help test some o theendeavorrsquos core ideas such as the reflection
principle or the role o phase transitionsAnother potential avenue is to use ldquodigital
sources like computer games where we cancontrol to a large extent the orm o the dataor the conditions under which they werecollectedrdquo Krakauer says
esting their theories is just one part o theteamrsquos ambitious aims Tey hope Flack says
People gather in Washington DC before Martin Luther King Jrrsquos ldquoI Have a Dreamrdquo speech on Aug 28 1963 to demand equalities
Individuals tend to move faster and be less predictable than the slow-moving institutions they create
to achieve nothing less than an understandingo why lie is organized the way it is rom thesmallest bacteria to the largest human insti-tutions Tat requires combining real-worldobservation and abstract mathematical theoryin novel and creative ways
And as i that wasnrsquot enough Krakauerhas one more question in mind is lie anaccident or is it inevitable And i lie isinevitable well are we alone
ldquoI itrsquos not a product o a series o random
accidents but therersquos an underlying law-likeregularity that would give us confidence inbelieving in the possibility o lie presenteverywhere in the universerdquo he says ldquoSo
when one asks why does it matter whetheritrsquos chance or necessity it matters i we care
whether wersquore alone are notrdquo
Nathan Collins is a feelance science writernew ather and film aficionado based inSan Francisco
ldquoLiving things canrsquot base their decisions on what
every other living thing is doing instead theyrsquore
forced to pay attention to just a few patternsrdquo
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12 Santa Fe Institute Bulletin Vol 28
ldquohellipin mere Time all things follow
one another and in mere Space
all things are side by side it is
accordingly only by the combi-
nation of Time and Space thatthe representation of coexistence
arisesrdquo
mdash ARTHUR SCHOPENHAUER On the Fourfold Root
of the Principle of Sufficient Reason 1813
Yves Tanguy Indefinite Divisibility 1942
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April 2014 Santa Fe Institute Bulletin 13
D I A C O M M O N S I N S E T H E N R I R O U S S E A U T H E
D R E A M 1 9 1 0 W I K I M E D I A C O M M O N S
Biological systems ndash rom cells totissues to individuals to societ-ies ndash are hierarchically organized
(eg Feldman amp Eschel 1982 Buss 1987Smith amp Szathmaacutery 1998 Valentineamp May 1996 Michod 2000 Frank2003) o many hierarchical organiza-tion suggests the nesting o components orindividuals into groups with these groupsaggregating into yet larger groups But this view ndash at least superficially ndash privilegesspace and matter over time and inorma-tion Many types o neural coding orexample require averaging or summing over neural firing ratesTe neuronsrsquo spatial location ndash that they are in proximity ndash iso course important but at least as important to the encoding
The explanation for the complex multi-scale structure of biological and social systems
lies in their manipulation of space and time to reduce uncertainty about the future
is their behavior in time Likewise insome monkey societies as I will discussin detail later in this review individualsestimate the uture cost o social inter-action by encoding the average outcomeo past interactions in special signals and
then summing over these signalsIn both examples inormation rom
events distributed in time as well as space(Figure 1) is captured with encodingsthat are used to control some behavioraloutput My collaborators and I in theCenter or Complexity amp Collective
Computation are exploring the idea that hierarchical organiza-tion at its core is a nesting o these kinds o unctional encodingsAs I will explain we think these unctional encodings result
LIFErsquoS INFORMATION HIERARCHY
BY JESSICA C FLACK
CO-DIRECTOR CENTER FOR COMPLEXITY amp COLLECTIVE COMPUTATION
WISCONSIN INSTITUTE FOR DISCOVERY UNIVERSITY OF WISCONSIN-MADISON
EXTERNAL PROFESSOR SANTA FE INSTITUTE
Jessica Flack
Number of spatial dimensions
N u m b e
r o f t i m e d i m e n s i o n s
0
0
1
2
3
4
5
1 2 3 4 5
UNSTABLE
TOO SIMPLE
UNPREDICTABLE (ELLIPTIC)
UNPREDICTABLE(ULTRAHYPERBOLIC)
TACHYONSONLY
UNSTABLE
Figure 1 The dimensionality of the time-space
continuum with properties postulated when x
does not equal 3 and y is larger than 1 Life on
earth exist s in three spatial dimensions and one
temporal dimension Biological systems effec-
tively ldquodiscretizerdquo time and space to reduce
environmental uncertainty by coarse-graining
and compressing environmental time series to
find regularities Components use the coarse-
grained descriptions to predict the future
tuning their behavior to their predictions
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14 Santa Fe Institute Bulletin Vol 28
rom biological systemsmanipulating space andtime (Figure 2) to acilitateinormation extraction which in turn acilitatesmore efficient extraction
o energy h i s inormation
hierarchy appears to bea universal property obiological systems andmay be the key to one oliersquos greatest mysteries ndashthe origins o biologicalcomplexity In this essayI review a body o work byDavid Krakauer myseland our research group
that has been inspiredby many years o work atthe Santa Fe Institute (eg Crutchfield 1994 Gell-Mann1996 Gell-Mann amp Lloyd 1996 Fontana amp Buss 1996 West Brown amp Enquist 1997 Fontana amp Schuster 1998Ancel amp Fontana 2000 Stadler Stadler Wagner amp Fontana2001 Smith 2003 Crutchfield amp Goumlrnerup 2006 Smith2008) Our work suggests that complexity and the multi-scalestructure o biological systems are the predictable outcomeo evolutionary dynamics driven by uncertainty minimiza-tion (Krakauer 2011 Flack 2012 Flack Erwin Elliot ampKrakauer 2013)
Tis recasting o the evolutionary process as an inerentialone1 (Bergstrom amp Rosvall 2009 Krakauer 2011) is based onthe premise that organisms and other biological systems canbe viewed as hypotheses about the present and uture environ-ments they or their offspring will encounter induced rom thehistory o past environmental states they or their ancestors have
experienced (eg Crutchfield amp Feldman 2001 Krakauer ampZannotto 2009 Ellison Flack amp Krakauer in prep) Tis premise o course only holds i the past is prologue ndash that ishas regularities and the regularities can be estimated and evenmanipulated (as in niche construction) by biological systems ortheir components to produce adaptive behavior (Flack ErwinElliot amp Krakauer 2013 Ellison Flack amp Krakauer in prep)
I these premises are correct lie at its core is computa-tional and a central question becomes How do systems andtheir components estimate and control the regularity in theirenvironments and use these estimates to tune their strategiesI suggest that the answer to this question and the explanation
or complexity is that biological systems manipulate spatialand temporal structure to produce order ndash low variance ndash atlocal scales
UNCERTAINTY REDUCTION
Te story I want to tell starts with the observation that witheach new level o organization typically comes new unction-ality ndash a new eature with positive payoff consequences or thesystem as a whole or or its components (Flack Erwin Elliot
1 Tis idea is related to work on Maxwel lrsquos Demon (eg Krakauer2011 Mandal 983121uan amp Jarzynski 2013) and the Carnot cycle ( egSmith 2003) but we do not yet understand the mapping
Figure 2 Biological systems ndash from
(left to right ) Volvox colonies to slime
molds to animal societies to large-scale
ecosystems such as reefs t o human
cities ndash are hierarchically organized with
multiple functionally important time and
space scales All feature 1) components
with only partially aligned interests
exhibiting coherent behavior at the
aggregate level 2) components that turn
over and that co-exist in the sys tem at
varying stages of development
3) social structure that persists but
component behavior that fluctuates and
4) macroscopic variation in temporal
and spatial structure and coupling with
microscopic behavior which has func-
tional impl ications when the components
can perceive ndash in evolutionary develop-
mental or ecological time ndash regularities
at the macroscopic scale
1 2
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April 2014 Santa Fe Institute Bulletin 15
amp Krakauer 2013) Policing in a pigtailed macaque group is anexample Once a heavy tailed distribution o power ndash defined asthe degree o consensus in the group that an individual can winfights (see Flack amp Krakauer 2006 Boehm amp Flack 2010 BrushKrakauer amp Flack2013) ndash becomes effec-
tively institutionalized(here meaning hard tochange) policing (anintrinsically costlystrategy) becomesaffordable at least tothose animals that sitin the tail o the powerdistribution thosesuper powerul monkeys who are rarely or never challenged when they break up fights (Flack de Waal amp Krakauer 2005Flack Girvan de Waal amp Krakauer 2006)
My collaborators and I propose that a primary driver o theemergence o new unctionality such as policing is the reduc-tion o environmental uncertainty through the constructiono nested dynamical processes with a range o characteris-tic time constants (Flack Erwin Elliot amp Krakauer 2013)Tese nested dynamical processes arise as components extractregularities rom ast microscopic behavior by coarse-grain-ing (or compressing) the history o events to which they havebeen exposed
Proteins or example can have a long hal-lie relative toRNA transcripts and can be thought o as the summed outputo translation Cells have a long hal-lie relative to proteins
and are a unction o the summed output o arrays o spatiallystructured proteins Both proteins and cells represent someaverage measure o the noisier activity o their constituentsSimilarly a pigtailed macaquersquos estimate o its power is a kind
o average measure othe collective percep-
tion in the groupthat the macaque iscapable o winningights and this is abetter predictor othe cost the macaque will pay during fightsthan the outcome oany single melee as
these outcomes can fluctuate or contextual reasons Tesecoarse-grainings or averages are encoded as slow variables(Flack amp de Waal 2007 Flack 2012 Flack Erwin Elliot
amp Krakauer 2013 see also Feret Danos Krivine Harner ampFontana 2009 or a similar idea) Slow variables may have aspatial component as well as a temporal component as in the protein and cell examples (Figure 6) or minimally only atemporal component as in the monkey example
As a consequence o integrating over abundant microscopic processes slow variables provide better predictors o the localuture configuration o a system than the states o the fluctuatingmicroscopic components In doing so they promote acceleratedrates o microscopic adaptation Slow variables acilitate adapta-tion in two ways Tey allow components to fine-tune theirbehavior and they ree components to search at low cost a larger
3 4
Slow variables provide better predictors
of the local future configuration of a
system than the states of the fluctuating
microscopic componentsrdquo
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Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and
proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since
fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous
starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of
relevant genes and proteins and the output is the target phenot ypic feature
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April 2014 Santa Fe Institute Bulletin 17
space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
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A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
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April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
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22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
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April 2014 Santa Fe Institute Bulletin 25
More than 70 of our annual
operating budget comes from
private sources - forward-thinking
individuals corporations and
foundations If you believe
complex systems science can
generate the insights that matter
most for science and society makea gift today Visit our website at
wwwsantafeedusupport and
help us achieve great things
ITrsquoS SIMPLE
Support
ComplexityScience
8122019 How Life Got Complex
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8122019 How Life Got Complex
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ldquoVerily at the rst Chaos came to be but next wide-bosomed
Earth the ever-sure foundations of all the deathless ones whohold the peaks of snowy Olympus and dim Tartarus in the
depth of the wide-pathed Earth and Eros fairest among the
deathless gods who unnerves the limbs and overcomes the
mind and wise counsels of all gods and all men within them
From Chaos came forth Erebus and black Night but of Night
were born Aether and Day whom she conceived and bare
from union in love with Erebus And Earth rst bare starry Heaven equal to herself to cover her on every side and to be
an ever-sure abiding-place for the blessed godsrdquo mdash Hesiod
from the Theogony Part 2 translated by HG Evelyn-White
THE CLASSICAL UNIVERSE is made up brick by brick starting inthe void and culminating with the earth rom the emptinesso night that gave rise to day to the day that produces the
outward order o the heavens and finally to lie upon the groundTeTeogony is a story describing the origins o energy and matter andinormation in the orm o lie Te Teogony exemplifies humanityrsquosgreat surprise that the universe should have emerged rom chaosthat emptiness has not reigned eternal and that the earth shouldbe hospitable and supportive o multiorm sentience
Perspectives
TheComplexity of LifeBY DAVID KRAKAUER
DIRECTOR WISCONSIN INSTITUTEFOR DISCOVERY UNIVERSITY
OF WISCONSIN-MADISON
CO-DIRECTOR CENTER FOR COMPLEXITY
amp COLLECTIVE COMPUTATION
WISCONSIN INSTITUTE FOR DISCOVERY
UNIVERSITY OF WISCONSIN-MADISON
EXTERNAL PROFESSOR SANTA FE INSTITUTE
About the Santa Fe Institute
Founded in 1984 the Santa Fe Institute
is an independent nonprofit research
and education center that has pioneered the science of complex systems Its
missions are supported by philanthropic
individuals and foundations forward-
thinking partner companies and
government science agencies
The SFI Bulletin is published periodically to
keep SFIrsquos community informed of its scientific
work The Bulletin is available online either
as a web-based (html5) tablet publication
or as a pdf at wwwsantafeedubulletin
To receive email notifications of future
online issues please subscribe at
wwwsantafeedubulletin To request a
subscription to the printed edition please
email the editor at jdgsantafeedu
Editorial Staff
SFI Chair of the Faculty Jennifer Dunne
SFI Director of Communications John German
Art Director Paula Eastwood Eastwood Design
Design amp Function 8 Arms Creative
Printing Starline Printing
Published by the Santa Fe Institute
1399 Hyde Park Road
Santa Fe New Mexico 87501 USA
Phone 5059848800
Printed On
100 recycled fiber
100 post-consumer waste
Green-e certified
Low-VOC vegetable-based ink
wwwsantafeedu
COVER CHARLES DARWIN ADOC-PHOTOSART RE-SOURCE NEW YORK CIRCUIT SHUTTERSTOCKCOMCOMPOSITE PAULA EASTWOOD
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 1
ldquo Inorganic chemistry is essentiallysilent on the topic of biology We donot exist The theory of everything
is a theory of everything exceptof those things that theorize
rdquo
David Krakauer
David Krakauer
Afer almost three millennia our concerns are essentially the sameas those o this celebrated Greek armer and poet In just less than 14billion years the universe has generated rom nothing more than100 billion galaxies each o which contains on average 100 billionstars and around many o these stars a system o planets In our ownMilky Way tucked away in a local bubble o the Orion-Cygnus armo the galaxy 27000 light years rom the galactic center spins oursolar system home to eight planets ndash our small and dense and ourlarge and gaseous On one o these planets the third nearest the sun
we find lie o the best o our knowledge it is the only planet inour solar system supporting adaptive matter
From physical law we can derive essential properties o the sunthe elements and the planets Te incredible machinery o thetheories o gravity quantum mechanics and the standard model
give us significant insights into the observable structure in theuniverse Optimistically we can even deduce simple moleculesrom inorganic chemistry And then the theory machine stopsPhysics runs out o gas Chemistry dries up From the perspectiveo physics our own solar system or galaxy are not in any waydifferent rom those anywhere else in the universe Inorganicchemistry is essentially silent on the topic o biology We do notexist Te theory o everything is a theory o everything except othose things that theorize
8122019 How Life Got Complex
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2 Santa Fe Institute Bulletin Vol 28
Te projects described in this issue o the Bulletin are allefforts to grapple with some o the key ideas and conceptsrequired to understand living systems with an emphasis onevolution both biological and cultural We ask how overlong stretches o time successively more effective mechanismsor storing and processing inormation have been adaptivelyengineered and how these biological computing systems areused to predict model and control relevant states o noisy andliving environments We consider complexity through the lenso inormation processing we seek to quantiy how inormationis encoded in living systems (genomes and brains) and wesuggest estimates or upper bounds in adaptive inormation
capacity Some o the concepts relevant to understandingbiological complexity include hierarchy individuality criti-cality inormationuncertainty computation and sociality
Stanislaw Lem in his science fiction che-drsquoœuvre Solaris (1961) considers a planet swaddled by an inscrutable oceancapable o astonishing acts o reasoning So vastly more intel-ligent and powerul is the Ocean to the human explorers andscientists (Solarists) who dedicate their lives to its analysisand explication that humanity is orced to resign itsel toignorance over its ultimate mechanisms and motives I haveofen wondered whether Solaris is not a metaphor or lie onearth where the methods o the Solarists are the traditional
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 3
Cronus mutilates his father Uranus at the behest
of his mother Gaia in this scene from Greek
mythology featured in Hesiodrsquos Theogonymethods o science Solaris is a huge interconnected system oenergy and inormation flows that dey traditional methodso reduction Perhaps Solaris is waiting on complexity scienceinormation theory scaling network theory evolutionarydynamics and computation Perhaps we only stand a chanceo understanding complex lie when we approach it throughthe sciences o complexity ndash in which case consider thisissue o the Bulletin a temporary visa granting access to allo those restless explorers intent on the understanding o ourterrestrial Solaris
8122019 How Life Got Complex
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4 Santa Fe Institute Bulletin Vol 28
R ESEARCH on the origins development and dynam-ics o complexity in biological systems has been a coretopic o inquiry at the Santa Fe Institute since its
ounding 30 years ago in 1984 SFIrsquos scientists have workedto develop an understanding o a dizzying array o biological
phenomena rom the origins o lie to transitions rom single-to multi-cellularity to evolutionary innovation at different
levels o biological organization to the relationship betweenecological complexity and dynamical stabilityTe intertwined concepts o energy and inormation are
undamental to any understanding o biological complexityBiological systems are ar rom equilibrium requiring a constantflow o energy to maintain their organization and unction-ality Te processing and encoding o inormation providesa means or lie to manage and maintain energy acquisitionuse and dissipation
Te work o ormer resident proessors and current externalaculty members Jessica Flack and David Krakauer (and their manycolleagues) highlighted in this issue o the SFI Bulletin addressesthe intimate dance between energy and inormation processing inbiological systems ndash a dance they suggest gives rise to the complexmulti-scale structure we observe
Tis computational-thermodynamic view o biology provides
a powerul potentially generic ramework or understanding the properties o any kind o complex adaptive system including socio-economic systems
Sincerely
Jennier Dunne Chair o the Faculty Santa Fe Institute
Biological Systems Energy and Information
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April 2014 Santa Fe Institute Bulletin 5
IMAGE CREDITS THIS PAGE FROM TOP BRAIN DRAWING HESSDESIGNWORKSCOM EARTH FROM SPACE NASA ROUSSEAU THE DREAM WIKIMEDIA COMMONSFACING PAGE PORTRAIT OF JENNIFER DUNNE MINESH BACRANIA PHOTOGRAPHY
SFI BULLETIN
VOL 28 APRIL 2014Contents
0 Perspectives The Complexity of Life
By DAVID KRAKAUER
4 Biological Systems Energy and Information
By JENNIFER DUNNE
6 Why Nature Went to the Trouble of CreatingYouBy NATHAN COLLINS
12 Lifersquos Information Hierarchy
By JESSICA FLACK
ldquo There is nothing that living things do that cannot be
understood from the point of view that they are made
of atoms acting according to the laws of physicsrdquo ndash R I C H A R D F E Y N M A N
12
6
0
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8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 7April 2014 Santa Fe Institute Bulletin 7
Y pestis the bacterium that causes bubonic
plague (aka The Black Death of the Middle Ages)
Lie on earth began some 35 billion
years ago not all that long afer the planet itsel first ormed and or 15
billion years it chugged along single-celledcreatures sel-replicating dividing diversi-ying Remarkably it took that long ndash 7500times longer than all o human history ndash orthe first multicellular lie to emerge and stilllonger or it to evolve into lie as we know it
Despite that the question manyresearchers ask isnrsquot what took so long
but rather why complex lie would haveevolved in the first place Consider thissingle-celled organisms make up more thanhal o the biomass on earth and even oneo the tiniest organisms ndash Y pestis betterknown as bubonic plague ndash can effortlesslythoughtlessly kill you
Nature it seems doesnrsquot need youIndeed there isnrsquot any obvious reasonit would go to the trouble o creatingsomething as complex as a human beingcomplete with its differentiated organs and
top-down control systems And yet despiteour billion years o Naturersquos great ldquomehrdquohere you are alive multicellular complex
ndash even intelligent enough to ponder yourown existence
What was Nature thinking Accordingto one argument bacteria first boundtogether in colonies that enhanced cooper-ation and hence survival Eventually thosebacteria bound together physically as wellcreating the first multicellular lie Andso on
ldquoYou could say thatrsquos an answer but then you could go a bit urther and ask what isit exactly that makes you a better compet-
itorrdquo says David Krakauer who with SFIExternal Proessor Jessica Flack co-directs
the Wisconsin Institute or DiscoveryrsquosCenter or Complexity and CollectiveComputation or C4 and SFIrsquos Johnempleton Foundation-unded ldquoEvolutiono Complexity and Intelligence on Earthrdquoresearch project
ldquoWell yoursquore outsmarting everyone elserdquoKrakauer says
Simple versus complex
Despite its seeming indifference Naturedoes seem to have thought highly enough
Why Nature Went to the Trouble
of CreatinghellipYou BY NATHAN COLLINS
In a complex world where plants and animals and everything else are duking
it out to survive an organism stands to gain from becoming more complex
o complex structures to produce a ew othem and to have ratcheted up the complex-ity urther by embedding complex structures
within complex structures ndash animals withhearts and lungs and circulatory systems orgroups o people capable o building theirown social institutions
But why What purpose does it serveldquoWhy lie is hierarchically organized isnot at all obviousrdquo Flack says and howan organismrsquos or a societyrsquos complexity
relates to the complexity o its environ-ment remains unclear
Our anthropomorphized Nature mighthave started with one very simple idea
what Krakauer calls the reflection princi- ple which presupposes that living thingscanrsquot be more complex than their environ-ments an idea rooted in experiments ldquoI
you take organisms and you place themin simpler environments they just throweverything [superfluous] away Tey losegenesrdquo Krakauer says
At the same time the world does seem toavor an intelligent creature Even the tiniestliving things need to be able to comprehend predict and react to their environmentsthatrsquos what allows them to outsmart eachother he says In a complex world where
plants and animals and everything else areduking it out to survive an organism standsto gain rom becoming more complex
Tat tension between simplicity andcomplexity is the starting point or C4
postdoctoral ellow Christopher Ellison
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8 Santa Fe Institute Bulletin Vol 28
Watch David Krakauerrsquos interview at
wwwsantafeedulife
What does Nature care about hearts brains and other organs or for that
matter political parties ndash in other words structures within structures
ldquoWersquod like to understand the implica-tions this has or the environmentrdquo hesays ldquoFor example do simple or complexorganisms experience and live in simple orcomplex environmentsrdquo
Working with Flack and KrakauerEllison developed ldquoMarkov organismsrdquo
computer-simulated creatures that merge
insights rom biology with inormation processing techniques rom computerscience to help figure out how lie balancesthese trade-offs Rather than modeling real
organisms themselves he ocuses on howinormation flows in the ecological system
Itrsquos early days Ellison says but his simula-tions suggest that lie will ofen evolve tomatch its environmentrsquos complexity ndash findingsthat are in line with the reflection principlebut with some interesting caveats For one
thing evolving Markov organisms tend toovershoot their worldsrsquocomplexity and mighttake a long time to pruneunnecessary complexities
Teyrsquore also suscepti-ble to ldquobasis mismatchrdquo a
problem you know welli yoursquove ever tried to explain to a tourist howto get around in your hometown o youthere are just a ew steps but to the noviceitrsquos a complex process with many twists and
turns and every intersection represents a possible misdirection in sprawling cities likeLos Angeles a direction as simple as ldquotakeSunset to Vermont and turn lefrdquo becomesinfinitely complex Markov organisms arethe same i their way o solving a problem
doesnrsquot line up with how their environmentsconstructed it Ellisonrsquos simulations showMarkov organismsrsquo complexity keeps evolv-ing upward orever
But Ellisonrsquos inormation-centricapproach has some benefits One he says
ldquois that it attempts to answer the questiono how complexity evolves in an organ-ism-independent ashionrdquo meaning thatthe ideas apply equally well to anythingrom bacteria to politics Similarly Ellisonrsquosmethod allows the team to describe both anorganism and its environmentrsquos complexity
in the same terms because both derive romthe same underlying models o inormation
processing Surprisingly thatrsquos somethingew i any other researchers have done
Constructing predictability
So it appears Nature might avor multicel-lular lie i it affords a certain computational power not readily available to single-celledorganisms But what does Nature care abouthearts brains and other organs or or thatmatter political parties ndash in other words
structures within structureshe answer Flack says is that living
things like their worlds to be predictableand what makes cells and people more likelyto survive Nature avors
Much o the structure we observe in the world Flack says probably evolved becausestructure begets stability hence predictabilityGroups o genes cells or animals changetheir collective behaviors slowly compared
with individual genes cells or animals givingthe aster-moving individual components a
chance to anticipate changes more easilyBiologists call the idea that plants andanimals ndash and genes and organs and so on
ndash structure their environments to be morestable and predictable ldquoniche constructionrdquoand it usually applies to physical structurelike ants building nests But it also can beapplied to temporal structure
Politics offers perhaps a simple exampleEarly US politicians were explicit aboutdesigning Congress and the rest o govern-ment so that it would change gradually and
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 9April 2014 Santa Fe Institute Bulletin 9
According to the reflection principle organisms are reflections of their environments Here the environment is represented as a prose excerpt from
Darwinrsquos Origin of Species wherein he contemplates an entangled bank filled with ldquoendless forms most beautiful and most wonderfulrdquo each evolved through natural selection Organism A matches the environmentrsquos complexity while Organism B is less complex than the environment and Organism C is
more complex than the environment The research team is asking when evolution satisfies or v iolates the reflection principle
be more stable Even when we complainabout our slow-moving government weare undoubtedly comorted by that verycharacteristic because slow is predictableAnd when our environment is predictable we know how to make sound decisions
So people construct relatively slow-mov-ing predictable institutions At the sametime those institutions help shape thebehavior o the individuals who createdthem points out C4 postdoctoral ellowPhilip Poon who is examining eedbackrom institutions to explain (in the govern-ment case) why or example Democrats andRepublicans seem to trade off controllingthe White House and Congress every ewterms A critical issue is o course howthat eedback works rom a mechanical
perspective ndash that is how the ways individ-uals perceive and understand institutionsinfluence their decision-making
Drawing on the theory o phase transi-tions the same one that explains whychanging a seemingly minor variable cansuddenly shif an entire system rom onestate to another Flack Krakauer and C4researcher and ormer SFI PostdoctoralFellow Bryan Daniels argue that hierarchi-cal structures bestow another advantageeicient inormation low rom the
collective to its individual parts Systems perched on the edge o a phase transitionare exquisitely sensitive so that a small orlocalized change ldquoleads to a large change inthe global dynamicsrdquo Daniels says Toughthat might seem unstable or chaotic systems
near the critical point where a phase transi-
tion begins are actually quite predictableGroups o macaque monkeys ndash one o
Flack and the teamrsquos earliest sources o data
and inspiration ndash are one system that appearsto be resting near a phase transition Whenmonkeys arenrsquot eeling especially aggres-sive Daniels says things are stable and ldquoi Isuddenly act out nothingrsquos going to happenhellipbut i [the group is] sitting at the critical
point then [my] contribution is always moreimportantrdquo and one extra monkey picking afight is enough to kick off a large-scale brawl
Below the critical point individualmonkeys act airly independently but rightat the transition their behavior is tightly
ldquoWhen monkeys arenrsquot feeling especially aggressive
things are stable but if the group is sitting at the
critical point one extra monkey picking a fight
is enough to kick off a large-scale brawlrdquo
coordinated and individual monkeys acttogether as one Tat Krakauer empha-sizes eases the flow o inormation romthe system as a whole to its constituent
parts making it all the more predictableNature is rie with examples one birdrsquos
sudden course correction changing the
direction o an entire flock alternatingDemocrat and Republican control oCongress and so on
Social circuitry
In the abstract Nature has good reasonto avor complexity and hierarchy ndash eachin its own way makes the tasks o compre-hension prediction and strategizingeasier But its a third concept circuitsthat grounds Flack and Krakauerrsquos teamand lays the practical oundation ormuch o their work
Usually the word ldquocircuitrdquo conjuresimages o the transistors microprocessors
8122019 How Life Got Complex
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10 Santa Fe Institute Bulletin Vol 28
Time series representation of the dis tribution of fight sizes in a macaque society Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once An outburst by an individual wonrsquot normally tip the scales But when the system is perched at
a phase transition a brawl among many individuals can erupt
and lengths o copper wire that make upa computer Te analogy is apt Like anelectronic circuit individual components
ndash genes organs people ndash inormational-ly bind living things to orm a kind obiological or social computer In act the
circuit approach to describing a systemstems rom a hunch that the hierarchicalscales present throughout nature ldquoarise
Time
F i g h
t 1
F i g h
t 2
Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once
Vd
Pa
Je
Cb
Yv
Ta
Sg
Qs
Po
Mv
Ip
Fp
Eo
Dh
Tr
Pr
Fc
Zu
Vf
Fp
Ec
Zu
Zr
Iw
Is
Fp
Fc
Cc
Yv
Cd
Ta
Cc
Qv
Ip
Cb
Pa
Eb
Sb
Jc
Je
Hp
Lr
Jc
Zu
Po
Dh
Vf
Eo
Eo
Cd
Pa
Dh
Hh
At
Zu
Yv
Th
Ta
Fn
through a process o collective computa-tionrdquo creating slow-changing predictablesocial and biological structures Flack says
Circuits are more than just an analo-gy though ndash theyrsquore the tools that bridgethe gap between individual and collective
behavior And in a scientific field whereitrsquos easy to avoid real data circuits are one
way Flack and Krakauerrsquos research group
makes sure their theories are a good matchto the real world ldquoOur group is committedto an empirical approachrdquo Flack says ldquoWebelieve that only when these measures aredeveloped with an understanding o thedata generated by real systems will they
be useulrdquoTe process o building circuits begins
by analyzing how a systemrsquos individual parts work together Using the macaquefight data Flack Krakauer and ormerSFI Omidyar Fellow Simon DeDeo (nowat Indiana University Bloomington) devel-oped a statistical method they dubbedinductive game theory to analyze howthe monkeys reacted to othersrsquo fights Teresulting social circuit Flack says servesas a detailed model ldquoor how the micro-
scopic behavior maps to the unctionallyimportant macroscopic eatures o socialstructurerdquo such as the distribution o fightsizes In other words to construct a socialor biological circuit is to understand how agroup builds and maintains stable predict-able inormation hierarchies
Te final step is to produce a simplifiedsocial circuit what the researchers call a
ldquocognitive effective theoryrdquo that accurate-ly predicts how groups behave Te aimis to extract the key sorts o interactions
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 11
responsible or power structures fight-sizedistributions or other macroscopic eaturesusing ldquowhat we know about individual orcomponent cognition to coarse grain orcompressrdquo social circuits Flack says Suchcompression is essential she says because
living things canrsquot base their decisions on what ever y other living thing is doing instead theyrsquore orced to pay attention to
just a ew patterns or details o whatrsquos goingon around them
Te key question here as collaboratorand Princeton University graduate studentEleanor Brush puts it is how little inor-mation individuals need to successullyoutsmart others
Accidental or inevitable
Answering questions like that one ndash or testingsome o the teamrsquos more abstract predic-tions ndash remains a central challenge Poonor example describes his studies o electioncycles and policy change as ldquotoy modelsrdquo ndashthey capture qualitative eatures o the datasuch as party switching but donrsquot stand upto more precise quantitative tests
Meanwhile Krakauer and others sayitrsquos not always clear how to test particularhypotheses such as the prediction that basismismatch leads to ever-increasing complexity
in living things ldquoWersquore still looking or somecompelling examplerdquo Ellison says ldquoPart othe issue is that one can ofen play the devilrsquosadvocate and call into question the examplerdquoone reason why his work to construct ormal
precise measures o complexity is so import-ant he says
New techniques or rapidly analyzinggenetic data Krakauer says might improvethe situation Combining those techniques
with laboratory-based ldquoexperimental evolutionrdquo
in which researchers study the effects o preciseenvironmental changes on small organismssuch as bacteria could help test some o theendeavorrsquos core ideas such as the reflection
principle or the role o phase transitionsAnother potential avenue is to use ldquodigital
sources like computer games where we cancontrol to a large extent the orm o the dataor the conditions under which they werecollectedrdquo Krakauer says
esting their theories is just one part o theteamrsquos ambitious aims Tey hope Flack says
People gather in Washington DC before Martin Luther King Jrrsquos ldquoI Have a Dreamrdquo speech on Aug 28 1963 to demand equalities
Individuals tend to move faster and be less predictable than the slow-moving institutions they create
to achieve nothing less than an understandingo why lie is organized the way it is rom thesmallest bacteria to the largest human insti-tutions Tat requires combining real-worldobservation and abstract mathematical theoryin novel and creative ways
And as i that wasnrsquot enough Krakauerhas one more question in mind is lie anaccident or is it inevitable And i lie isinevitable well are we alone
ldquoI itrsquos not a product o a series o random
accidents but therersquos an underlying law-likeregularity that would give us confidence inbelieving in the possibility o lie presenteverywhere in the universerdquo he says ldquoSo
when one asks why does it matter whetheritrsquos chance or necessity it matters i we care
whether wersquore alone are notrdquo
Nathan Collins is a feelance science writernew ather and film aficionado based inSan Francisco
ldquoLiving things canrsquot base their decisions on what
every other living thing is doing instead theyrsquore
forced to pay attention to just a few patternsrdquo
8122019 How Life Got Complex
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12 Santa Fe Institute Bulletin Vol 28
ldquohellipin mere Time all things follow
one another and in mere Space
all things are side by side it is
accordingly only by the combi-
nation of Time and Space thatthe representation of coexistence
arisesrdquo
mdash ARTHUR SCHOPENHAUER On the Fourfold Root
of the Principle of Sufficient Reason 1813
Yves Tanguy Indefinite Divisibility 1942
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 13
D I A C O M M O N S I N S E T H E N R I R O U S S E A U T H E
D R E A M 1 9 1 0 W I K I M E D I A C O M M O N S
Biological systems ndash rom cells totissues to individuals to societ-ies ndash are hierarchically organized
(eg Feldman amp Eschel 1982 Buss 1987Smith amp Szathmaacutery 1998 Valentineamp May 1996 Michod 2000 Frank2003) o many hierarchical organiza-tion suggests the nesting o components orindividuals into groups with these groupsaggregating into yet larger groups But this view ndash at least superficially ndash privilegesspace and matter over time and inorma-tion Many types o neural coding orexample require averaging or summing over neural firing ratesTe neuronsrsquo spatial location ndash that they are in proximity ndash iso course important but at least as important to the encoding
The explanation for the complex multi-scale structure of biological and social systems
lies in their manipulation of space and time to reduce uncertainty about the future
is their behavior in time Likewise insome monkey societies as I will discussin detail later in this review individualsestimate the uture cost o social inter-action by encoding the average outcomeo past interactions in special signals and
then summing over these signalsIn both examples inormation rom
events distributed in time as well as space(Figure 1) is captured with encodingsthat are used to control some behavioraloutput My collaborators and I in theCenter or Complexity amp Collective
Computation are exploring the idea that hierarchical organiza-tion at its core is a nesting o these kinds o unctional encodingsAs I will explain we think these unctional encodings result
LIFErsquoS INFORMATION HIERARCHY
BY JESSICA C FLACK
CO-DIRECTOR CENTER FOR COMPLEXITY amp COLLECTIVE COMPUTATION
WISCONSIN INSTITUTE FOR DISCOVERY UNIVERSITY OF WISCONSIN-MADISON
EXTERNAL PROFESSOR SANTA FE INSTITUTE
Jessica Flack
Number of spatial dimensions
N u m b e
r o f t i m e d i m e n s i o n s
0
0
1
2
3
4
5
1 2 3 4 5
UNSTABLE
TOO SIMPLE
UNPREDICTABLE (ELLIPTIC)
UNPREDICTABLE(ULTRAHYPERBOLIC)
TACHYONSONLY
UNSTABLE
Figure 1 The dimensionality of the time-space
continuum with properties postulated when x
does not equal 3 and y is larger than 1 Life on
earth exist s in three spatial dimensions and one
temporal dimension Biological systems effec-
tively ldquodiscretizerdquo time and space to reduce
environmental uncertainty by coarse-graining
and compressing environmental time series to
find regularities Components use the coarse-
grained descriptions to predict the future
tuning their behavior to their predictions
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rom biological systemsmanipulating space andtime (Figure 2) to acilitateinormation extraction which in turn acilitatesmore efficient extraction
o energy h i s inormation
hierarchy appears to bea universal property obiological systems andmay be the key to one oliersquos greatest mysteries ndashthe origins o biologicalcomplexity In this essayI review a body o work byDavid Krakauer myseland our research group
that has been inspiredby many years o work atthe Santa Fe Institute (eg Crutchfield 1994 Gell-Mann1996 Gell-Mann amp Lloyd 1996 Fontana amp Buss 1996 West Brown amp Enquist 1997 Fontana amp Schuster 1998Ancel amp Fontana 2000 Stadler Stadler Wagner amp Fontana2001 Smith 2003 Crutchfield amp Goumlrnerup 2006 Smith2008) Our work suggests that complexity and the multi-scalestructure o biological systems are the predictable outcomeo evolutionary dynamics driven by uncertainty minimiza-tion (Krakauer 2011 Flack 2012 Flack Erwin Elliot ampKrakauer 2013)
Tis recasting o the evolutionary process as an inerentialone1 (Bergstrom amp Rosvall 2009 Krakauer 2011) is based onthe premise that organisms and other biological systems canbe viewed as hypotheses about the present and uture environ-ments they or their offspring will encounter induced rom thehistory o past environmental states they or their ancestors have
experienced (eg Crutchfield amp Feldman 2001 Krakauer ampZannotto 2009 Ellison Flack amp Krakauer in prep) Tis premise o course only holds i the past is prologue ndash that ishas regularities and the regularities can be estimated and evenmanipulated (as in niche construction) by biological systems ortheir components to produce adaptive behavior (Flack ErwinElliot amp Krakauer 2013 Ellison Flack amp Krakauer in prep)
I these premises are correct lie at its core is computa-tional and a central question becomes How do systems andtheir components estimate and control the regularity in theirenvironments and use these estimates to tune their strategiesI suggest that the answer to this question and the explanation
or complexity is that biological systems manipulate spatialand temporal structure to produce order ndash low variance ndash atlocal scales
UNCERTAINTY REDUCTION
Te story I want to tell starts with the observation that witheach new level o organization typically comes new unction-ality ndash a new eature with positive payoff consequences or thesystem as a whole or or its components (Flack Erwin Elliot
1 Tis idea is related to work on Maxwel lrsquos Demon (eg Krakauer2011 Mandal 983121uan amp Jarzynski 2013) and the Carnot cycle ( egSmith 2003) but we do not yet understand the mapping
Figure 2 Biological systems ndash from
(left to right ) Volvox colonies to slime
molds to animal societies to large-scale
ecosystems such as reefs t o human
cities ndash are hierarchically organized with
multiple functionally important time and
space scales All feature 1) components
with only partially aligned interests
exhibiting coherent behavior at the
aggregate level 2) components that turn
over and that co-exist in the sys tem at
varying stages of development
3) social structure that persists but
component behavior that fluctuates and
4) macroscopic variation in temporal
and spatial structure and coupling with
microscopic behavior which has func-
tional impl ications when the components
can perceive ndash in evolutionary develop-
mental or ecological time ndash regularities
at the macroscopic scale
1 2
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April 2014 Santa Fe Institute Bulletin 15
amp Krakauer 2013) Policing in a pigtailed macaque group is anexample Once a heavy tailed distribution o power ndash defined asthe degree o consensus in the group that an individual can winfights (see Flack amp Krakauer 2006 Boehm amp Flack 2010 BrushKrakauer amp Flack2013) ndash becomes effec-
tively institutionalized(here meaning hard tochange) policing (anintrinsically costlystrategy) becomesaffordable at least tothose animals that sitin the tail o the powerdistribution thosesuper powerul monkeys who are rarely or never challenged when they break up fights (Flack de Waal amp Krakauer 2005Flack Girvan de Waal amp Krakauer 2006)
My collaborators and I propose that a primary driver o theemergence o new unctionality such as policing is the reduc-tion o environmental uncertainty through the constructiono nested dynamical processes with a range o characteris-tic time constants (Flack Erwin Elliot amp Krakauer 2013)Tese nested dynamical processes arise as components extractregularities rom ast microscopic behavior by coarse-grain-ing (or compressing) the history o events to which they havebeen exposed
Proteins or example can have a long hal-lie relative toRNA transcripts and can be thought o as the summed outputo translation Cells have a long hal-lie relative to proteins
and are a unction o the summed output o arrays o spatiallystructured proteins Both proteins and cells represent someaverage measure o the noisier activity o their constituentsSimilarly a pigtailed macaquersquos estimate o its power is a kind
o average measure othe collective percep-
tion in the groupthat the macaque iscapable o winningights and this is abetter predictor othe cost the macaque will pay during fightsthan the outcome oany single melee as
these outcomes can fluctuate or contextual reasons Tesecoarse-grainings or averages are encoded as slow variables(Flack amp de Waal 2007 Flack 2012 Flack Erwin Elliot
amp Krakauer 2013 see also Feret Danos Krivine Harner ampFontana 2009 or a similar idea) Slow variables may have aspatial component as well as a temporal component as in the protein and cell examples (Figure 6) or minimally only atemporal component as in the monkey example
As a consequence o integrating over abundant microscopic processes slow variables provide better predictors o the localuture configuration o a system than the states o the fluctuatingmicroscopic components In doing so they promote acceleratedrates o microscopic adaptation Slow variables acilitate adapta-tion in two ways Tey allow components to fine-tune theirbehavior and they ree components to search at low cost a larger
3 4
Slow variables provide better predictors
of the local future configuration of a
system than the states of the fluctuating
microscopic componentsrdquo
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Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and
proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since
fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous
starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of
relevant genes and proteins and the output is the target phenot ypic feature
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April 2014 Santa Fe Institute Bulletin 17
space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
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April 2014 Santa Fe Institute Bulletin 19
A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
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vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
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22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
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24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 25
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operating budget comes from
private sources - forward-thinking
individuals corporations and
foundations If you believe
complex systems science can
generate the insights that matter
most for science and society makea gift today Visit our website at
wwwsantafeedusupport and
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ITrsquoS SIMPLE
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ComplexityScience
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 1
ldquo Inorganic chemistry is essentiallysilent on the topic of biology We donot exist The theory of everything
is a theory of everything exceptof those things that theorize
rdquo
David Krakauer
David Krakauer
Afer almost three millennia our concerns are essentially the sameas those o this celebrated Greek armer and poet In just less than 14billion years the universe has generated rom nothing more than100 billion galaxies each o which contains on average 100 billionstars and around many o these stars a system o planets In our ownMilky Way tucked away in a local bubble o the Orion-Cygnus armo the galaxy 27000 light years rom the galactic center spins oursolar system home to eight planets ndash our small and dense and ourlarge and gaseous On one o these planets the third nearest the sun
we find lie o the best o our knowledge it is the only planet inour solar system supporting adaptive matter
From physical law we can derive essential properties o the sunthe elements and the planets Te incredible machinery o thetheories o gravity quantum mechanics and the standard model
give us significant insights into the observable structure in theuniverse Optimistically we can even deduce simple moleculesrom inorganic chemistry And then the theory machine stopsPhysics runs out o gas Chemistry dries up From the perspectiveo physics our own solar system or galaxy are not in any waydifferent rom those anywhere else in the universe Inorganicchemistry is essentially silent on the topic o biology We do notexist Te theory o everything is a theory o everything except othose things that theorize
8122019 How Life Got Complex
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2 Santa Fe Institute Bulletin Vol 28
Te projects described in this issue o the Bulletin are allefforts to grapple with some o the key ideas and conceptsrequired to understand living systems with an emphasis onevolution both biological and cultural We ask how overlong stretches o time successively more effective mechanismsor storing and processing inormation have been adaptivelyengineered and how these biological computing systems areused to predict model and control relevant states o noisy andliving environments We consider complexity through the lenso inormation processing we seek to quantiy how inormationis encoded in living systems (genomes and brains) and wesuggest estimates or upper bounds in adaptive inormation
capacity Some o the concepts relevant to understandingbiological complexity include hierarchy individuality criti-cality inormationuncertainty computation and sociality
Stanislaw Lem in his science fiction che-drsquoœuvre Solaris (1961) considers a planet swaddled by an inscrutable oceancapable o astonishing acts o reasoning So vastly more intel-ligent and powerul is the Ocean to the human explorers andscientists (Solarists) who dedicate their lives to its analysisand explication that humanity is orced to resign itsel toignorance over its ultimate mechanisms and motives I haveofen wondered whether Solaris is not a metaphor or lie onearth where the methods o the Solarists are the traditional
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 3
Cronus mutilates his father Uranus at the behest
of his mother Gaia in this scene from Greek
mythology featured in Hesiodrsquos Theogonymethods o science Solaris is a huge interconnected system oenergy and inormation flows that dey traditional methodso reduction Perhaps Solaris is waiting on complexity scienceinormation theory scaling network theory evolutionarydynamics and computation Perhaps we only stand a chanceo understanding complex lie when we approach it throughthe sciences o complexity ndash in which case consider thisissue o the Bulletin a temporary visa granting access to allo those restless explorers intent on the understanding o ourterrestrial Solaris
8122019 How Life Got Complex
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4 Santa Fe Institute Bulletin Vol 28
R ESEARCH on the origins development and dynam-ics o complexity in biological systems has been a coretopic o inquiry at the Santa Fe Institute since its
ounding 30 years ago in 1984 SFIrsquos scientists have workedto develop an understanding o a dizzying array o biological
phenomena rom the origins o lie to transitions rom single-to multi-cellularity to evolutionary innovation at different
levels o biological organization to the relationship betweenecological complexity and dynamical stabilityTe intertwined concepts o energy and inormation are
undamental to any understanding o biological complexityBiological systems are ar rom equilibrium requiring a constantflow o energy to maintain their organization and unction-ality Te processing and encoding o inormation providesa means or lie to manage and maintain energy acquisitionuse and dissipation
Te work o ormer resident proessors and current externalaculty members Jessica Flack and David Krakauer (and their manycolleagues) highlighted in this issue o the SFI Bulletin addressesthe intimate dance between energy and inormation processing inbiological systems ndash a dance they suggest gives rise to the complexmulti-scale structure we observe
Tis computational-thermodynamic view o biology provides
a powerul potentially generic ramework or understanding the properties o any kind o complex adaptive system including socio-economic systems
Sincerely
Jennier Dunne Chair o the Faculty Santa Fe Institute
Biological Systems Energy and Information
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April 2014 Santa Fe Institute Bulletin 5
IMAGE CREDITS THIS PAGE FROM TOP BRAIN DRAWING HESSDESIGNWORKSCOM EARTH FROM SPACE NASA ROUSSEAU THE DREAM WIKIMEDIA COMMONSFACING PAGE PORTRAIT OF JENNIFER DUNNE MINESH BACRANIA PHOTOGRAPHY
SFI BULLETIN
VOL 28 APRIL 2014Contents
0 Perspectives The Complexity of Life
By DAVID KRAKAUER
4 Biological Systems Energy and Information
By JENNIFER DUNNE
6 Why Nature Went to the Trouble of CreatingYouBy NATHAN COLLINS
12 Lifersquos Information Hierarchy
By JESSICA FLACK
ldquo There is nothing that living things do that cannot be
understood from the point of view that they are made
of atoms acting according to the laws of physicsrdquo ndash R I C H A R D F E Y N M A N
12
6
0
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8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 7April 2014 Santa Fe Institute Bulletin 7
Y pestis the bacterium that causes bubonic
plague (aka The Black Death of the Middle Ages)
Lie on earth began some 35 billion
years ago not all that long afer the planet itsel first ormed and or 15
billion years it chugged along single-celledcreatures sel-replicating dividing diversi-ying Remarkably it took that long ndash 7500times longer than all o human history ndash orthe first multicellular lie to emerge and stilllonger or it to evolve into lie as we know it
Despite that the question manyresearchers ask isnrsquot what took so long
but rather why complex lie would haveevolved in the first place Consider thissingle-celled organisms make up more thanhal o the biomass on earth and even oneo the tiniest organisms ndash Y pestis betterknown as bubonic plague ndash can effortlesslythoughtlessly kill you
Nature it seems doesnrsquot need youIndeed there isnrsquot any obvious reasonit would go to the trouble o creatingsomething as complex as a human beingcomplete with its differentiated organs and
top-down control systems And yet despiteour billion years o Naturersquos great ldquomehrdquohere you are alive multicellular complex
ndash even intelligent enough to ponder yourown existence
What was Nature thinking Accordingto one argument bacteria first boundtogether in colonies that enhanced cooper-ation and hence survival Eventually thosebacteria bound together physically as wellcreating the first multicellular lie Andso on
ldquoYou could say thatrsquos an answer but then you could go a bit urther and ask what isit exactly that makes you a better compet-
itorrdquo says David Krakauer who with SFIExternal Proessor Jessica Flack co-directs
the Wisconsin Institute or DiscoveryrsquosCenter or Complexity and CollectiveComputation or C4 and SFIrsquos Johnempleton Foundation-unded ldquoEvolutiono Complexity and Intelligence on Earthrdquoresearch project
ldquoWell yoursquore outsmarting everyone elserdquoKrakauer says
Simple versus complex
Despite its seeming indifference Naturedoes seem to have thought highly enough
Why Nature Went to the Trouble
of CreatinghellipYou BY NATHAN COLLINS
In a complex world where plants and animals and everything else are duking
it out to survive an organism stands to gain from becoming more complex
o complex structures to produce a ew othem and to have ratcheted up the complex-ity urther by embedding complex structures
within complex structures ndash animals withhearts and lungs and circulatory systems orgroups o people capable o building theirown social institutions
But why What purpose does it serveldquoWhy lie is hierarchically organized isnot at all obviousrdquo Flack says and howan organismrsquos or a societyrsquos complexity
relates to the complexity o its environ-ment remains unclear
Our anthropomorphized Nature mighthave started with one very simple idea
what Krakauer calls the reflection princi- ple which presupposes that living thingscanrsquot be more complex than their environ-ments an idea rooted in experiments ldquoI
you take organisms and you place themin simpler environments they just throweverything [superfluous] away Tey losegenesrdquo Krakauer says
At the same time the world does seem toavor an intelligent creature Even the tiniestliving things need to be able to comprehend predict and react to their environmentsthatrsquos what allows them to outsmart eachother he says In a complex world where
plants and animals and everything else areduking it out to survive an organism standsto gain rom becoming more complex
Tat tension between simplicity andcomplexity is the starting point or C4
postdoctoral ellow Christopher Ellison
8122019 How Life Got Complex
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8 Santa Fe Institute Bulletin Vol 28
Watch David Krakauerrsquos interview at
wwwsantafeedulife
What does Nature care about hearts brains and other organs or for that
matter political parties ndash in other words structures within structures
ldquoWersquod like to understand the implica-tions this has or the environmentrdquo hesays ldquoFor example do simple or complexorganisms experience and live in simple orcomplex environmentsrdquo
Working with Flack and KrakauerEllison developed ldquoMarkov organismsrdquo
computer-simulated creatures that merge
insights rom biology with inormation processing techniques rom computerscience to help figure out how lie balancesthese trade-offs Rather than modeling real
organisms themselves he ocuses on howinormation flows in the ecological system
Itrsquos early days Ellison says but his simula-tions suggest that lie will ofen evolve tomatch its environmentrsquos complexity ndash findingsthat are in line with the reflection principlebut with some interesting caveats For one
thing evolving Markov organisms tend toovershoot their worldsrsquocomplexity and mighttake a long time to pruneunnecessary complexities
Teyrsquore also suscepti-ble to ldquobasis mismatchrdquo a
problem you know welli yoursquove ever tried to explain to a tourist howto get around in your hometown o youthere are just a ew steps but to the noviceitrsquos a complex process with many twists and
turns and every intersection represents a possible misdirection in sprawling cities likeLos Angeles a direction as simple as ldquotakeSunset to Vermont and turn lefrdquo becomesinfinitely complex Markov organisms arethe same i their way o solving a problem
doesnrsquot line up with how their environmentsconstructed it Ellisonrsquos simulations showMarkov organismsrsquo complexity keeps evolv-ing upward orever
But Ellisonrsquos inormation-centricapproach has some benefits One he says
ldquois that it attempts to answer the questiono how complexity evolves in an organ-ism-independent ashionrdquo meaning thatthe ideas apply equally well to anythingrom bacteria to politics Similarly Ellisonrsquosmethod allows the team to describe both anorganism and its environmentrsquos complexity
in the same terms because both derive romthe same underlying models o inormation
processing Surprisingly thatrsquos somethingew i any other researchers have done
Constructing predictability
So it appears Nature might avor multicel-lular lie i it affords a certain computational power not readily available to single-celledorganisms But what does Nature care abouthearts brains and other organs or or thatmatter political parties ndash in other words
structures within structureshe answer Flack says is that living
things like their worlds to be predictableand what makes cells and people more likelyto survive Nature avors
Much o the structure we observe in the world Flack says probably evolved becausestructure begets stability hence predictabilityGroups o genes cells or animals changetheir collective behaviors slowly compared
with individual genes cells or animals givingthe aster-moving individual components a
chance to anticipate changes more easilyBiologists call the idea that plants andanimals ndash and genes and organs and so on
ndash structure their environments to be morestable and predictable ldquoniche constructionrdquoand it usually applies to physical structurelike ants building nests But it also can beapplied to temporal structure
Politics offers perhaps a simple exampleEarly US politicians were explicit aboutdesigning Congress and the rest o govern-ment so that it would change gradually and
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 9April 2014 Santa Fe Institute Bulletin 9
According to the reflection principle organisms are reflections of their environments Here the environment is represented as a prose excerpt from
Darwinrsquos Origin of Species wherein he contemplates an entangled bank filled with ldquoendless forms most beautiful and most wonderfulrdquo each evolved through natural selection Organism A matches the environmentrsquos complexity while Organism B is less complex than the environment and Organism C is
more complex than the environment The research team is asking when evolution satisfies or v iolates the reflection principle
be more stable Even when we complainabout our slow-moving government weare undoubtedly comorted by that verycharacteristic because slow is predictableAnd when our environment is predictable we know how to make sound decisions
So people construct relatively slow-mov-ing predictable institutions At the sametime those institutions help shape thebehavior o the individuals who createdthem points out C4 postdoctoral ellowPhilip Poon who is examining eedbackrom institutions to explain (in the govern-ment case) why or example Democrats andRepublicans seem to trade off controllingthe White House and Congress every ewterms A critical issue is o course howthat eedback works rom a mechanical
perspective ndash that is how the ways individ-uals perceive and understand institutionsinfluence their decision-making
Drawing on the theory o phase transi-tions the same one that explains whychanging a seemingly minor variable cansuddenly shif an entire system rom onestate to another Flack Krakauer and C4researcher and ormer SFI PostdoctoralFellow Bryan Daniels argue that hierarchi-cal structures bestow another advantageeicient inormation low rom the
collective to its individual parts Systems perched on the edge o a phase transitionare exquisitely sensitive so that a small orlocalized change ldquoleads to a large change inthe global dynamicsrdquo Daniels says Toughthat might seem unstable or chaotic systems
near the critical point where a phase transi-
tion begins are actually quite predictableGroups o macaque monkeys ndash one o
Flack and the teamrsquos earliest sources o data
and inspiration ndash are one system that appearsto be resting near a phase transition Whenmonkeys arenrsquot eeling especially aggres-sive Daniels says things are stable and ldquoi Isuddenly act out nothingrsquos going to happenhellipbut i [the group is] sitting at the critical
point then [my] contribution is always moreimportantrdquo and one extra monkey picking afight is enough to kick off a large-scale brawl
Below the critical point individualmonkeys act airly independently but rightat the transition their behavior is tightly
ldquoWhen monkeys arenrsquot feeling especially aggressive
things are stable but if the group is sitting at the
critical point one extra monkey picking a fight
is enough to kick off a large-scale brawlrdquo
coordinated and individual monkeys acttogether as one Tat Krakauer empha-sizes eases the flow o inormation romthe system as a whole to its constituent
parts making it all the more predictableNature is rie with examples one birdrsquos
sudden course correction changing the
direction o an entire flock alternatingDemocrat and Republican control oCongress and so on
Social circuitry
In the abstract Nature has good reasonto avor complexity and hierarchy ndash eachin its own way makes the tasks o compre-hension prediction and strategizingeasier But its a third concept circuitsthat grounds Flack and Krakauerrsquos teamand lays the practical oundation ormuch o their work
Usually the word ldquocircuitrdquo conjuresimages o the transistors microprocessors
8122019 How Life Got Complex
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10 Santa Fe Institute Bulletin Vol 28
Time series representation of the dis tribution of fight sizes in a macaque society Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once An outburst by an individual wonrsquot normally tip the scales But when the system is perched at
a phase transition a brawl among many individuals can erupt
and lengths o copper wire that make upa computer Te analogy is apt Like anelectronic circuit individual components
ndash genes organs people ndash inormational-ly bind living things to orm a kind obiological or social computer In act the
circuit approach to describing a systemstems rom a hunch that the hierarchicalscales present throughout nature ldquoarise
Time
F i g h
t 1
F i g h
t 2
Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once
Vd
Pa
Je
Cb
Yv
Ta
Sg
Qs
Po
Mv
Ip
Fp
Eo
Dh
Tr
Pr
Fc
Zu
Vf
Fp
Ec
Zu
Zr
Iw
Is
Fp
Fc
Cc
Yv
Cd
Ta
Cc
Qv
Ip
Cb
Pa
Eb
Sb
Jc
Je
Hp
Lr
Jc
Zu
Po
Dh
Vf
Eo
Eo
Cd
Pa
Dh
Hh
At
Zu
Yv
Th
Ta
Fn
through a process o collective computa-tionrdquo creating slow-changing predictablesocial and biological structures Flack says
Circuits are more than just an analo-gy though ndash theyrsquore the tools that bridgethe gap between individual and collective
behavior And in a scientific field whereitrsquos easy to avoid real data circuits are one
way Flack and Krakauerrsquos research group
makes sure their theories are a good matchto the real world ldquoOur group is committedto an empirical approachrdquo Flack says ldquoWebelieve that only when these measures aredeveloped with an understanding o thedata generated by real systems will they
be useulrdquoTe process o building circuits begins
by analyzing how a systemrsquos individual parts work together Using the macaquefight data Flack Krakauer and ormerSFI Omidyar Fellow Simon DeDeo (nowat Indiana University Bloomington) devel-oped a statistical method they dubbedinductive game theory to analyze howthe monkeys reacted to othersrsquo fights Teresulting social circuit Flack says servesas a detailed model ldquoor how the micro-
scopic behavior maps to the unctionallyimportant macroscopic eatures o socialstructurerdquo such as the distribution o fightsizes In other words to construct a socialor biological circuit is to understand how agroup builds and maintains stable predict-able inormation hierarchies
Te final step is to produce a simplifiedsocial circuit what the researchers call a
ldquocognitive effective theoryrdquo that accurate-ly predicts how groups behave Te aimis to extract the key sorts o interactions
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 11
responsible or power structures fight-sizedistributions or other macroscopic eaturesusing ldquowhat we know about individual orcomponent cognition to coarse grain orcompressrdquo social circuits Flack says Suchcompression is essential she says because
living things canrsquot base their decisions on what ever y other living thing is doing instead theyrsquore orced to pay attention to
just a ew patterns or details o whatrsquos goingon around them
Te key question here as collaboratorand Princeton University graduate studentEleanor Brush puts it is how little inor-mation individuals need to successullyoutsmart others
Accidental or inevitable
Answering questions like that one ndash or testingsome o the teamrsquos more abstract predic-tions ndash remains a central challenge Poonor example describes his studies o electioncycles and policy change as ldquotoy modelsrdquo ndashthey capture qualitative eatures o the datasuch as party switching but donrsquot stand upto more precise quantitative tests
Meanwhile Krakauer and others sayitrsquos not always clear how to test particularhypotheses such as the prediction that basismismatch leads to ever-increasing complexity
in living things ldquoWersquore still looking or somecompelling examplerdquo Ellison says ldquoPart othe issue is that one can ofen play the devilrsquosadvocate and call into question the examplerdquoone reason why his work to construct ormal
precise measures o complexity is so import-ant he says
New techniques or rapidly analyzinggenetic data Krakauer says might improvethe situation Combining those techniques
with laboratory-based ldquoexperimental evolutionrdquo
in which researchers study the effects o preciseenvironmental changes on small organismssuch as bacteria could help test some o theendeavorrsquos core ideas such as the reflection
principle or the role o phase transitionsAnother potential avenue is to use ldquodigital
sources like computer games where we cancontrol to a large extent the orm o the dataor the conditions under which they werecollectedrdquo Krakauer says
esting their theories is just one part o theteamrsquos ambitious aims Tey hope Flack says
People gather in Washington DC before Martin Luther King Jrrsquos ldquoI Have a Dreamrdquo speech on Aug 28 1963 to demand equalities
Individuals tend to move faster and be less predictable than the slow-moving institutions they create
to achieve nothing less than an understandingo why lie is organized the way it is rom thesmallest bacteria to the largest human insti-tutions Tat requires combining real-worldobservation and abstract mathematical theoryin novel and creative ways
And as i that wasnrsquot enough Krakauerhas one more question in mind is lie anaccident or is it inevitable And i lie isinevitable well are we alone
ldquoI itrsquos not a product o a series o random
accidents but therersquos an underlying law-likeregularity that would give us confidence inbelieving in the possibility o lie presenteverywhere in the universerdquo he says ldquoSo
when one asks why does it matter whetheritrsquos chance or necessity it matters i we care
whether wersquore alone are notrdquo
Nathan Collins is a feelance science writernew ather and film aficionado based inSan Francisco
ldquoLiving things canrsquot base their decisions on what
every other living thing is doing instead theyrsquore
forced to pay attention to just a few patternsrdquo
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12 Santa Fe Institute Bulletin Vol 28
ldquohellipin mere Time all things follow
one another and in mere Space
all things are side by side it is
accordingly only by the combi-
nation of Time and Space thatthe representation of coexistence
arisesrdquo
mdash ARTHUR SCHOPENHAUER On the Fourfold Root
of the Principle of Sufficient Reason 1813
Yves Tanguy Indefinite Divisibility 1942
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 13
D I A C O M M O N S I N S E T H E N R I R O U S S E A U T H E
D R E A M 1 9 1 0 W I K I M E D I A C O M M O N S
Biological systems ndash rom cells totissues to individuals to societ-ies ndash are hierarchically organized
(eg Feldman amp Eschel 1982 Buss 1987Smith amp Szathmaacutery 1998 Valentineamp May 1996 Michod 2000 Frank2003) o many hierarchical organiza-tion suggests the nesting o components orindividuals into groups with these groupsaggregating into yet larger groups But this view ndash at least superficially ndash privilegesspace and matter over time and inorma-tion Many types o neural coding orexample require averaging or summing over neural firing ratesTe neuronsrsquo spatial location ndash that they are in proximity ndash iso course important but at least as important to the encoding
The explanation for the complex multi-scale structure of biological and social systems
lies in their manipulation of space and time to reduce uncertainty about the future
is their behavior in time Likewise insome monkey societies as I will discussin detail later in this review individualsestimate the uture cost o social inter-action by encoding the average outcomeo past interactions in special signals and
then summing over these signalsIn both examples inormation rom
events distributed in time as well as space(Figure 1) is captured with encodingsthat are used to control some behavioraloutput My collaborators and I in theCenter or Complexity amp Collective
Computation are exploring the idea that hierarchical organiza-tion at its core is a nesting o these kinds o unctional encodingsAs I will explain we think these unctional encodings result
LIFErsquoS INFORMATION HIERARCHY
BY JESSICA C FLACK
CO-DIRECTOR CENTER FOR COMPLEXITY amp COLLECTIVE COMPUTATION
WISCONSIN INSTITUTE FOR DISCOVERY UNIVERSITY OF WISCONSIN-MADISON
EXTERNAL PROFESSOR SANTA FE INSTITUTE
Jessica Flack
Number of spatial dimensions
N u m b e
r o f t i m e d i m e n s i o n s
0
0
1
2
3
4
5
1 2 3 4 5
UNSTABLE
TOO SIMPLE
UNPREDICTABLE (ELLIPTIC)
UNPREDICTABLE(ULTRAHYPERBOLIC)
TACHYONSONLY
UNSTABLE
Figure 1 The dimensionality of the time-space
continuum with properties postulated when x
does not equal 3 and y is larger than 1 Life on
earth exist s in three spatial dimensions and one
temporal dimension Biological systems effec-
tively ldquodiscretizerdquo time and space to reduce
environmental uncertainty by coarse-graining
and compressing environmental time series to
find regularities Components use the coarse-
grained descriptions to predict the future
tuning their behavior to their predictions
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rom biological systemsmanipulating space andtime (Figure 2) to acilitateinormation extraction which in turn acilitatesmore efficient extraction
o energy h i s inormation
hierarchy appears to bea universal property obiological systems andmay be the key to one oliersquos greatest mysteries ndashthe origins o biologicalcomplexity In this essayI review a body o work byDavid Krakauer myseland our research group
that has been inspiredby many years o work atthe Santa Fe Institute (eg Crutchfield 1994 Gell-Mann1996 Gell-Mann amp Lloyd 1996 Fontana amp Buss 1996 West Brown amp Enquist 1997 Fontana amp Schuster 1998Ancel amp Fontana 2000 Stadler Stadler Wagner amp Fontana2001 Smith 2003 Crutchfield amp Goumlrnerup 2006 Smith2008) Our work suggests that complexity and the multi-scalestructure o biological systems are the predictable outcomeo evolutionary dynamics driven by uncertainty minimiza-tion (Krakauer 2011 Flack 2012 Flack Erwin Elliot ampKrakauer 2013)
Tis recasting o the evolutionary process as an inerentialone1 (Bergstrom amp Rosvall 2009 Krakauer 2011) is based onthe premise that organisms and other biological systems canbe viewed as hypotheses about the present and uture environ-ments they or their offspring will encounter induced rom thehistory o past environmental states they or their ancestors have
experienced (eg Crutchfield amp Feldman 2001 Krakauer ampZannotto 2009 Ellison Flack amp Krakauer in prep) Tis premise o course only holds i the past is prologue ndash that ishas regularities and the regularities can be estimated and evenmanipulated (as in niche construction) by biological systems ortheir components to produce adaptive behavior (Flack ErwinElliot amp Krakauer 2013 Ellison Flack amp Krakauer in prep)
I these premises are correct lie at its core is computa-tional and a central question becomes How do systems andtheir components estimate and control the regularity in theirenvironments and use these estimates to tune their strategiesI suggest that the answer to this question and the explanation
or complexity is that biological systems manipulate spatialand temporal structure to produce order ndash low variance ndash atlocal scales
UNCERTAINTY REDUCTION
Te story I want to tell starts with the observation that witheach new level o organization typically comes new unction-ality ndash a new eature with positive payoff consequences or thesystem as a whole or or its components (Flack Erwin Elliot
1 Tis idea is related to work on Maxwel lrsquos Demon (eg Krakauer2011 Mandal 983121uan amp Jarzynski 2013) and the Carnot cycle ( egSmith 2003) but we do not yet understand the mapping
Figure 2 Biological systems ndash from
(left to right ) Volvox colonies to slime
molds to animal societies to large-scale
ecosystems such as reefs t o human
cities ndash are hierarchically organized with
multiple functionally important time and
space scales All feature 1) components
with only partially aligned interests
exhibiting coherent behavior at the
aggregate level 2) components that turn
over and that co-exist in the sys tem at
varying stages of development
3) social structure that persists but
component behavior that fluctuates and
4) macroscopic variation in temporal
and spatial structure and coupling with
microscopic behavior which has func-
tional impl ications when the components
can perceive ndash in evolutionary develop-
mental or ecological time ndash regularities
at the macroscopic scale
1 2
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April 2014 Santa Fe Institute Bulletin 15
amp Krakauer 2013) Policing in a pigtailed macaque group is anexample Once a heavy tailed distribution o power ndash defined asthe degree o consensus in the group that an individual can winfights (see Flack amp Krakauer 2006 Boehm amp Flack 2010 BrushKrakauer amp Flack2013) ndash becomes effec-
tively institutionalized(here meaning hard tochange) policing (anintrinsically costlystrategy) becomesaffordable at least tothose animals that sitin the tail o the powerdistribution thosesuper powerul monkeys who are rarely or never challenged when they break up fights (Flack de Waal amp Krakauer 2005Flack Girvan de Waal amp Krakauer 2006)
My collaborators and I propose that a primary driver o theemergence o new unctionality such as policing is the reduc-tion o environmental uncertainty through the constructiono nested dynamical processes with a range o characteris-tic time constants (Flack Erwin Elliot amp Krakauer 2013)Tese nested dynamical processes arise as components extractregularities rom ast microscopic behavior by coarse-grain-ing (or compressing) the history o events to which they havebeen exposed
Proteins or example can have a long hal-lie relative toRNA transcripts and can be thought o as the summed outputo translation Cells have a long hal-lie relative to proteins
and are a unction o the summed output o arrays o spatiallystructured proteins Both proteins and cells represent someaverage measure o the noisier activity o their constituentsSimilarly a pigtailed macaquersquos estimate o its power is a kind
o average measure othe collective percep-
tion in the groupthat the macaque iscapable o winningights and this is abetter predictor othe cost the macaque will pay during fightsthan the outcome oany single melee as
these outcomes can fluctuate or contextual reasons Tesecoarse-grainings or averages are encoded as slow variables(Flack amp de Waal 2007 Flack 2012 Flack Erwin Elliot
amp Krakauer 2013 see also Feret Danos Krivine Harner ampFontana 2009 or a similar idea) Slow variables may have aspatial component as well as a temporal component as in the protein and cell examples (Figure 6) or minimally only atemporal component as in the monkey example
As a consequence o integrating over abundant microscopic processes slow variables provide better predictors o the localuture configuration o a system than the states o the fluctuatingmicroscopic components In doing so they promote acceleratedrates o microscopic adaptation Slow variables acilitate adapta-tion in two ways Tey allow components to fine-tune theirbehavior and they ree components to search at low cost a larger
3 4
Slow variables provide better predictors
of the local future configuration of a
system than the states of the fluctuating
microscopic componentsrdquo
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Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and
proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since
fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous
starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of
relevant genes and proteins and the output is the target phenot ypic feature
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 17
space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
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April 2014 Santa Fe Institute Bulletin 19
A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
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April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
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22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
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24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 25
More than 70 of our annual
operating budget comes from
private sources - forward-thinking
individuals corporations and
foundations If you believe
complex systems science can
generate the insights that matter
most for science and society makea gift today Visit our website at
wwwsantafeedusupport and
help us achieve great things
ITrsquoS SIMPLE
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2 Santa Fe Institute Bulletin Vol 28
Te projects described in this issue o the Bulletin are allefforts to grapple with some o the key ideas and conceptsrequired to understand living systems with an emphasis onevolution both biological and cultural We ask how overlong stretches o time successively more effective mechanismsor storing and processing inormation have been adaptivelyengineered and how these biological computing systems areused to predict model and control relevant states o noisy andliving environments We consider complexity through the lenso inormation processing we seek to quantiy how inormationis encoded in living systems (genomes and brains) and wesuggest estimates or upper bounds in adaptive inormation
capacity Some o the concepts relevant to understandingbiological complexity include hierarchy individuality criti-cality inormationuncertainty computation and sociality
Stanislaw Lem in his science fiction che-drsquoœuvre Solaris (1961) considers a planet swaddled by an inscrutable oceancapable o astonishing acts o reasoning So vastly more intel-ligent and powerul is the Ocean to the human explorers andscientists (Solarists) who dedicate their lives to its analysisand explication that humanity is orced to resign itsel toignorance over its ultimate mechanisms and motives I haveofen wondered whether Solaris is not a metaphor or lie onearth where the methods o the Solarists are the traditional
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 3
Cronus mutilates his father Uranus at the behest
of his mother Gaia in this scene from Greek
mythology featured in Hesiodrsquos Theogonymethods o science Solaris is a huge interconnected system oenergy and inormation flows that dey traditional methodso reduction Perhaps Solaris is waiting on complexity scienceinormation theory scaling network theory evolutionarydynamics and computation Perhaps we only stand a chanceo understanding complex lie when we approach it throughthe sciences o complexity ndash in which case consider thisissue o the Bulletin a temporary visa granting access to allo those restless explorers intent on the understanding o ourterrestrial Solaris
8122019 How Life Got Complex
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4 Santa Fe Institute Bulletin Vol 28
R ESEARCH on the origins development and dynam-ics o complexity in biological systems has been a coretopic o inquiry at the Santa Fe Institute since its
ounding 30 years ago in 1984 SFIrsquos scientists have workedto develop an understanding o a dizzying array o biological
phenomena rom the origins o lie to transitions rom single-to multi-cellularity to evolutionary innovation at different
levels o biological organization to the relationship betweenecological complexity and dynamical stabilityTe intertwined concepts o energy and inormation are
undamental to any understanding o biological complexityBiological systems are ar rom equilibrium requiring a constantflow o energy to maintain their organization and unction-ality Te processing and encoding o inormation providesa means or lie to manage and maintain energy acquisitionuse and dissipation
Te work o ormer resident proessors and current externalaculty members Jessica Flack and David Krakauer (and their manycolleagues) highlighted in this issue o the SFI Bulletin addressesthe intimate dance between energy and inormation processing inbiological systems ndash a dance they suggest gives rise to the complexmulti-scale structure we observe
Tis computational-thermodynamic view o biology provides
a powerul potentially generic ramework or understanding the properties o any kind o complex adaptive system including socio-economic systems
Sincerely
Jennier Dunne Chair o the Faculty Santa Fe Institute
Biological Systems Energy and Information
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 5
IMAGE CREDITS THIS PAGE FROM TOP BRAIN DRAWING HESSDESIGNWORKSCOM EARTH FROM SPACE NASA ROUSSEAU THE DREAM WIKIMEDIA COMMONSFACING PAGE PORTRAIT OF JENNIFER DUNNE MINESH BACRANIA PHOTOGRAPHY
SFI BULLETIN
VOL 28 APRIL 2014Contents
0 Perspectives The Complexity of Life
By DAVID KRAKAUER
4 Biological Systems Energy and Information
By JENNIFER DUNNE
6 Why Nature Went to the Trouble of CreatingYouBy NATHAN COLLINS
12 Lifersquos Information Hierarchy
By JESSICA FLACK
ldquo There is nothing that living things do that cannot be
understood from the point of view that they are made
of atoms acting according to the laws of physicsrdquo ndash R I C H A R D F E Y N M A N
12
6
0
8122019 How Life Got Complex
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8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 7April 2014 Santa Fe Institute Bulletin 7
Y pestis the bacterium that causes bubonic
plague (aka The Black Death of the Middle Ages)
Lie on earth began some 35 billion
years ago not all that long afer the planet itsel first ormed and or 15
billion years it chugged along single-celledcreatures sel-replicating dividing diversi-ying Remarkably it took that long ndash 7500times longer than all o human history ndash orthe first multicellular lie to emerge and stilllonger or it to evolve into lie as we know it
Despite that the question manyresearchers ask isnrsquot what took so long
but rather why complex lie would haveevolved in the first place Consider thissingle-celled organisms make up more thanhal o the biomass on earth and even oneo the tiniest organisms ndash Y pestis betterknown as bubonic plague ndash can effortlesslythoughtlessly kill you
Nature it seems doesnrsquot need youIndeed there isnrsquot any obvious reasonit would go to the trouble o creatingsomething as complex as a human beingcomplete with its differentiated organs and
top-down control systems And yet despiteour billion years o Naturersquos great ldquomehrdquohere you are alive multicellular complex
ndash even intelligent enough to ponder yourown existence
What was Nature thinking Accordingto one argument bacteria first boundtogether in colonies that enhanced cooper-ation and hence survival Eventually thosebacteria bound together physically as wellcreating the first multicellular lie Andso on
ldquoYou could say thatrsquos an answer but then you could go a bit urther and ask what isit exactly that makes you a better compet-
itorrdquo says David Krakauer who with SFIExternal Proessor Jessica Flack co-directs
the Wisconsin Institute or DiscoveryrsquosCenter or Complexity and CollectiveComputation or C4 and SFIrsquos Johnempleton Foundation-unded ldquoEvolutiono Complexity and Intelligence on Earthrdquoresearch project
ldquoWell yoursquore outsmarting everyone elserdquoKrakauer says
Simple versus complex
Despite its seeming indifference Naturedoes seem to have thought highly enough
Why Nature Went to the Trouble
of CreatinghellipYou BY NATHAN COLLINS
In a complex world where plants and animals and everything else are duking
it out to survive an organism stands to gain from becoming more complex
o complex structures to produce a ew othem and to have ratcheted up the complex-ity urther by embedding complex structures
within complex structures ndash animals withhearts and lungs and circulatory systems orgroups o people capable o building theirown social institutions
But why What purpose does it serveldquoWhy lie is hierarchically organized isnot at all obviousrdquo Flack says and howan organismrsquos or a societyrsquos complexity
relates to the complexity o its environ-ment remains unclear
Our anthropomorphized Nature mighthave started with one very simple idea
what Krakauer calls the reflection princi- ple which presupposes that living thingscanrsquot be more complex than their environ-ments an idea rooted in experiments ldquoI
you take organisms and you place themin simpler environments they just throweverything [superfluous] away Tey losegenesrdquo Krakauer says
At the same time the world does seem toavor an intelligent creature Even the tiniestliving things need to be able to comprehend predict and react to their environmentsthatrsquos what allows them to outsmart eachother he says In a complex world where
plants and animals and everything else areduking it out to survive an organism standsto gain rom becoming more complex
Tat tension between simplicity andcomplexity is the starting point or C4
postdoctoral ellow Christopher Ellison
8122019 How Life Got Complex
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8 Santa Fe Institute Bulletin Vol 28
Watch David Krakauerrsquos interview at
wwwsantafeedulife
What does Nature care about hearts brains and other organs or for that
matter political parties ndash in other words structures within structures
ldquoWersquod like to understand the implica-tions this has or the environmentrdquo hesays ldquoFor example do simple or complexorganisms experience and live in simple orcomplex environmentsrdquo
Working with Flack and KrakauerEllison developed ldquoMarkov organismsrdquo
computer-simulated creatures that merge
insights rom biology with inormation processing techniques rom computerscience to help figure out how lie balancesthese trade-offs Rather than modeling real
organisms themselves he ocuses on howinormation flows in the ecological system
Itrsquos early days Ellison says but his simula-tions suggest that lie will ofen evolve tomatch its environmentrsquos complexity ndash findingsthat are in line with the reflection principlebut with some interesting caveats For one
thing evolving Markov organisms tend toovershoot their worldsrsquocomplexity and mighttake a long time to pruneunnecessary complexities
Teyrsquore also suscepti-ble to ldquobasis mismatchrdquo a
problem you know welli yoursquove ever tried to explain to a tourist howto get around in your hometown o youthere are just a ew steps but to the noviceitrsquos a complex process with many twists and
turns and every intersection represents a possible misdirection in sprawling cities likeLos Angeles a direction as simple as ldquotakeSunset to Vermont and turn lefrdquo becomesinfinitely complex Markov organisms arethe same i their way o solving a problem
doesnrsquot line up with how their environmentsconstructed it Ellisonrsquos simulations showMarkov organismsrsquo complexity keeps evolv-ing upward orever
But Ellisonrsquos inormation-centricapproach has some benefits One he says
ldquois that it attempts to answer the questiono how complexity evolves in an organ-ism-independent ashionrdquo meaning thatthe ideas apply equally well to anythingrom bacteria to politics Similarly Ellisonrsquosmethod allows the team to describe both anorganism and its environmentrsquos complexity
in the same terms because both derive romthe same underlying models o inormation
processing Surprisingly thatrsquos somethingew i any other researchers have done
Constructing predictability
So it appears Nature might avor multicel-lular lie i it affords a certain computational power not readily available to single-celledorganisms But what does Nature care abouthearts brains and other organs or or thatmatter political parties ndash in other words
structures within structureshe answer Flack says is that living
things like their worlds to be predictableand what makes cells and people more likelyto survive Nature avors
Much o the structure we observe in the world Flack says probably evolved becausestructure begets stability hence predictabilityGroups o genes cells or animals changetheir collective behaviors slowly compared
with individual genes cells or animals givingthe aster-moving individual components a
chance to anticipate changes more easilyBiologists call the idea that plants andanimals ndash and genes and organs and so on
ndash structure their environments to be morestable and predictable ldquoniche constructionrdquoand it usually applies to physical structurelike ants building nests But it also can beapplied to temporal structure
Politics offers perhaps a simple exampleEarly US politicians were explicit aboutdesigning Congress and the rest o govern-ment so that it would change gradually and
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 9April 2014 Santa Fe Institute Bulletin 9
According to the reflection principle organisms are reflections of their environments Here the environment is represented as a prose excerpt from
Darwinrsquos Origin of Species wherein he contemplates an entangled bank filled with ldquoendless forms most beautiful and most wonderfulrdquo each evolved through natural selection Organism A matches the environmentrsquos complexity while Organism B is less complex than the environment and Organism C is
more complex than the environment The research team is asking when evolution satisfies or v iolates the reflection principle
be more stable Even when we complainabout our slow-moving government weare undoubtedly comorted by that verycharacteristic because slow is predictableAnd when our environment is predictable we know how to make sound decisions
So people construct relatively slow-mov-ing predictable institutions At the sametime those institutions help shape thebehavior o the individuals who createdthem points out C4 postdoctoral ellowPhilip Poon who is examining eedbackrom institutions to explain (in the govern-ment case) why or example Democrats andRepublicans seem to trade off controllingthe White House and Congress every ewterms A critical issue is o course howthat eedback works rom a mechanical
perspective ndash that is how the ways individ-uals perceive and understand institutionsinfluence their decision-making
Drawing on the theory o phase transi-tions the same one that explains whychanging a seemingly minor variable cansuddenly shif an entire system rom onestate to another Flack Krakauer and C4researcher and ormer SFI PostdoctoralFellow Bryan Daniels argue that hierarchi-cal structures bestow another advantageeicient inormation low rom the
collective to its individual parts Systems perched on the edge o a phase transitionare exquisitely sensitive so that a small orlocalized change ldquoleads to a large change inthe global dynamicsrdquo Daniels says Toughthat might seem unstable or chaotic systems
near the critical point where a phase transi-
tion begins are actually quite predictableGroups o macaque monkeys ndash one o
Flack and the teamrsquos earliest sources o data
and inspiration ndash are one system that appearsto be resting near a phase transition Whenmonkeys arenrsquot eeling especially aggres-sive Daniels says things are stable and ldquoi Isuddenly act out nothingrsquos going to happenhellipbut i [the group is] sitting at the critical
point then [my] contribution is always moreimportantrdquo and one extra monkey picking afight is enough to kick off a large-scale brawl
Below the critical point individualmonkeys act airly independently but rightat the transition their behavior is tightly
ldquoWhen monkeys arenrsquot feeling especially aggressive
things are stable but if the group is sitting at the
critical point one extra monkey picking a fight
is enough to kick off a large-scale brawlrdquo
coordinated and individual monkeys acttogether as one Tat Krakauer empha-sizes eases the flow o inormation romthe system as a whole to its constituent
parts making it all the more predictableNature is rie with examples one birdrsquos
sudden course correction changing the
direction o an entire flock alternatingDemocrat and Republican control oCongress and so on
Social circuitry
In the abstract Nature has good reasonto avor complexity and hierarchy ndash eachin its own way makes the tasks o compre-hension prediction and strategizingeasier But its a third concept circuitsthat grounds Flack and Krakauerrsquos teamand lays the practical oundation ormuch o their work
Usually the word ldquocircuitrdquo conjuresimages o the transistors microprocessors
8122019 How Life Got Complex
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10 Santa Fe Institute Bulletin Vol 28
Time series representation of the dis tribution of fight sizes in a macaque society Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once An outburst by an individual wonrsquot normally tip the scales But when the system is perched at
a phase transition a brawl among many individuals can erupt
and lengths o copper wire that make upa computer Te analogy is apt Like anelectronic circuit individual components
ndash genes organs people ndash inormational-ly bind living things to orm a kind obiological or social computer In act the
circuit approach to describing a systemstems rom a hunch that the hierarchicalscales present throughout nature ldquoarise
Time
F i g h
t 1
F i g h
t 2
Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once
Vd
Pa
Je
Cb
Yv
Ta
Sg
Qs
Po
Mv
Ip
Fp
Eo
Dh
Tr
Pr
Fc
Zu
Vf
Fp
Ec
Zu
Zr
Iw
Is
Fp
Fc
Cc
Yv
Cd
Ta
Cc
Qv
Ip
Cb
Pa
Eb
Sb
Jc
Je
Hp
Lr
Jc
Zu
Po
Dh
Vf
Eo
Eo
Cd
Pa
Dh
Hh
At
Zu
Yv
Th
Ta
Fn
through a process o collective computa-tionrdquo creating slow-changing predictablesocial and biological structures Flack says
Circuits are more than just an analo-gy though ndash theyrsquore the tools that bridgethe gap between individual and collective
behavior And in a scientific field whereitrsquos easy to avoid real data circuits are one
way Flack and Krakauerrsquos research group
makes sure their theories are a good matchto the real world ldquoOur group is committedto an empirical approachrdquo Flack says ldquoWebelieve that only when these measures aredeveloped with an understanding o thedata generated by real systems will they
be useulrdquoTe process o building circuits begins
by analyzing how a systemrsquos individual parts work together Using the macaquefight data Flack Krakauer and ormerSFI Omidyar Fellow Simon DeDeo (nowat Indiana University Bloomington) devel-oped a statistical method they dubbedinductive game theory to analyze howthe monkeys reacted to othersrsquo fights Teresulting social circuit Flack says servesas a detailed model ldquoor how the micro-
scopic behavior maps to the unctionallyimportant macroscopic eatures o socialstructurerdquo such as the distribution o fightsizes In other words to construct a socialor biological circuit is to understand how agroup builds and maintains stable predict-able inormation hierarchies
Te final step is to produce a simplifiedsocial circuit what the researchers call a
ldquocognitive effective theoryrdquo that accurate-ly predicts how groups behave Te aimis to extract the key sorts o interactions
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 11
responsible or power structures fight-sizedistributions or other macroscopic eaturesusing ldquowhat we know about individual orcomponent cognition to coarse grain orcompressrdquo social circuits Flack says Suchcompression is essential she says because
living things canrsquot base their decisions on what ever y other living thing is doing instead theyrsquore orced to pay attention to
just a ew patterns or details o whatrsquos goingon around them
Te key question here as collaboratorand Princeton University graduate studentEleanor Brush puts it is how little inor-mation individuals need to successullyoutsmart others
Accidental or inevitable
Answering questions like that one ndash or testingsome o the teamrsquos more abstract predic-tions ndash remains a central challenge Poonor example describes his studies o electioncycles and policy change as ldquotoy modelsrdquo ndashthey capture qualitative eatures o the datasuch as party switching but donrsquot stand upto more precise quantitative tests
Meanwhile Krakauer and others sayitrsquos not always clear how to test particularhypotheses such as the prediction that basismismatch leads to ever-increasing complexity
in living things ldquoWersquore still looking or somecompelling examplerdquo Ellison says ldquoPart othe issue is that one can ofen play the devilrsquosadvocate and call into question the examplerdquoone reason why his work to construct ormal
precise measures o complexity is so import-ant he says
New techniques or rapidly analyzinggenetic data Krakauer says might improvethe situation Combining those techniques
with laboratory-based ldquoexperimental evolutionrdquo
in which researchers study the effects o preciseenvironmental changes on small organismssuch as bacteria could help test some o theendeavorrsquos core ideas such as the reflection
principle or the role o phase transitionsAnother potential avenue is to use ldquodigital
sources like computer games where we cancontrol to a large extent the orm o the dataor the conditions under which they werecollectedrdquo Krakauer says
esting their theories is just one part o theteamrsquos ambitious aims Tey hope Flack says
People gather in Washington DC before Martin Luther King Jrrsquos ldquoI Have a Dreamrdquo speech on Aug 28 1963 to demand equalities
Individuals tend to move faster and be less predictable than the slow-moving institutions they create
to achieve nothing less than an understandingo why lie is organized the way it is rom thesmallest bacteria to the largest human insti-tutions Tat requires combining real-worldobservation and abstract mathematical theoryin novel and creative ways
And as i that wasnrsquot enough Krakauerhas one more question in mind is lie anaccident or is it inevitable And i lie isinevitable well are we alone
ldquoI itrsquos not a product o a series o random
accidents but therersquos an underlying law-likeregularity that would give us confidence inbelieving in the possibility o lie presenteverywhere in the universerdquo he says ldquoSo
when one asks why does it matter whetheritrsquos chance or necessity it matters i we care
whether wersquore alone are notrdquo
Nathan Collins is a feelance science writernew ather and film aficionado based inSan Francisco
ldquoLiving things canrsquot base their decisions on what
every other living thing is doing instead theyrsquore
forced to pay attention to just a few patternsrdquo
8122019 How Life Got Complex
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12 Santa Fe Institute Bulletin Vol 28
ldquohellipin mere Time all things follow
one another and in mere Space
all things are side by side it is
accordingly only by the combi-
nation of Time and Space thatthe representation of coexistence
arisesrdquo
mdash ARTHUR SCHOPENHAUER On the Fourfold Root
of the Principle of Sufficient Reason 1813
Yves Tanguy Indefinite Divisibility 1942
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 13
D I A C O M M O N S I N S E T H E N R I R O U S S E A U T H E
D R E A M 1 9 1 0 W I K I M E D I A C O M M O N S
Biological systems ndash rom cells totissues to individuals to societ-ies ndash are hierarchically organized
(eg Feldman amp Eschel 1982 Buss 1987Smith amp Szathmaacutery 1998 Valentineamp May 1996 Michod 2000 Frank2003) o many hierarchical organiza-tion suggests the nesting o components orindividuals into groups with these groupsaggregating into yet larger groups But this view ndash at least superficially ndash privilegesspace and matter over time and inorma-tion Many types o neural coding orexample require averaging or summing over neural firing ratesTe neuronsrsquo spatial location ndash that they are in proximity ndash iso course important but at least as important to the encoding
The explanation for the complex multi-scale structure of biological and social systems
lies in their manipulation of space and time to reduce uncertainty about the future
is their behavior in time Likewise insome monkey societies as I will discussin detail later in this review individualsestimate the uture cost o social inter-action by encoding the average outcomeo past interactions in special signals and
then summing over these signalsIn both examples inormation rom
events distributed in time as well as space(Figure 1) is captured with encodingsthat are used to control some behavioraloutput My collaborators and I in theCenter or Complexity amp Collective
Computation are exploring the idea that hierarchical organiza-tion at its core is a nesting o these kinds o unctional encodingsAs I will explain we think these unctional encodings result
LIFErsquoS INFORMATION HIERARCHY
BY JESSICA C FLACK
CO-DIRECTOR CENTER FOR COMPLEXITY amp COLLECTIVE COMPUTATION
WISCONSIN INSTITUTE FOR DISCOVERY UNIVERSITY OF WISCONSIN-MADISON
EXTERNAL PROFESSOR SANTA FE INSTITUTE
Jessica Flack
Number of spatial dimensions
N u m b e
r o f t i m e d i m e n s i o n s
0
0
1
2
3
4
5
1 2 3 4 5
UNSTABLE
TOO SIMPLE
UNPREDICTABLE (ELLIPTIC)
UNPREDICTABLE(ULTRAHYPERBOLIC)
TACHYONSONLY
UNSTABLE
Figure 1 The dimensionality of the time-space
continuum with properties postulated when x
does not equal 3 and y is larger than 1 Life on
earth exist s in three spatial dimensions and one
temporal dimension Biological systems effec-
tively ldquodiscretizerdquo time and space to reduce
environmental uncertainty by coarse-graining
and compressing environmental time series to
find regularities Components use the coarse-
grained descriptions to predict the future
tuning their behavior to their predictions
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rom biological systemsmanipulating space andtime (Figure 2) to acilitateinormation extraction which in turn acilitatesmore efficient extraction
o energy h i s inormation
hierarchy appears to bea universal property obiological systems andmay be the key to one oliersquos greatest mysteries ndashthe origins o biologicalcomplexity In this essayI review a body o work byDavid Krakauer myseland our research group
that has been inspiredby many years o work atthe Santa Fe Institute (eg Crutchfield 1994 Gell-Mann1996 Gell-Mann amp Lloyd 1996 Fontana amp Buss 1996 West Brown amp Enquist 1997 Fontana amp Schuster 1998Ancel amp Fontana 2000 Stadler Stadler Wagner amp Fontana2001 Smith 2003 Crutchfield amp Goumlrnerup 2006 Smith2008) Our work suggests that complexity and the multi-scalestructure o biological systems are the predictable outcomeo evolutionary dynamics driven by uncertainty minimiza-tion (Krakauer 2011 Flack 2012 Flack Erwin Elliot ampKrakauer 2013)
Tis recasting o the evolutionary process as an inerentialone1 (Bergstrom amp Rosvall 2009 Krakauer 2011) is based onthe premise that organisms and other biological systems canbe viewed as hypotheses about the present and uture environ-ments they or their offspring will encounter induced rom thehistory o past environmental states they or their ancestors have
experienced (eg Crutchfield amp Feldman 2001 Krakauer ampZannotto 2009 Ellison Flack amp Krakauer in prep) Tis premise o course only holds i the past is prologue ndash that ishas regularities and the regularities can be estimated and evenmanipulated (as in niche construction) by biological systems ortheir components to produce adaptive behavior (Flack ErwinElliot amp Krakauer 2013 Ellison Flack amp Krakauer in prep)
I these premises are correct lie at its core is computa-tional and a central question becomes How do systems andtheir components estimate and control the regularity in theirenvironments and use these estimates to tune their strategiesI suggest that the answer to this question and the explanation
or complexity is that biological systems manipulate spatialand temporal structure to produce order ndash low variance ndash atlocal scales
UNCERTAINTY REDUCTION
Te story I want to tell starts with the observation that witheach new level o organization typically comes new unction-ality ndash a new eature with positive payoff consequences or thesystem as a whole or or its components (Flack Erwin Elliot
1 Tis idea is related to work on Maxwel lrsquos Demon (eg Krakauer2011 Mandal 983121uan amp Jarzynski 2013) and the Carnot cycle ( egSmith 2003) but we do not yet understand the mapping
Figure 2 Biological systems ndash from
(left to right ) Volvox colonies to slime
molds to animal societies to large-scale
ecosystems such as reefs t o human
cities ndash are hierarchically organized with
multiple functionally important time and
space scales All feature 1) components
with only partially aligned interests
exhibiting coherent behavior at the
aggregate level 2) components that turn
over and that co-exist in the sys tem at
varying stages of development
3) social structure that persists but
component behavior that fluctuates and
4) macroscopic variation in temporal
and spatial structure and coupling with
microscopic behavior which has func-
tional impl ications when the components
can perceive ndash in evolutionary develop-
mental or ecological time ndash regularities
at the macroscopic scale
1 2
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April 2014 Santa Fe Institute Bulletin 15
amp Krakauer 2013) Policing in a pigtailed macaque group is anexample Once a heavy tailed distribution o power ndash defined asthe degree o consensus in the group that an individual can winfights (see Flack amp Krakauer 2006 Boehm amp Flack 2010 BrushKrakauer amp Flack2013) ndash becomes effec-
tively institutionalized(here meaning hard tochange) policing (anintrinsically costlystrategy) becomesaffordable at least tothose animals that sitin the tail o the powerdistribution thosesuper powerul monkeys who are rarely or never challenged when they break up fights (Flack de Waal amp Krakauer 2005Flack Girvan de Waal amp Krakauer 2006)
My collaborators and I propose that a primary driver o theemergence o new unctionality such as policing is the reduc-tion o environmental uncertainty through the constructiono nested dynamical processes with a range o characteris-tic time constants (Flack Erwin Elliot amp Krakauer 2013)Tese nested dynamical processes arise as components extractregularities rom ast microscopic behavior by coarse-grain-ing (or compressing) the history o events to which they havebeen exposed
Proteins or example can have a long hal-lie relative toRNA transcripts and can be thought o as the summed outputo translation Cells have a long hal-lie relative to proteins
and are a unction o the summed output o arrays o spatiallystructured proteins Both proteins and cells represent someaverage measure o the noisier activity o their constituentsSimilarly a pigtailed macaquersquos estimate o its power is a kind
o average measure othe collective percep-
tion in the groupthat the macaque iscapable o winningights and this is abetter predictor othe cost the macaque will pay during fightsthan the outcome oany single melee as
these outcomes can fluctuate or contextual reasons Tesecoarse-grainings or averages are encoded as slow variables(Flack amp de Waal 2007 Flack 2012 Flack Erwin Elliot
amp Krakauer 2013 see also Feret Danos Krivine Harner ampFontana 2009 or a similar idea) Slow variables may have aspatial component as well as a temporal component as in the protein and cell examples (Figure 6) or minimally only atemporal component as in the monkey example
As a consequence o integrating over abundant microscopic processes slow variables provide better predictors o the localuture configuration o a system than the states o the fluctuatingmicroscopic components In doing so they promote acceleratedrates o microscopic adaptation Slow variables acilitate adapta-tion in two ways Tey allow components to fine-tune theirbehavior and they ree components to search at low cost a larger
3 4
Slow variables provide better predictors
of the local future configuration of a
system than the states of the fluctuating
microscopic componentsrdquo
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Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and
proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since
fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous
starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of
relevant genes and proteins and the output is the target phenot ypic feature
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space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
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deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
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A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
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vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
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22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
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24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 25
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operating budget comes from
private sources - forward-thinking
individuals corporations and
foundations If you believe
complex systems science can
generate the insights that matter
most for science and society makea gift today Visit our website at
wwwsantafeedusupport and
help us achieve great things
ITrsquoS SIMPLE
Support
ComplexityScience
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April 2014 Santa Fe Institute Bulletin 3
Cronus mutilates his father Uranus at the behest
of his mother Gaia in this scene from Greek
mythology featured in Hesiodrsquos Theogonymethods o science Solaris is a huge interconnected system oenergy and inormation flows that dey traditional methodso reduction Perhaps Solaris is waiting on complexity scienceinormation theory scaling network theory evolutionarydynamics and computation Perhaps we only stand a chanceo understanding complex lie when we approach it throughthe sciences o complexity ndash in which case consider thisissue o the Bulletin a temporary visa granting access to allo those restless explorers intent on the understanding o ourterrestrial Solaris
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4 Santa Fe Institute Bulletin Vol 28
R ESEARCH on the origins development and dynam-ics o complexity in biological systems has been a coretopic o inquiry at the Santa Fe Institute since its
ounding 30 years ago in 1984 SFIrsquos scientists have workedto develop an understanding o a dizzying array o biological
phenomena rom the origins o lie to transitions rom single-to multi-cellularity to evolutionary innovation at different
levels o biological organization to the relationship betweenecological complexity and dynamical stabilityTe intertwined concepts o energy and inormation are
undamental to any understanding o biological complexityBiological systems are ar rom equilibrium requiring a constantflow o energy to maintain their organization and unction-ality Te processing and encoding o inormation providesa means or lie to manage and maintain energy acquisitionuse and dissipation
Te work o ormer resident proessors and current externalaculty members Jessica Flack and David Krakauer (and their manycolleagues) highlighted in this issue o the SFI Bulletin addressesthe intimate dance between energy and inormation processing inbiological systems ndash a dance they suggest gives rise to the complexmulti-scale structure we observe
Tis computational-thermodynamic view o biology provides
a powerul potentially generic ramework or understanding the properties o any kind o complex adaptive system including socio-economic systems
Sincerely
Jennier Dunne Chair o the Faculty Santa Fe Institute
Biological Systems Energy and Information
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 5
IMAGE CREDITS THIS PAGE FROM TOP BRAIN DRAWING HESSDESIGNWORKSCOM EARTH FROM SPACE NASA ROUSSEAU THE DREAM WIKIMEDIA COMMONSFACING PAGE PORTRAIT OF JENNIFER DUNNE MINESH BACRANIA PHOTOGRAPHY
SFI BULLETIN
VOL 28 APRIL 2014Contents
0 Perspectives The Complexity of Life
By DAVID KRAKAUER
4 Biological Systems Energy and Information
By JENNIFER DUNNE
6 Why Nature Went to the Trouble of CreatingYouBy NATHAN COLLINS
12 Lifersquos Information Hierarchy
By JESSICA FLACK
ldquo There is nothing that living things do that cannot be
understood from the point of view that they are made
of atoms acting according to the laws of physicsrdquo ndash R I C H A R D F E Y N M A N
12
6
0
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8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 7April 2014 Santa Fe Institute Bulletin 7
Y pestis the bacterium that causes bubonic
plague (aka The Black Death of the Middle Ages)
Lie on earth began some 35 billion
years ago not all that long afer the planet itsel first ormed and or 15
billion years it chugged along single-celledcreatures sel-replicating dividing diversi-ying Remarkably it took that long ndash 7500times longer than all o human history ndash orthe first multicellular lie to emerge and stilllonger or it to evolve into lie as we know it
Despite that the question manyresearchers ask isnrsquot what took so long
but rather why complex lie would haveevolved in the first place Consider thissingle-celled organisms make up more thanhal o the biomass on earth and even oneo the tiniest organisms ndash Y pestis betterknown as bubonic plague ndash can effortlesslythoughtlessly kill you
Nature it seems doesnrsquot need youIndeed there isnrsquot any obvious reasonit would go to the trouble o creatingsomething as complex as a human beingcomplete with its differentiated organs and
top-down control systems And yet despiteour billion years o Naturersquos great ldquomehrdquohere you are alive multicellular complex
ndash even intelligent enough to ponder yourown existence
What was Nature thinking Accordingto one argument bacteria first boundtogether in colonies that enhanced cooper-ation and hence survival Eventually thosebacteria bound together physically as wellcreating the first multicellular lie Andso on
ldquoYou could say thatrsquos an answer but then you could go a bit urther and ask what isit exactly that makes you a better compet-
itorrdquo says David Krakauer who with SFIExternal Proessor Jessica Flack co-directs
the Wisconsin Institute or DiscoveryrsquosCenter or Complexity and CollectiveComputation or C4 and SFIrsquos Johnempleton Foundation-unded ldquoEvolutiono Complexity and Intelligence on Earthrdquoresearch project
ldquoWell yoursquore outsmarting everyone elserdquoKrakauer says
Simple versus complex
Despite its seeming indifference Naturedoes seem to have thought highly enough
Why Nature Went to the Trouble
of CreatinghellipYou BY NATHAN COLLINS
In a complex world where plants and animals and everything else are duking
it out to survive an organism stands to gain from becoming more complex
o complex structures to produce a ew othem and to have ratcheted up the complex-ity urther by embedding complex structures
within complex structures ndash animals withhearts and lungs and circulatory systems orgroups o people capable o building theirown social institutions
But why What purpose does it serveldquoWhy lie is hierarchically organized isnot at all obviousrdquo Flack says and howan organismrsquos or a societyrsquos complexity
relates to the complexity o its environ-ment remains unclear
Our anthropomorphized Nature mighthave started with one very simple idea
what Krakauer calls the reflection princi- ple which presupposes that living thingscanrsquot be more complex than their environ-ments an idea rooted in experiments ldquoI
you take organisms and you place themin simpler environments they just throweverything [superfluous] away Tey losegenesrdquo Krakauer says
At the same time the world does seem toavor an intelligent creature Even the tiniestliving things need to be able to comprehend predict and react to their environmentsthatrsquos what allows them to outsmart eachother he says In a complex world where
plants and animals and everything else areduking it out to survive an organism standsto gain rom becoming more complex
Tat tension between simplicity andcomplexity is the starting point or C4
postdoctoral ellow Christopher Ellison
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8 Santa Fe Institute Bulletin Vol 28
Watch David Krakauerrsquos interview at
wwwsantafeedulife
What does Nature care about hearts brains and other organs or for that
matter political parties ndash in other words structures within structures
ldquoWersquod like to understand the implica-tions this has or the environmentrdquo hesays ldquoFor example do simple or complexorganisms experience and live in simple orcomplex environmentsrdquo
Working with Flack and KrakauerEllison developed ldquoMarkov organismsrdquo
computer-simulated creatures that merge
insights rom biology with inormation processing techniques rom computerscience to help figure out how lie balancesthese trade-offs Rather than modeling real
organisms themselves he ocuses on howinormation flows in the ecological system
Itrsquos early days Ellison says but his simula-tions suggest that lie will ofen evolve tomatch its environmentrsquos complexity ndash findingsthat are in line with the reflection principlebut with some interesting caveats For one
thing evolving Markov organisms tend toovershoot their worldsrsquocomplexity and mighttake a long time to pruneunnecessary complexities
Teyrsquore also suscepti-ble to ldquobasis mismatchrdquo a
problem you know welli yoursquove ever tried to explain to a tourist howto get around in your hometown o youthere are just a ew steps but to the noviceitrsquos a complex process with many twists and
turns and every intersection represents a possible misdirection in sprawling cities likeLos Angeles a direction as simple as ldquotakeSunset to Vermont and turn lefrdquo becomesinfinitely complex Markov organisms arethe same i their way o solving a problem
doesnrsquot line up with how their environmentsconstructed it Ellisonrsquos simulations showMarkov organismsrsquo complexity keeps evolv-ing upward orever
But Ellisonrsquos inormation-centricapproach has some benefits One he says
ldquois that it attempts to answer the questiono how complexity evolves in an organ-ism-independent ashionrdquo meaning thatthe ideas apply equally well to anythingrom bacteria to politics Similarly Ellisonrsquosmethod allows the team to describe both anorganism and its environmentrsquos complexity
in the same terms because both derive romthe same underlying models o inormation
processing Surprisingly thatrsquos somethingew i any other researchers have done
Constructing predictability
So it appears Nature might avor multicel-lular lie i it affords a certain computational power not readily available to single-celledorganisms But what does Nature care abouthearts brains and other organs or or thatmatter political parties ndash in other words
structures within structureshe answer Flack says is that living
things like their worlds to be predictableand what makes cells and people more likelyto survive Nature avors
Much o the structure we observe in the world Flack says probably evolved becausestructure begets stability hence predictabilityGroups o genes cells or animals changetheir collective behaviors slowly compared
with individual genes cells or animals givingthe aster-moving individual components a
chance to anticipate changes more easilyBiologists call the idea that plants andanimals ndash and genes and organs and so on
ndash structure their environments to be morestable and predictable ldquoniche constructionrdquoand it usually applies to physical structurelike ants building nests But it also can beapplied to temporal structure
Politics offers perhaps a simple exampleEarly US politicians were explicit aboutdesigning Congress and the rest o govern-ment so that it would change gradually and
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 9April 2014 Santa Fe Institute Bulletin 9
According to the reflection principle organisms are reflections of their environments Here the environment is represented as a prose excerpt from
Darwinrsquos Origin of Species wherein he contemplates an entangled bank filled with ldquoendless forms most beautiful and most wonderfulrdquo each evolved through natural selection Organism A matches the environmentrsquos complexity while Organism B is less complex than the environment and Organism C is
more complex than the environment The research team is asking when evolution satisfies or v iolates the reflection principle
be more stable Even when we complainabout our slow-moving government weare undoubtedly comorted by that verycharacteristic because slow is predictableAnd when our environment is predictable we know how to make sound decisions
So people construct relatively slow-mov-ing predictable institutions At the sametime those institutions help shape thebehavior o the individuals who createdthem points out C4 postdoctoral ellowPhilip Poon who is examining eedbackrom institutions to explain (in the govern-ment case) why or example Democrats andRepublicans seem to trade off controllingthe White House and Congress every ewterms A critical issue is o course howthat eedback works rom a mechanical
perspective ndash that is how the ways individ-uals perceive and understand institutionsinfluence their decision-making
Drawing on the theory o phase transi-tions the same one that explains whychanging a seemingly minor variable cansuddenly shif an entire system rom onestate to another Flack Krakauer and C4researcher and ormer SFI PostdoctoralFellow Bryan Daniels argue that hierarchi-cal structures bestow another advantageeicient inormation low rom the
collective to its individual parts Systems perched on the edge o a phase transitionare exquisitely sensitive so that a small orlocalized change ldquoleads to a large change inthe global dynamicsrdquo Daniels says Toughthat might seem unstable or chaotic systems
near the critical point where a phase transi-
tion begins are actually quite predictableGroups o macaque monkeys ndash one o
Flack and the teamrsquos earliest sources o data
and inspiration ndash are one system that appearsto be resting near a phase transition Whenmonkeys arenrsquot eeling especially aggres-sive Daniels says things are stable and ldquoi Isuddenly act out nothingrsquos going to happenhellipbut i [the group is] sitting at the critical
point then [my] contribution is always moreimportantrdquo and one extra monkey picking afight is enough to kick off a large-scale brawl
Below the critical point individualmonkeys act airly independently but rightat the transition their behavior is tightly
ldquoWhen monkeys arenrsquot feeling especially aggressive
things are stable but if the group is sitting at the
critical point one extra monkey picking a fight
is enough to kick off a large-scale brawlrdquo
coordinated and individual monkeys acttogether as one Tat Krakauer empha-sizes eases the flow o inormation romthe system as a whole to its constituent
parts making it all the more predictableNature is rie with examples one birdrsquos
sudden course correction changing the
direction o an entire flock alternatingDemocrat and Republican control oCongress and so on
Social circuitry
In the abstract Nature has good reasonto avor complexity and hierarchy ndash eachin its own way makes the tasks o compre-hension prediction and strategizingeasier But its a third concept circuitsthat grounds Flack and Krakauerrsquos teamand lays the practical oundation ormuch o their work
Usually the word ldquocircuitrdquo conjuresimages o the transistors microprocessors
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10 Santa Fe Institute Bulletin Vol 28
Time series representation of the dis tribution of fight sizes in a macaque society Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once An outburst by an individual wonrsquot normally tip the scales But when the system is perched at
a phase transition a brawl among many individuals can erupt
and lengths o copper wire that make upa computer Te analogy is apt Like anelectronic circuit individual components
ndash genes organs people ndash inormational-ly bind living things to orm a kind obiological or social computer In act the
circuit approach to describing a systemstems rom a hunch that the hierarchicalscales present throughout nature ldquoarise
Time
F i g h
t 1
F i g h
t 2
Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once
Vd
Pa
Je
Cb
Yv
Ta
Sg
Qs
Po
Mv
Ip
Fp
Eo
Dh
Tr
Pr
Fc
Zu
Vf
Fp
Ec
Zu
Zr
Iw
Is
Fp
Fc
Cc
Yv
Cd
Ta
Cc
Qv
Ip
Cb
Pa
Eb
Sb
Jc
Je
Hp
Lr
Jc
Zu
Po
Dh
Vf
Eo
Eo
Cd
Pa
Dh
Hh
At
Zu
Yv
Th
Ta
Fn
through a process o collective computa-tionrdquo creating slow-changing predictablesocial and biological structures Flack says
Circuits are more than just an analo-gy though ndash theyrsquore the tools that bridgethe gap between individual and collective
behavior And in a scientific field whereitrsquos easy to avoid real data circuits are one
way Flack and Krakauerrsquos research group
makes sure their theories are a good matchto the real world ldquoOur group is committedto an empirical approachrdquo Flack says ldquoWebelieve that only when these measures aredeveloped with an understanding o thedata generated by real systems will they
be useulrdquoTe process o building circuits begins
by analyzing how a systemrsquos individual parts work together Using the macaquefight data Flack Krakauer and ormerSFI Omidyar Fellow Simon DeDeo (nowat Indiana University Bloomington) devel-oped a statistical method they dubbedinductive game theory to analyze howthe monkeys reacted to othersrsquo fights Teresulting social circuit Flack says servesas a detailed model ldquoor how the micro-
scopic behavior maps to the unctionallyimportant macroscopic eatures o socialstructurerdquo such as the distribution o fightsizes In other words to construct a socialor biological circuit is to understand how agroup builds and maintains stable predict-able inormation hierarchies
Te final step is to produce a simplifiedsocial circuit what the researchers call a
ldquocognitive effective theoryrdquo that accurate-ly predicts how groups behave Te aimis to extract the key sorts o interactions
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 11
responsible or power structures fight-sizedistributions or other macroscopic eaturesusing ldquowhat we know about individual orcomponent cognition to coarse grain orcompressrdquo social circuits Flack says Suchcompression is essential she says because
living things canrsquot base their decisions on what ever y other living thing is doing instead theyrsquore orced to pay attention to
just a ew patterns or details o whatrsquos goingon around them
Te key question here as collaboratorand Princeton University graduate studentEleanor Brush puts it is how little inor-mation individuals need to successullyoutsmart others
Accidental or inevitable
Answering questions like that one ndash or testingsome o the teamrsquos more abstract predic-tions ndash remains a central challenge Poonor example describes his studies o electioncycles and policy change as ldquotoy modelsrdquo ndashthey capture qualitative eatures o the datasuch as party switching but donrsquot stand upto more precise quantitative tests
Meanwhile Krakauer and others sayitrsquos not always clear how to test particularhypotheses such as the prediction that basismismatch leads to ever-increasing complexity
in living things ldquoWersquore still looking or somecompelling examplerdquo Ellison says ldquoPart othe issue is that one can ofen play the devilrsquosadvocate and call into question the examplerdquoone reason why his work to construct ormal
precise measures o complexity is so import-ant he says
New techniques or rapidly analyzinggenetic data Krakauer says might improvethe situation Combining those techniques
with laboratory-based ldquoexperimental evolutionrdquo
in which researchers study the effects o preciseenvironmental changes on small organismssuch as bacteria could help test some o theendeavorrsquos core ideas such as the reflection
principle or the role o phase transitionsAnother potential avenue is to use ldquodigital
sources like computer games where we cancontrol to a large extent the orm o the dataor the conditions under which they werecollectedrdquo Krakauer says
esting their theories is just one part o theteamrsquos ambitious aims Tey hope Flack says
People gather in Washington DC before Martin Luther King Jrrsquos ldquoI Have a Dreamrdquo speech on Aug 28 1963 to demand equalities
Individuals tend to move faster and be less predictable than the slow-moving institutions they create
to achieve nothing less than an understandingo why lie is organized the way it is rom thesmallest bacteria to the largest human insti-tutions Tat requires combining real-worldobservation and abstract mathematical theoryin novel and creative ways
And as i that wasnrsquot enough Krakauerhas one more question in mind is lie anaccident or is it inevitable And i lie isinevitable well are we alone
ldquoI itrsquos not a product o a series o random
accidents but therersquos an underlying law-likeregularity that would give us confidence inbelieving in the possibility o lie presenteverywhere in the universerdquo he says ldquoSo
when one asks why does it matter whetheritrsquos chance or necessity it matters i we care
whether wersquore alone are notrdquo
Nathan Collins is a feelance science writernew ather and film aficionado based inSan Francisco
ldquoLiving things canrsquot base their decisions on what
every other living thing is doing instead theyrsquore
forced to pay attention to just a few patternsrdquo
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12 Santa Fe Institute Bulletin Vol 28
ldquohellipin mere Time all things follow
one another and in mere Space
all things are side by side it is
accordingly only by the combi-
nation of Time and Space thatthe representation of coexistence
arisesrdquo
mdash ARTHUR SCHOPENHAUER On the Fourfold Root
of the Principle of Sufficient Reason 1813
Yves Tanguy Indefinite Divisibility 1942
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 13
D I A C O M M O N S I N S E T H E N R I R O U S S E A U T H E
D R E A M 1 9 1 0 W I K I M E D I A C O M M O N S
Biological systems ndash rom cells totissues to individuals to societ-ies ndash are hierarchically organized
(eg Feldman amp Eschel 1982 Buss 1987Smith amp Szathmaacutery 1998 Valentineamp May 1996 Michod 2000 Frank2003) o many hierarchical organiza-tion suggests the nesting o components orindividuals into groups with these groupsaggregating into yet larger groups But this view ndash at least superficially ndash privilegesspace and matter over time and inorma-tion Many types o neural coding orexample require averaging or summing over neural firing ratesTe neuronsrsquo spatial location ndash that they are in proximity ndash iso course important but at least as important to the encoding
The explanation for the complex multi-scale structure of biological and social systems
lies in their manipulation of space and time to reduce uncertainty about the future
is their behavior in time Likewise insome monkey societies as I will discussin detail later in this review individualsestimate the uture cost o social inter-action by encoding the average outcomeo past interactions in special signals and
then summing over these signalsIn both examples inormation rom
events distributed in time as well as space(Figure 1) is captured with encodingsthat are used to control some behavioraloutput My collaborators and I in theCenter or Complexity amp Collective
Computation are exploring the idea that hierarchical organiza-tion at its core is a nesting o these kinds o unctional encodingsAs I will explain we think these unctional encodings result
LIFErsquoS INFORMATION HIERARCHY
BY JESSICA C FLACK
CO-DIRECTOR CENTER FOR COMPLEXITY amp COLLECTIVE COMPUTATION
WISCONSIN INSTITUTE FOR DISCOVERY UNIVERSITY OF WISCONSIN-MADISON
EXTERNAL PROFESSOR SANTA FE INSTITUTE
Jessica Flack
Number of spatial dimensions
N u m b e
r o f t i m e d i m e n s i o n s
0
0
1
2
3
4
5
1 2 3 4 5
UNSTABLE
TOO SIMPLE
UNPREDICTABLE (ELLIPTIC)
UNPREDICTABLE(ULTRAHYPERBOLIC)
TACHYONSONLY
UNSTABLE
Figure 1 The dimensionality of the time-space
continuum with properties postulated when x
does not equal 3 and y is larger than 1 Life on
earth exist s in three spatial dimensions and one
temporal dimension Biological systems effec-
tively ldquodiscretizerdquo time and space to reduce
environmental uncertainty by coarse-graining
and compressing environmental time series to
find regularities Components use the coarse-
grained descriptions to predict the future
tuning their behavior to their predictions
8122019 How Life Got Complex
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14 Santa Fe Institute Bulletin Vol 28
rom biological systemsmanipulating space andtime (Figure 2) to acilitateinormation extraction which in turn acilitatesmore efficient extraction
o energy h i s inormation
hierarchy appears to bea universal property obiological systems andmay be the key to one oliersquos greatest mysteries ndashthe origins o biologicalcomplexity In this essayI review a body o work byDavid Krakauer myseland our research group
that has been inspiredby many years o work atthe Santa Fe Institute (eg Crutchfield 1994 Gell-Mann1996 Gell-Mann amp Lloyd 1996 Fontana amp Buss 1996 West Brown amp Enquist 1997 Fontana amp Schuster 1998Ancel amp Fontana 2000 Stadler Stadler Wagner amp Fontana2001 Smith 2003 Crutchfield amp Goumlrnerup 2006 Smith2008) Our work suggests that complexity and the multi-scalestructure o biological systems are the predictable outcomeo evolutionary dynamics driven by uncertainty minimiza-tion (Krakauer 2011 Flack 2012 Flack Erwin Elliot ampKrakauer 2013)
Tis recasting o the evolutionary process as an inerentialone1 (Bergstrom amp Rosvall 2009 Krakauer 2011) is based onthe premise that organisms and other biological systems canbe viewed as hypotheses about the present and uture environ-ments they or their offspring will encounter induced rom thehistory o past environmental states they or their ancestors have
experienced (eg Crutchfield amp Feldman 2001 Krakauer ampZannotto 2009 Ellison Flack amp Krakauer in prep) Tis premise o course only holds i the past is prologue ndash that ishas regularities and the regularities can be estimated and evenmanipulated (as in niche construction) by biological systems ortheir components to produce adaptive behavior (Flack ErwinElliot amp Krakauer 2013 Ellison Flack amp Krakauer in prep)
I these premises are correct lie at its core is computa-tional and a central question becomes How do systems andtheir components estimate and control the regularity in theirenvironments and use these estimates to tune their strategiesI suggest that the answer to this question and the explanation
or complexity is that biological systems manipulate spatialand temporal structure to produce order ndash low variance ndash atlocal scales
UNCERTAINTY REDUCTION
Te story I want to tell starts with the observation that witheach new level o organization typically comes new unction-ality ndash a new eature with positive payoff consequences or thesystem as a whole or or its components (Flack Erwin Elliot
1 Tis idea is related to work on Maxwel lrsquos Demon (eg Krakauer2011 Mandal 983121uan amp Jarzynski 2013) and the Carnot cycle ( egSmith 2003) but we do not yet understand the mapping
Figure 2 Biological systems ndash from
(left to right ) Volvox colonies to slime
molds to animal societies to large-scale
ecosystems such as reefs t o human
cities ndash are hierarchically organized with
multiple functionally important time and
space scales All feature 1) components
with only partially aligned interests
exhibiting coherent behavior at the
aggregate level 2) components that turn
over and that co-exist in the sys tem at
varying stages of development
3) social structure that persists but
component behavior that fluctuates and
4) macroscopic variation in temporal
and spatial structure and coupling with
microscopic behavior which has func-
tional impl ications when the components
can perceive ndash in evolutionary develop-
mental or ecological time ndash regularities
at the macroscopic scale
1 2
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April 2014 Santa Fe Institute Bulletin 15
amp Krakauer 2013) Policing in a pigtailed macaque group is anexample Once a heavy tailed distribution o power ndash defined asthe degree o consensus in the group that an individual can winfights (see Flack amp Krakauer 2006 Boehm amp Flack 2010 BrushKrakauer amp Flack2013) ndash becomes effec-
tively institutionalized(here meaning hard tochange) policing (anintrinsically costlystrategy) becomesaffordable at least tothose animals that sitin the tail o the powerdistribution thosesuper powerul monkeys who are rarely or never challenged when they break up fights (Flack de Waal amp Krakauer 2005Flack Girvan de Waal amp Krakauer 2006)
My collaborators and I propose that a primary driver o theemergence o new unctionality such as policing is the reduc-tion o environmental uncertainty through the constructiono nested dynamical processes with a range o characteris-tic time constants (Flack Erwin Elliot amp Krakauer 2013)Tese nested dynamical processes arise as components extractregularities rom ast microscopic behavior by coarse-grain-ing (or compressing) the history o events to which they havebeen exposed
Proteins or example can have a long hal-lie relative toRNA transcripts and can be thought o as the summed outputo translation Cells have a long hal-lie relative to proteins
and are a unction o the summed output o arrays o spatiallystructured proteins Both proteins and cells represent someaverage measure o the noisier activity o their constituentsSimilarly a pigtailed macaquersquos estimate o its power is a kind
o average measure othe collective percep-
tion in the groupthat the macaque iscapable o winningights and this is abetter predictor othe cost the macaque will pay during fightsthan the outcome oany single melee as
these outcomes can fluctuate or contextual reasons Tesecoarse-grainings or averages are encoded as slow variables(Flack amp de Waal 2007 Flack 2012 Flack Erwin Elliot
amp Krakauer 2013 see also Feret Danos Krivine Harner ampFontana 2009 or a similar idea) Slow variables may have aspatial component as well as a temporal component as in the protein and cell examples (Figure 6) or minimally only atemporal component as in the monkey example
As a consequence o integrating over abundant microscopic processes slow variables provide better predictors o the localuture configuration o a system than the states o the fluctuatingmicroscopic components In doing so they promote acceleratedrates o microscopic adaptation Slow variables acilitate adapta-tion in two ways Tey allow components to fine-tune theirbehavior and they ree components to search at low cost a larger
3 4
Slow variables provide better predictors
of the local future configuration of a
system than the states of the fluctuating
microscopic componentsrdquo
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Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and
proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since
fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous
starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of
relevant genes and proteins and the output is the target phenot ypic feature
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April 2014 Santa Fe Institute Bulletin 17
space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
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A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
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vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
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22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
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24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 25
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operating budget comes from
private sources - forward-thinking
individuals corporations and
foundations If you believe
complex systems science can
generate the insights that matter
most for science and society makea gift today Visit our website at
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8122019 How Life Got Complex
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4 Santa Fe Institute Bulletin Vol 28
R ESEARCH on the origins development and dynam-ics o complexity in biological systems has been a coretopic o inquiry at the Santa Fe Institute since its
ounding 30 years ago in 1984 SFIrsquos scientists have workedto develop an understanding o a dizzying array o biological
phenomena rom the origins o lie to transitions rom single-to multi-cellularity to evolutionary innovation at different
levels o biological organization to the relationship betweenecological complexity and dynamical stabilityTe intertwined concepts o energy and inormation are
undamental to any understanding o biological complexityBiological systems are ar rom equilibrium requiring a constantflow o energy to maintain their organization and unction-ality Te processing and encoding o inormation providesa means or lie to manage and maintain energy acquisitionuse and dissipation
Te work o ormer resident proessors and current externalaculty members Jessica Flack and David Krakauer (and their manycolleagues) highlighted in this issue o the SFI Bulletin addressesthe intimate dance between energy and inormation processing inbiological systems ndash a dance they suggest gives rise to the complexmulti-scale structure we observe
Tis computational-thermodynamic view o biology provides
a powerul potentially generic ramework or understanding the properties o any kind o complex adaptive system including socio-economic systems
Sincerely
Jennier Dunne Chair o the Faculty Santa Fe Institute
Biological Systems Energy and Information
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 5
IMAGE CREDITS THIS PAGE FROM TOP BRAIN DRAWING HESSDESIGNWORKSCOM EARTH FROM SPACE NASA ROUSSEAU THE DREAM WIKIMEDIA COMMONSFACING PAGE PORTRAIT OF JENNIFER DUNNE MINESH BACRANIA PHOTOGRAPHY
SFI BULLETIN
VOL 28 APRIL 2014Contents
0 Perspectives The Complexity of Life
By DAVID KRAKAUER
4 Biological Systems Energy and Information
By JENNIFER DUNNE
6 Why Nature Went to the Trouble of CreatingYouBy NATHAN COLLINS
12 Lifersquos Information Hierarchy
By JESSICA FLACK
ldquo There is nothing that living things do that cannot be
understood from the point of view that they are made
of atoms acting according to the laws of physicsrdquo ndash R I C H A R D F E Y N M A N
12
6
0
8122019 How Life Got Complex
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8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 7April 2014 Santa Fe Institute Bulletin 7
Y pestis the bacterium that causes bubonic
plague (aka The Black Death of the Middle Ages)
Lie on earth began some 35 billion
years ago not all that long afer the planet itsel first ormed and or 15
billion years it chugged along single-celledcreatures sel-replicating dividing diversi-ying Remarkably it took that long ndash 7500times longer than all o human history ndash orthe first multicellular lie to emerge and stilllonger or it to evolve into lie as we know it
Despite that the question manyresearchers ask isnrsquot what took so long
but rather why complex lie would haveevolved in the first place Consider thissingle-celled organisms make up more thanhal o the biomass on earth and even oneo the tiniest organisms ndash Y pestis betterknown as bubonic plague ndash can effortlesslythoughtlessly kill you
Nature it seems doesnrsquot need youIndeed there isnrsquot any obvious reasonit would go to the trouble o creatingsomething as complex as a human beingcomplete with its differentiated organs and
top-down control systems And yet despiteour billion years o Naturersquos great ldquomehrdquohere you are alive multicellular complex
ndash even intelligent enough to ponder yourown existence
What was Nature thinking Accordingto one argument bacteria first boundtogether in colonies that enhanced cooper-ation and hence survival Eventually thosebacteria bound together physically as wellcreating the first multicellular lie Andso on
ldquoYou could say thatrsquos an answer but then you could go a bit urther and ask what isit exactly that makes you a better compet-
itorrdquo says David Krakauer who with SFIExternal Proessor Jessica Flack co-directs
the Wisconsin Institute or DiscoveryrsquosCenter or Complexity and CollectiveComputation or C4 and SFIrsquos Johnempleton Foundation-unded ldquoEvolutiono Complexity and Intelligence on Earthrdquoresearch project
ldquoWell yoursquore outsmarting everyone elserdquoKrakauer says
Simple versus complex
Despite its seeming indifference Naturedoes seem to have thought highly enough
Why Nature Went to the Trouble
of CreatinghellipYou BY NATHAN COLLINS
In a complex world where plants and animals and everything else are duking
it out to survive an organism stands to gain from becoming more complex
o complex structures to produce a ew othem and to have ratcheted up the complex-ity urther by embedding complex structures
within complex structures ndash animals withhearts and lungs and circulatory systems orgroups o people capable o building theirown social institutions
But why What purpose does it serveldquoWhy lie is hierarchically organized isnot at all obviousrdquo Flack says and howan organismrsquos or a societyrsquos complexity
relates to the complexity o its environ-ment remains unclear
Our anthropomorphized Nature mighthave started with one very simple idea
what Krakauer calls the reflection princi- ple which presupposes that living thingscanrsquot be more complex than their environ-ments an idea rooted in experiments ldquoI
you take organisms and you place themin simpler environments they just throweverything [superfluous] away Tey losegenesrdquo Krakauer says
At the same time the world does seem toavor an intelligent creature Even the tiniestliving things need to be able to comprehend predict and react to their environmentsthatrsquos what allows them to outsmart eachother he says In a complex world where
plants and animals and everything else areduking it out to survive an organism standsto gain rom becoming more complex
Tat tension between simplicity andcomplexity is the starting point or C4
postdoctoral ellow Christopher Ellison
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8 Santa Fe Institute Bulletin Vol 28
Watch David Krakauerrsquos interview at
wwwsantafeedulife
What does Nature care about hearts brains and other organs or for that
matter political parties ndash in other words structures within structures
ldquoWersquod like to understand the implica-tions this has or the environmentrdquo hesays ldquoFor example do simple or complexorganisms experience and live in simple orcomplex environmentsrdquo
Working with Flack and KrakauerEllison developed ldquoMarkov organismsrdquo
computer-simulated creatures that merge
insights rom biology with inormation processing techniques rom computerscience to help figure out how lie balancesthese trade-offs Rather than modeling real
organisms themselves he ocuses on howinormation flows in the ecological system
Itrsquos early days Ellison says but his simula-tions suggest that lie will ofen evolve tomatch its environmentrsquos complexity ndash findingsthat are in line with the reflection principlebut with some interesting caveats For one
thing evolving Markov organisms tend toovershoot their worldsrsquocomplexity and mighttake a long time to pruneunnecessary complexities
Teyrsquore also suscepti-ble to ldquobasis mismatchrdquo a
problem you know welli yoursquove ever tried to explain to a tourist howto get around in your hometown o youthere are just a ew steps but to the noviceitrsquos a complex process with many twists and
turns and every intersection represents a possible misdirection in sprawling cities likeLos Angeles a direction as simple as ldquotakeSunset to Vermont and turn lefrdquo becomesinfinitely complex Markov organisms arethe same i their way o solving a problem
doesnrsquot line up with how their environmentsconstructed it Ellisonrsquos simulations showMarkov organismsrsquo complexity keeps evolv-ing upward orever
But Ellisonrsquos inormation-centricapproach has some benefits One he says
ldquois that it attempts to answer the questiono how complexity evolves in an organ-ism-independent ashionrdquo meaning thatthe ideas apply equally well to anythingrom bacteria to politics Similarly Ellisonrsquosmethod allows the team to describe both anorganism and its environmentrsquos complexity
in the same terms because both derive romthe same underlying models o inormation
processing Surprisingly thatrsquos somethingew i any other researchers have done
Constructing predictability
So it appears Nature might avor multicel-lular lie i it affords a certain computational power not readily available to single-celledorganisms But what does Nature care abouthearts brains and other organs or or thatmatter political parties ndash in other words
structures within structureshe answer Flack says is that living
things like their worlds to be predictableand what makes cells and people more likelyto survive Nature avors
Much o the structure we observe in the world Flack says probably evolved becausestructure begets stability hence predictabilityGroups o genes cells or animals changetheir collective behaviors slowly compared
with individual genes cells or animals givingthe aster-moving individual components a
chance to anticipate changes more easilyBiologists call the idea that plants andanimals ndash and genes and organs and so on
ndash structure their environments to be morestable and predictable ldquoniche constructionrdquoand it usually applies to physical structurelike ants building nests But it also can beapplied to temporal structure
Politics offers perhaps a simple exampleEarly US politicians were explicit aboutdesigning Congress and the rest o govern-ment so that it would change gradually and
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 9April 2014 Santa Fe Institute Bulletin 9
According to the reflection principle organisms are reflections of their environments Here the environment is represented as a prose excerpt from
Darwinrsquos Origin of Species wherein he contemplates an entangled bank filled with ldquoendless forms most beautiful and most wonderfulrdquo each evolved through natural selection Organism A matches the environmentrsquos complexity while Organism B is less complex than the environment and Organism C is
more complex than the environment The research team is asking when evolution satisfies or v iolates the reflection principle
be more stable Even when we complainabout our slow-moving government weare undoubtedly comorted by that verycharacteristic because slow is predictableAnd when our environment is predictable we know how to make sound decisions
So people construct relatively slow-mov-ing predictable institutions At the sametime those institutions help shape thebehavior o the individuals who createdthem points out C4 postdoctoral ellowPhilip Poon who is examining eedbackrom institutions to explain (in the govern-ment case) why or example Democrats andRepublicans seem to trade off controllingthe White House and Congress every ewterms A critical issue is o course howthat eedback works rom a mechanical
perspective ndash that is how the ways individ-uals perceive and understand institutionsinfluence their decision-making
Drawing on the theory o phase transi-tions the same one that explains whychanging a seemingly minor variable cansuddenly shif an entire system rom onestate to another Flack Krakauer and C4researcher and ormer SFI PostdoctoralFellow Bryan Daniels argue that hierarchi-cal structures bestow another advantageeicient inormation low rom the
collective to its individual parts Systems perched on the edge o a phase transitionare exquisitely sensitive so that a small orlocalized change ldquoleads to a large change inthe global dynamicsrdquo Daniels says Toughthat might seem unstable or chaotic systems
near the critical point where a phase transi-
tion begins are actually quite predictableGroups o macaque monkeys ndash one o
Flack and the teamrsquos earliest sources o data
and inspiration ndash are one system that appearsto be resting near a phase transition Whenmonkeys arenrsquot eeling especially aggres-sive Daniels says things are stable and ldquoi Isuddenly act out nothingrsquos going to happenhellipbut i [the group is] sitting at the critical
point then [my] contribution is always moreimportantrdquo and one extra monkey picking afight is enough to kick off a large-scale brawl
Below the critical point individualmonkeys act airly independently but rightat the transition their behavior is tightly
ldquoWhen monkeys arenrsquot feeling especially aggressive
things are stable but if the group is sitting at the
critical point one extra monkey picking a fight
is enough to kick off a large-scale brawlrdquo
coordinated and individual monkeys acttogether as one Tat Krakauer empha-sizes eases the flow o inormation romthe system as a whole to its constituent
parts making it all the more predictableNature is rie with examples one birdrsquos
sudden course correction changing the
direction o an entire flock alternatingDemocrat and Republican control oCongress and so on
Social circuitry
In the abstract Nature has good reasonto avor complexity and hierarchy ndash eachin its own way makes the tasks o compre-hension prediction and strategizingeasier But its a third concept circuitsthat grounds Flack and Krakauerrsquos teamand lays the practical oundation ormuch o their work
Usually the word ldquocircuitrdquo conjuresimages o the transistors microprocessors
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10 Santa Fe Institute Bulletin Vol 28
Time series representation of the dis tribution of fight sizes in a macaque society Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once An outburst by an individual wonrsquot normally tip the scales But when the system is perched at
a phase transition a brawl among many individuals can erupt
and lengths o copper wire that make upa computer Te analogy is apt Like anelectronic circuit individual components
ndash genes organs people ndash inormational-ly bind living things to orm a kind obiological or social computer In act the
circuit approach to describing a systemstems rom a hunch that the hierarchicalscales present throughout nature ldquoarise
Time
F i g h
t 1
F i g h
t 2
Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once
Vd
Pa
Je
Cb
Yv
Ta
Sg
Qs
Po
Mv
Ip
Fp
Eo
Dh
Tr
Pr
Fc
Zu
Vf
Fp
Ec
Zu
Zr
Iw
Is
Fp
Fc
Cc
Yv
Cd
Ta
Cc
Qv
Ip
Cb
Pa
Eb
Sb
Jc
Je
Hp
Lr
Jc
Zu
Po
Dh
Vf
Eo
Eo
Cd
Pa
Dh
Hh
At
Zu
Yv
Th
Ta
Fn
through a process o collective computa-tionrdquo creating slow-changing predictablesocial and biological structures Flack says
Circuits are more than just an analo-gy though ndash theyrsquore the tools that bridgethe gap between individual and collective
behavior And in a scientific field whereitrsquos easy to avoid real data circuits are one
way Flack and Krakauerrsquos research group
makes sure their theories are a good matchto the real world ldquoOur group is committedto an empirical approachrdquo Flack says ldquoWebelieve that only when these measures aredeveloped with an understanding o thedata generated by real systems will they
be useulrdquoTe process o building circuits begins
by analyzing how a systemrsquos individual parts work together Using the macaquefight data Flack Krakauer and ormerSFI Omidyar Fellow Simon DeDeo (nowat Indiana University Bloomington) devel-oped a statistical method they dubbedinductive game theory to analyze howthe monkeys reacted to othersrsquo fights Teresulting social circuit Flack says servesas a detailed model ldquoor how the micro-
scopic behavior maps to the unctionallyimportant macroscopic eatures o socialstructurerdquo such as the distribution o fightsizes In other words to construct a socialor biological circuit is to understand how agroup builds and maintains stable predict-able inormation hierarchies
Te final step is to produce a simplifiedsocial circuit what the researchers call a
ldquocognitive effective theoryrdquo that accurate-ly predicts how groups behave Te aimis to extract the key sorts o interactions
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 1328
April 2014 Santa Fe Institute Bulletin 11
responsible or power structures fight-sizedistributions or other macroscopic eaturesusing ldquowhat we know about individual orcomponent cognition to coarse grain orcompressrdquo social circuits Flack says Suchcompression is essential she says because
living things canrsquot base their decisions on what ever y other living thing is doing instead theyrsquore orced to pay attention to
just a ew patterns or details o whatrsquos goingon around them
Te key question here as collaboratorand Princeton University graduate studentEleanor Brush puts it is how little inor-mation individuals need to successullyoutsmart others
Accidental or inevitable
Answering questions like that one ndash or testingsome o the teamrsquos more abstract predic-tions ndash remains a central challenge Poonor example describes his studies o electioncycles and policy change as ldquotoy modelsrdquo ndashthey capture qualitative eatures o the datasuch as party switching but donrsquot stand upto more precise quantitative tests
Meanwhile Krakauer and others sayitrsquos not always clear how to test particularhypotheses such as the prediction that basismismatch leads to ever-increasing complexity
in living things ldquoWersquore still looking or somecompelling examplerdquo Ellison says ldquoPart othe issue is that one can ofen play the devilrsquosadvocate and call into question the examplerdquoone reason why his work to construct ormal
precise measures o complexity is so import-ant he says
New techniques or rapidly analyzinggenetic data Krakauer says might improvethe situation Combining those techniques
with laboratory-based ldquoexperimental evolutionrdquo
in which researchers study the effects o preciseenvironmental changes on small organismssuch as bacteria could help test some o theendeavorrsquos core ideas such as the reflection
principle or the role o phase transitionsAnother potential avenue is to use ldquodigital
sources like computer games where we cancontrol to a large extent the orm o the dataor the conditions under which they werecollectedrdquo Krakauer says
esting their theories is just one part o theteamrsquos ambitious aims Tey hope Flack says
People gather in Washington DC before Martin Luther King Jrrsquos ldquoI Have a Dreamrdquo speech on Aug 28 1963 to demand equalities
Individuals tend to move faster and be less predictable than the slow-moving institutions they create
to achieve nothing less than an understandingo why lie is organized the way it is rom thesmallest bacteria to the largest human insti-tutions Tat requires combining real-worldobservation and abstract mathematical theoryin novel and creative ways
And as i that wasnrsquot enough Krakauerhas one more question in mind is lie anaccident or is it inevitable And i lie isinevitable well are we alone
ldquoI itrsquos not a product o a series o random
accidents but therersquos an underlying law-likeregularity that would give us confidence inbelieving in the possibility o lie presenteverywhere in the universerdquo he says ldquoSo
when one asks why does it matter whetheritrsquos chance or necessity it matters i we care
whether wersquore alone are notrdquo
Nathan Collins is a feelance science writernew ather and film aficionado based inSan Francisco
ldquoLiving things canrsquot base their decisions on what
every other living thing is doing instead theyrsquore
forced to pay attention to just a few patternsrdquo
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12 Santa Fe Institute Bulletin Vol 28
ldquohellipin mere Time all things follow
one another and in mere Space
all things are side by side it is
accordingly only by the combi-
nation of Time and Space thatthe representation of coexistence
arisesrdquo
mdash ARTHUR SCHOPENHAUER On the Fourfold Root
of the Principle of Sufficient Reason 1813
Yves Tanguy Indefinite Divisibility 1942
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 13
D I A C O M M O N S I N S E T H E N R I R O U S S E A U T H E
D R E A M 1 9 1 0 W I K I M E D I A C O M M O N S
Biological systems ndash rom cells totissues to individuals to societ-ies ndash are hierarchically organized
(eg Feldman amp Eschel 1982 Buss 1987Smith amp Szathmaacutery 1998 Valentineamp May 1996 Michod 2000 Frank2003) o many hierarchical organiza-tion suggests the nesting o components orindividuals into groups with these groupsaggregating into yet larger groups But this view ndash at least superficially ndash privilegesspace and matter over time and inorma-tion Many types o neural coding orexample require averaging or summing over neural firing ratesTe neuronsrsquo spatial location ndash that they are in proximity ndash iso course important but at least as important to the encoding
The explanation for the complex multi-scale structure of biological and social systems
lies in their manipulation of space and time to reduce uncertainty about the future
is their behavior in time Likewise insome monkey societies as I will discussin detail later in this review individualsestimate the uture cost o social inter-action by encoding the average outcomeo past interactions in special signals and
then summing over these signalsIn both examples inormation rom
events distributed in time as well as space(Figure 1) is captured with encodingsthat are used to control some behavioraloutput My collaborators and I in theCenter or Complexity amp Collective
Computation are exploring the idea that hierarchical organiza-tion at its core is a nesting o these kinds o unctional encodingsAs I will explain we think these unctional encodings result
LIFErsquoS INFORMATION HIERARCHY
BY JESSICA C FLACK
CO-DIRECTOR CENTER FOR COMPLEXITY amp COLLECTIVE COMPUTATION
WISCONSIN INSTITUTE FOR DISCOVERY UNIVERSITY OF WISCONSIN-MADISON
EXTERNAL PROFESSOR SANTA FE INSTITUTE
Jessica Flack
Number of spatial dimensions
N u m b e
r o f t i m e d i m e n s i o n s
0
0
1
2
3
4
5
1 2 3 4 5
UNSTABLE
TOO SIMPLE
UNPREDICTABLE (ELLIPTIC)
UNPREDICTABLE(ULTRAHYPERBOLIC)
TACHYONSONLY
UNSTABLE
Figure 1 The dimensionality of the time-space
continuum with properties postulated when x
does not equal 3 and y is larger than 1 Life on
earth exist s in three spatial dimensions and one
temporal dimension Biological systems effec-
tively ldquodiscretizerdquo time and space to reduce
environmental uncertainty by coarse-graining
and compressing environmental time series to
find regularities Components use the coarse-
grained descriptions to predict the future
tuning their behavior to their predictions
8122019 How Life Got Complex
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14 Santa Fe Institute Bulletin Vol 28
rom biological systemsmanipulating space andtime (Figure 2) to acilitateinormation extraction which in turn acilitatesmore efficient extraction
o energy h i s inormation
hierarchy appears to bea universal property obiological systems andmay be the key to one oliersquos greatest mysteries ndashthe origins o biologicalcomplexity In this essayI review a body o work byDavid Krakauer myseland our research group
that has been inspiredby many years o work atthe Santa Fe Institute (eg Crutchfield 1994 Gell-Mann1996 Gell-Mann amp Lloyd 1996 Fontana amp Buss 1996 West Brown amp Enquist 1997 Fontana amp Schuster 1998Ancel amp Fontana 2000 Stadler Stadler Wagner amp Fontana2001 Smith 2003 Crutchfield amp Goumlrnerup 2006 Smith2008) Our work suggests that complexity and the multi-scalestructure o biological systems are the predictable outcomeo evolutionary dynamics driven by uncertainty minimiza-tion (Krakauer 2011 Flack 2012 Flack Erwin Elliot ampKrakauer 2013)
Tis recasting o the evolutionary process as an inerentialone1 (Bergstrom amp Rosvall 2009 Krakauer 2011) is based onthe premise that organisms and other biological systems canbe viewed as hypotheses about the present and uture environ-ments they or their offspring will encounter induced rom thehistory o past environmental states they or their ancestors have
experienced (eg Crutchfield amp Feldman 2001 Krakauer ampZannotto 2009 Ellison Flack amp Krakauer in prep) Tis premise o course only holds i the past is prologue ndash that ishas regularities and the regularities can be estimated and evenmanipulated (as in niche construction) by biological systems ortheir components to produce adaptive behavior (Flack ErwinElliot amp Krakauer 2013 Ellison Flack amp Krakauer in prep)
I these premises are correct lie at its core is computa-tional and a central question becomes How do systems andtheir components estimate and control the regularity in theirenvironments and use these estimates to tune their strategiesI suggest that the answer to this question and the explanation
or complexity is that biological systems manipulate spatialand temporal structure to produce order ndash low variance ndash atlocal scales
UNCERTAINTY REDUCTION
Te story I want to tell starts with the observation that witheach new level o organization typically comes new unction-ality ndash a new eature with positive payoff consequences or thesystem as a whole or or its components (Flack Erwin Elliot
1 Tis idea is related to work on Maxwel lrsquos Demon (eg Krakauer2011 Mandal 983121uan amp Jarzynski 2013) and the Carnot cycle ( egSmith 2003) but we do not yet understand the mapping
Figure 2 Biological systems ndash from
(left to right ) Volvox colonies to slime
molds to animal societies to large-scale
ecosystems such as reefs t o human
cities ndash are hierarchically organized with
multiple functionally important time and
space scales All feature 1) components
with only partially aligned interests
exhibiting coherent behavior at the
aggregate level 2) components that turn
over and that co-exist in the sys tem at
varying stages of development
3) social structure that persists but
component behavior that fluctuates and
4) macroscopic variation in temporal
and spatial structure and coupling with
microscopic behavior which has func-
tional impl ications when the components
can perceive ndash in evolutionary develop-
mental or ecological time ndash regularities
at the macroscopic scale
1 2
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April 2014 Santa Fe Institute Bulletin 15
amp Krakauer 2013) Policing in a pigtailed macaque group is anexample Once a heavy tailed distribution o power ndash defined asthe degree o consensus in the group that an individual can winfights (see Flack amp Krakauer 2006 Boehm amp Flack 2010 BrushKrakauer amp Flack2013) ndash becomes effec-
tively institutionalized(here meaning hard tochange) policing (anintrinsically costlystrategy) becomesaffordable at least tothose animals that sitin the tail o the powerdistribution thosesuper powerul monkeys who are rarely or never challenged when they break up fights (Flack de Waal amp Krakauer 2005Flack Girvan de Waal amp Krakauer 2006)
My collaborators and I propose that a primary driver o theemergence o new unctionality such as policing is the reduc-tion o environmental uncertainty through the constructiono nested dynamical processes with a range o characteris-tic time constants (Flack Erwin Elliot amp Krakauer 2013)Tese nested dynamical processes arise as components extractregularities rom ast microscopic behavior by coarse-grain-ing (or compressing) the history o events to which they havebeen exposed
Proteins or example can have a long hal-lie relative toRNA transcripts and can be thought o as the summed outputo translation Cells have a long hal-lie relative to proteins
and are a unction o the summed output o arrays o spatiallystructured proteins Both proteins and cells represent someaverage measure o the noisier activity o their constituentsSimilarly a pigtailed macaquersquos estimate o its power is a kind
o average measure othe collective percep-
tion in the groupthat the macaque iscapable o winningights and this is abetter predictor othe cost the macaque will pay during fightsthan the outcome oany single melee as
these outcomes can fluctuate or contextual reasons Tesecoarse-grainings or averages are encoded as slow variables(Flack amp de Waal 2007 Flack 2012 Flack Erwin Elliot
amp Krakauer 2013 see also Feret Danos Krivine Harner ampFontana 2009 or a similar idea) Slow variables may have aspatial component as well as a temporal component as in the protein and cell examples (Figure 6) or minimally only atemporal component as in the monkey example
As a consequence o integrating over abundant microscopic processes slow variables provide better predictors o the localuture configuration o a system than the states o the fluctuatingmicroscopic components In doing so they promote acceleratedrates o microscopic adaptation Slow variables acilitate adapta-tion in two ways Tey allow components to fine-tune theirbehavior and they ree components to search at low cost a larger
3 4
Slow variables provide better predictors
of the local future configuration of a
system than the states of the fluctuating
microscopic componentsrdquo
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Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and
proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since
fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous
starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of
relevant genes and proteins and the output is the target phenot ypic feature
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April 2014 Santa Fe Institute Bulletin 17
space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
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April 2014 Santa Fe Institute Bulletin 19
A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
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April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
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22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 25
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individuals corporations and
foundations If you believe
complex systems science can
generate the insights that matter
most for science and society makea gift today Visit our website at
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April 2014 Santa Fe Institute Bulletin 5
IMAGE CREDITS THIS PAGE FROM TOP BRAIN DRAWING HESSDESIGNWORKSCOM EARTH FROM SPACE NASA ROUSSEAU THE DREAM WIKIMEDIA COMMONSFACING PAGE PORTRAIT OF JENNIFER DUNNE MINESH BACRANIA PHOTOGRAPHY
SFI BULLETIN
VOL 28 APRIL 2014Contents
0 Perspectives The Complexity of Life
By DAVID KRAKAUER
4 Biological Systems Energy and Information
By JENNIFER DUNNE
6 Why Nature Went to the Trouble of CreatingYouBy NATHAN COLLINS
12 Lifersquos Information Hierarchy
By JESSICA FLACK
ldquo There is nothing that living things do that cannot be
understood from the point of view that they are made
of atoms acting according to the laws of physicsrdquo ndash R I C H A R D F E Y N M A N
12
6
0
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8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 7April 2014 Santa Fe Institute Bulletin 7
Y pestis the bacterium that causes bubonic
plague (aka The Black Death of the Middle Ages)
Lie on earth began some 35 billion
years ago not all that long afer the planet itsel first ormed and or 15
billion years it chugged along single-celledcreatures sel-replicating dividing diversi-ying Remarkably it took that long ndash 7500times longer than all o human history ndash orthe first multicellular lie to emerge and stilllonger or it to evolve into lie as we know it
Despite that the question manyresearchers ask isnrsquot what took so long
but rather why complex lie would haveevolved in the first place Consider thissingle-celled organisms make up more thanhal o the biomass on earth and even oneo the tiniest organisms ndash Y pestis betterknown as bubonic plague ndash can effortlesslythoughtlessly kill you
Nature it seems doesnrsquot need youIndeed there isnrsquot any obvious reasonit would go to the trouble o creatingsomething as complex as a human beingcomplete with its differentiated organs and
top-down control systems And yet despiteour billion years o Naturersquos great ldquomehrdquohere you are alive multicellular complex
ndash even intelligent enough to ponder yourown existence
What was Nature thinking Accordingto one argument bacteria first boundtogether in colonies that enhanced cooper-ation and hence survival Eventually thosebacteria bound together physically as wellcreating the first multicellular lie Andso on
ldquoYou could say thatrsquos an answer but then you could go a bit urther and ask what isit exactly that makes you a better compet-
itorrdquo says David Krakauer who with SFIExternal Proessor Jessica Flack co-directs
the Wisconsin Institute or DiscoveryrsquosCenter or Complexity and CollectiveComputation or C4 and SFIrsquos Johnempleton Foundation-unded ldquoEvolutiono Complexity and Intelligence on Earthrdquoresearch project
ldquoWell yoursquore outsmarting everyone elserdquoKrakauer says
Simple versus complex
Despite its seeming indifference Naturedoes seem to have thought highly enough
Why Nature Went to the Trouble
of CreatinghellipYou BY NATHAN COLLINS
In a complex world where plants and animals and everything else are duking
it out to survive an organism stands to gain from becoming more complex
o complex structures to produce a ew othem and to have ratcheted up the complex-ity urther by embedding complex structures
within complex structures ndash animals withhearts and lungs and circulatory systems orgroups o people capable o building theirown social institutions
But why What purpose does it serveldquoWhy lie is hierarchically organized isnot at all obviousrdquo Flack says and howan organismrsquos or a societyrsquos complexity
relates to the complexity o its environ-ment remains unclear
Our anthropomorphized Nature mighthave started with one very simple idea
what Krakauer calls the reflection princi- ple which presupposes that living thingscanrsquot be more complex than their environ-ments an idea rooted in experiments ldquoI
you take organisms and you place themin simpler environments they just throweverything [superfluous] away Tey losegenesrdquo Krakauer says
At the same time the world does seem toavor an intelligent creature Even the tiniestliving things need to be able to comprehend predict and react to their environmentsthatrsquos what allows them to outsmart eachother he says In a complex world where
plants and animals and everything else areduking it out to survive an organism standsto gain rom becoming more complex
Tat tension between simplicity andcomplexity is the starting point or C4
postdoctoral ellow Christopher Ellison
8122019 How Life Got Complex
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8 Santa Fe Institute Bulletin Vol 28
Watch David Krakauerrsquos interview at
wwwsantafeedulife
What does Nature care about hearts brains and other organs or for that
matter political parties ndash in other words structures within structures
ldquoWersquod like to understand the implica-tions this has or the environmentrdquo hesays ldquoFor example do simple or complexorganisms experience and live in simple orcomplex environmentsrdquo
Working with Flack and KrakauerEllison developed ldquoMarkov organismsrdquo
computer-simulated creatures that merge
insights rom biology with inormation processing techniques rom computerscience to help figure out how lie balancesthese trade-offs Rather than modeling real
organisms themselves he ocuses on howinormation flows in the ecological system
Itrsquos early days Ellison says but his simula-tions suggest that lie will ofen evolve tomatch its environmentrsquos complexity ndash findingsthat are in line with the reflection principlebut with some interesting caveats For one
thing evolving Markov organisms tend toovershoot their worldsrsquocomplexity and mighttake a long time to pruneunnecessary complexities
Teyrsquore also suscepti-ble to ldquobasis mismatchrdquo a
problem you know welli yoursquove ever tried to explain to a tourist howto get around in your hometown o youthere are just a ew steps but to the noviceitrsquos a complex process with many twists and
turns and every intersection represents a possible misdirection in sprawling cities likeLos Angeles a direction as simple as ldquotakeSunset to Vermont and turn lefrdquo becomesinfinitely complex Markov organisms arethe same i their way o solving a problem
doesnrsquot line up with how their environmentsconstructed it Ellisonrsquos simulations showMarkov organismsrsquo complexity keeps evolv-ing upward orever
But Ellisonrsquos inormation-centricapproach has some benefits One he says
ldquois that it attempts to answer the questiono how complexity evolves in an organ-ism-independent ashionrdquo meaning thatthe ideas apply equally well to anythingrom bacteria to politics Similarly Ellisonrsquosmethod allows the team to describe both anorganism and its environmentrsquos complexity
in the same terms because both derive romthe same underlying models o inormation
processing Surprisingly thatrsquos somethingew i any other researchers have done
Constructing predictability
So it appears Nature might avor multicel-lular lie i it affords a certain computational power not readily available to single-celledorganisms But what does Nature care abouthearts brains and other organs or or thatmatter political parties ndash in other words
structures within structureshe answer Flack says is that living
things like their worlds to be predictableand what makes cells and people more likelyto survive Nature avors
Much o the structure we observe in the world Flack says probably evolved becausestructure begets stability hence predictabilityGroups o genes cells or animals changetheir collective behaviors slowly compared
with individual genes cells or animals givingthe aster-moving individual components a
chance to anticipate changes more easilyBiologists call the idea that plants andanimals ndash and genes and organs and so on
ndash structure their environments to be morestable and predictable ldquoniche constructionrdquoand it usually applies to physical structurelike ants building nests But it also can beapplied to temporal structure
Politics offers perhaps a simple exampleEarly US politicians were explicit aboutdesigning Congress and the rest o govern-ment so that it would change gradually and
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April 2014 Santa Fe Institute Bulletin 9April 2014 Santa Fe Institute Bulletin 9
According to the reflection principle organisms are reflections of their environments Here the environment is represented as a prose excerpt from
Darwinrsquos Origin of Species wherein he contemplates an entangled bank filled with ldquoendless forms most beautiful and most wonderfulrdquo each evolved through natural selection Organism A matches the environmentrsquos complexity while Organism B is less complex than the environment and Organism C is
more complex than the environment The research team is asking when evolution satisfies or v iolates the reflection principle
be more stable Even when we complainabout our slow-moving government weare undoubtedly comorted by that verycharacteristic because slow is predictableAnd when our environment is predictable we know how to make sound decisions
So people construct relatively slow-mov-ing predictable institutions At the sametime those institutions help shape thebehavior o the individuals who createdthem points out C4 postdoctoral ellowPhilip Poon who is examining eedbackrom institutions to explain (in the govern-ment case) why or example Democrats andRepublicans seem to trade off controllingthe White House and Congress every ewterms A critical issue is o course howthat eedback works rom a mechanical
perspective ndash that is how the ways individ-uals perceive and understand institutionsinfluence their decision-making
Drawing on the theory o phase transi-tions the same one that explains whychanging a seemingly minor variable cansuddenly shif an entire system rom onestate to another Flack Krakauer and C4researcher and ormer SFI PostdoctoralFellow Bryan Daniels argue that hierarchi-cal structures bestow another advantageeicient inormation low rom the
collective to its individual parts Systems perched on the edge o a phase transitionare exquisitely sensitive so that a small orlocalized change ldquoleads to a large change inthe global dynamicsrdquo Daniels says Toughthat might seem unstable or chaotic systems
near the critical point where a phase transi-
tion begins are actually quite predictableGroups o macaque monkeys ndash one o
Flack and the teamrsquos earliest sources o data
and inspiration ndash are one system that appearsto be resting near a phase transition Whenmonkeys arenrsquot eeling especially aggres-sive Daniels says things are stable and ldquoi Isuddenly act out nothingrsquos going to happenhellipbut i [the group is] sitting at the critical
point then [my] contribution is always moreimportantrdquo and one extra monkey picking afight is enough to kick off a large-scale brawl
Below the critical point individualmonkeys act airly independently but rightat the transition their behavior is tightly
ldquoWhen monkeys arenrsquot feeling especially aggressive
things are stable but if the group is sitting at the
critical point one extra monkey picking a fight
is enough to kick off a large-scale brawlrdquo
coordinated and individual monkeys acttogether as one Tat Krakauer empha-sizes eases the flow o inormation romthe system as a whole to its constituent
parts making it all the more predictableNature is rie with examples one birdrsquos
sudden course correction changing the
direction o an entire flock alternatingDemocrat and Republican control oCongress and so on
Social circuitry
In the abstract Nature has good reasonto avor complexity and hierarchy ndash eachin its own way makes the tasks o compre-hension prediction and strategizingeasier But its a third concept circuitsthat grounds Flack and Krakauerrsquos teamand lays the practical oundation ormuch o their work
Usually the word ldquocircuitrdquo conjuresimages o the transistors microprocessors
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10 Santa Fe Institute Bulletin Vol 28
Time series representation of the dis tribution of fight sizes in a macaque society Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once An outburst by an individual wonrsquot normally tip the scales But when the system is perched at
a phase transition a brawl among many individuals can erupt
and lengths o copper wire that make upa computer Te analogy is apt Like anelectronic circuit individual components
ndash genes organs people ndash inormational-ly bind living things to orm a kind obiological or social computer In act the
circuit approach to describing a systemstems rom a hunch that the hierarchicalscales present throughout nature ldquoarise
Time
F i g h
t 1
F i g h
t 2
Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once
Vd
Pa
Je
Cb
Yv
Ta
Sg
Qs
Po
Mv
Ip
Fp
Eo
Dh
Tr
Pr
Fc
Zu
Vf
Fp
Ec
Zu
Zr
Iw
Is
Fp
Fc
Cc
Yv
Cd
Ta
Cc
Qv
Ip
Cb
Pa
Eb
Sb
Jc
Je
Hp
Lr
Jc
Zu
Po
Dh
Vf
Eo
Eo
Cd
Pa
Dh
Hh
At
Zu
Yv
Th
Ta
Fn
through a process o collective computa-tionrdquo creating slow-changing predictablesocial and biological structures Flack says
Circuits are more than just an analo-gy though ndash theyrsquore the tools that bridgethe gap between individual and collective
behavior And in a scientific field whereitrsquos easy to avoid real data circuits are one
way Flack and Krakauerrsquos research group
makes sure their theories are a good matchto the real world ldquoOur group is committedto an empirical approachrdquo Flack says ldquoWebelieve that only when these measures aredeveloped with an understanding o thedata generated by real systems will they
be useulrdquoTe process o building circuits begins
by analyzing how a systemrsquos individual parts work together Using the macaquefight data Flack Krakauer and ormerSFI Omidyar Fellow Simon DeDeo (nowat Indiana University Bloomington) devel-oped a statistical method they dubbedinductive game theory to analyze howthe monkeys reacted to othersrsquo fights Teresulting social circuit Flack says servesas a detailed model ldquoor how the micro-
scopic behavior maps to the unctionallyimportant macroscopic eatures o socialstructurerdquo such as the distribution o fightsizes In other words to construct a socialor biological circuit is to understand how agroup builds and maintains stable predict-able inormation hierarchies
Te final step is to produce a simplifiedsocial circuit what the researchers call a
ldquocognitive effective theoryrdquo that accurate-ly predicts how groups behave Te aimis to extract the key sorts o interactions
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 11
responsible or power structures fight-sizedistributions or other macroscopic eaturesusing ldquowhat we know about individual orcomponent cognition to coarse grain orcompressrdquo social circuits Flack says Suchcompression is essential she says because
living things canrsquot base their decisions on what ever y other living thing is doing instead theyrsquore orced to pay attention to
just a ew patterns or details o whatrsquos goingon around them
Te key question here as collaboratorand Princeton University graduate studentEleanor Brush puts it is how little inor-mation individuals need to successullyoutsmart others
Accidental or inevitable
Answering questions like that one ndash or testingsome o the teamrsquos more abstract predic-tions ndash remains a central challenge Poonor example describes his studies o electioncycles and policy change as ldquotoy modelsrdquo ndashthey capture qualitative eatures o the datasuch as party switching but donrsquot stand upto more precise quantitative tests
Meanwhile Krakauer and others sayitrsquos not always clear how to test particularhypotheses such as the prediction that basismismatch leads to ever-increasing complexity
in living things ldquoWersquore still looking or somecompelling examplerdquo Ellison says ldquoPart othe issue is that one can ofen play the devilrsquosadvocate and call into question the examplerdquoone reason why his work to construct ormal
precise measures o complexity is so import-ant he says
New techniques or rapidly analyzinggenetic data Krakauer says might improvethe situation Combining those techniques
with laboratory-based ldquoexperimental evolutionrdquo
in which researchers study the effects o preciseenvironmental changes on small organismssuch as bacteria could help test some o theendeavorrsquos core ideas such as the reflection
principle or the role o phase transitionsAnother potential avenue is to use ldquodigital
sources like computer games where we cancontrol to a large extent the orm o the dataor the conditions under which they werecollectedrdquo Krakauer says
esting their theories is just one part o theteamrsquos ambitious aims Tey hope Flack says
People gather in Washington DC before Martin Luther King Jrrsquos ldquoI Have a Dreamrdquo speech on Aug 28 1963 to demand equalities
Individuals tend to move faster and be less predictable than the slow-moving institutions they create
to achieve nothing less than an understandingo why lie is organized the way it is rom thesmallest bacteria to the largest human insti-tutions Tat requires combining real-worldobservation and abstract mathematical theoryin novel and creative ways
And as i that wasnrsquot enough Krakauerhas one more question in mind is lie anaccident or is it inevitable And i lie isinevitable well are we alone
ldquoI itrsquos not a product o a series o random
accidents but therersquos an underlying law-likeregularity that would give us confidence inbelieving in the possibility o lie presenteverywhere in the universerdquo he says ldquoSo
when one asks why does it matter whetheritrsquos chance or necessity it matters i we care
whether wersquore alone are notrdquo
Nathan Collins is a feelance science writernew ather and film aficionado based inSan Francisco
ldquoLiving things canrsquot base their decisions on what
every other living thing is doing instead theyrsquore
forced to pay attention to just a few patternsrdquo
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12 Santa Fe Institute Bulletin Vol 28
ldquohellipin mere Time all things follow
one another and in mere Space
all things are side by side it is
accordingly only by the combi-
nation of Time and Space thatthe representation of coexistence
arisesrdquo
mdash ARTHUR SCHOPENHAUER On the Fourfold Root
of the Principle of Sufficient Reason 1813
Yves Tanguy Indefinite Divisibility 1942
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 13
D I A C O M M O N S I N S E T H E N R I R O U S S E A U T H E
D R E A M 1 9 1 0 W I K I M E D I A C O M M O N S
Biological systems ndash rom cells totissues to individuals to societ-ies ndash are hierarchically organized
(eg Feldman amp Eschel 1982 Buss 1987Smith amp Szathmaacutery 1998 Valentineamp May 1996 Michod 2000 Frank2003) o many hierarchical organiza-tion suggests the nesting o components orindividuals into groups with these groupsaggregating into yet larger groups But this view ndash at least superficially ndash privilegesspace and matter over time and inorma-tion Many types o neural coding orexample require averaging or summing over neural firing ratesTe neuronsrsquo spatial location ndash that they are in proximity ndash iso course important but at least as important to the encoding
The explanation for the complex multi-scale structure of biological and social systems
lies in their manipulation of space and time to reduce uncertainty about the future
is their behavior in time Likewise insome monkey societies as I will discussin detail later in this review individualsestimate the uture cost o social inter-action by encoding the average outcomeo past interactions in special signals and
then summing over these signalsIn both examples inormation rom
events distributed in time as well as space(Figure 1) is captured with encodingsthat are used to control some behavioraloutput My collaborators and I in theCenter or Complexity amp Collective
Computation are exploring the idea that hierarchical organiza-tion at its core is a nesting o these kinds o unctional encodingsAs I will explain we think these unctional encodings result
LIFErsquoS INFORMATION HIERARCHY
BY JESSICA C FLACK
CO-DIRECTOR CENTER FOR COMPLEXITY amp COLLECTIVE COMPUTATION
WISCONSIN INSTITUTE FOR DISCOVERY UNIVERSITY OF WISCONSIN-MADISON
EXTERNAL PROFESSOR SANTA FE INSTITUTE
Jessica Flack
Number of spatial dimensions
N u m b e
r o f t i m e d i m e n s i o n s
0
0
1
2
3
4
5
1 2 3 4 5
UNSTABLE
TOO SIMPLE
UNPREDICTABLE (ELLIPTIC)
UNPREDICTABLE(ULTRAHYPERBOLIC)
TACHYONSONLY
UNSTABLE
Figure 1 The dimensionality of the time-space
continuum with properties postulated when x
does not equal 3 and y is larger than 1 Life on
earth exist s in three spatial dimensions and one
temporal dimension Biological systems effec-
tively ldquodiscretizerdquo time and space to reduce
environmental uncertainty by coarse-graining
and compressing environmental time series to
find regularities Components use the coarse-
grained descriptions to predict the future
tuning their behavior to their predictions
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14 Santa Fe Institute Bulletin Vol 28
rom biological systemsmanipulating space andtime (Figure 2) to acilitateinormation extraction which in turn acilitatesmore efficient extraction
o energy h i s inormation
hierarchy appears to bea universal property obiological systems andmay be the key to one oliersquos greatest mysteries ndashthe origins o biologicalcomplexity In this essayI review a body o work byDavid Krakauer myseland our research group
that has been inspiredby many years o work atthe Santa Fe Institute (eg Crutchfield 1994 Gell-Mann1996 Gell-Mann amp Lloyd 1996 Fontana amp Buss 1996 West Brown amp Enquist 1997 Fontana amp Schuster 1998Ancel amp Fontana 2000 Stadler Stadler Wagner amp Fontana2001 Smith 2003 Crutchfield amp Goumlrnerup 2006 Smith2008) Our work suggests that complexity and the multi-scalestructure o biological systems are the predictable outcomeo evolutionary dynamics driven by uncertainty minimiza-tion (Krakauer 2011 Flack 2012 Flack Erwin Elliot ampKrakauer 2013)
Tis recasting o the evolutionary process as an inerentialone1 (Bergstrom amp Rosvall 2009 Krakauer 2011) is based onthe premise that organisms and other biological systems canbe viewed as hypotheses about the present and uture environ-ments they or their offspring will encounter induced rom thehistory o past environmental states they or their ancestors have
experienced (eg Crutchfield amp Feldman 2001 Krakauer ampZannotto 2009 Ellison Flack amp Krakauer in prep) Tis premise o course only holds i the past is prologue ndash that ishas regularities and the regularities can be estimated and evenmanipulated (as in niche construction) by biological systems ortheir components to produce adaptive behavior (Flack ErwinElliot amp Krakauer 2013 Ellison Flack amp Krakauer in prep)
I these premises are correct lie at its core is computa-tional and a central question becomes How do systems andtheir components estimate and control the regularity in theirenvironments and use these estimates to tune their strategiesI suggest that the answer to this question and the explanation
or complexity is that biological systems manipulate spatialand temporal structure to produce order ndash low variance ndash atlocal scales
UNCERTAINTY REDUCTION
Te story I want to tell starts with the observation that witheach new level o organization typically comes new unction-ality ndash a new eature with positive payoff consequences or thesystem as a whole or or its components (Flack Erwin Elliot
1 Tis idea is related to work on Maxwel lrsquos Demon (eg Krakauer2011 Mandal 983121uan amp Jarzynski 2013) and the Carnot cycle ( egSmith 2003) but we do not yet understand the mapping
Figure 2 Biological systems ndash from
(left to right ) Volvox colonies to slime
molds to animal societies to large-scale
ecosystems such as reefs t o human
cities ndash are hierarchically organized with
multiple functionally important time and
space scales All feature 1) components
with only partially aligned interests
exhibiting coherent behavior at the
aggregate level 2) components that turn
over and that co-exist in the sys tem at
varying stages of development
3) social structure that persists but
component behavior that fluctuates and
4) macroscopic variation in temporal
and spatial structure and coupling with
microscopic behavior which has func-
tional impl ications when the components
can perceive ndash in evolutionary develop-
mental or ecological time ndash regularities
at the macroscopic scale
1 2
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 15
amp Krakauer 2013) Policing in a pigtailed macaque group is anexample Once a heavy tailed distribution o power ndash defined asthe degree o consensus in the group that an individual can winfights (see Flack amp Krakauer 2006 Boehm amp Flack 2010 BrushKrakauer amp Flack2013) ndash becomes effec-
tively institutionalized(here meaning hard tochange) policing (anintrinsically costlystrategy) becomesaffordable at least tothose animals that sitin the tail o the powerdistribution thosesuper powerul monkeys who are rarely or never challenged when they break up fights (Flack de Waal amp Krakauer 2005Flack Girvan de Waal amp Krakauer 2006)
My collaborators and I propose that a primary driver o theemergence o new unctionality such as policing is the reduc-tion o environmental uncertainty through the constructiono nested dynamical processes with a range o characteris-tic time constants (Flack Erwin Elliot amp Krakauer 2013)Tese nested dynamical processes arise as components extractregularities rom ast microscopic behavior by coarse-grain-ing (or compressing) the history o events to which they havebeen exposed
Proteins or example can have a long hal-lie relative toRNA transcripts and can be thought o as the summed outputo translation Cells have a long hal-lie relative to proteins
and are a unction o the summed output o arrays o spatiallystructured proteins Both proteins and cells represent someaverage measure o the noisier activity o their constituentsSimilarly a pigtailed macaquersquos estimate o its power is a kind
o average measure othe collective percep-
tion in the groupthat the macaque iscapable o winningights and this is abetter predictor othe cost the macaque will pay during fightsthan the outcome oany single melee as
these outcomes can fluctuate or contextual reasons Tesecoarse-grainings or averages are encoded as slow variables(Flack amp de Waal 2007 Flack 2012 Flack Erwin Elliot
amp Krakauer 2013 see also Feret Danos Krivine Harner ampFontana 2009 or a similar idea) Slow variables may have aspatial component as well as a temporal component as in the protein and cell examples (Figure 6) or minimally only atemporal component as in the monkey example
As a consequence o integrating over abundant microscopic processes slow variables provide better predictors o the localuture configuration o a system than the states o the fluctuatingmicroscopic components In doing so they promote acceleratedrates o microscopic adaptation Slow variables acilitate adapta-tion in two ways Tey allow components to fine-tune theirbehavior and they ree components to search at low cost a larger
3 4
Slow variables provide better predictors
of the local future configuration of a
system than the states of the fluctuating
microscopic componentsrdquo
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16 Santa Fe Institute Bulletin Vol 28
Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and
proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since
fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous
starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of
relevant genes and proteins and the output is the target phenot ypic feature
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 17
space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
8122019 How Life Got Complex
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
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April 2014 Santa Fe Institute Bulletin 19
A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
8122019 How Life Got Complex
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22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
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help us achieve great things
ITrsquoS SIMPLE
Support
ComplexityScience
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April 2014 Santa Fe Institute Bulletin 7April 2014 Santa Fe Institute Bulletin 7
Y pestis the bacterium that causes bubonic
plague (aka The Black Death of the Middle Ages)
Lie on earth began some 35 billion
years ago not all that long afer the planet itsel first ormed and or 15
billion years it chugged along single-celledcreatures sel-replicating dividing diversi-ying Remarkably it took that long ndash 7500times longer than all o human history ndash orthe first multicellular lie to emerge and stilllonger or it to evolve into lie as we know it
Despite that the question manyresearchers ask isnrsquot what took so long
but rather why complex lie would haveevolved in the first place Consider thissingle-celled organisms make up more thanhal o the biomass on earth and even oneo the tiniest organisms ndash Y pestis betterknown as bubonic plague ndash can effortlesslythoughtlessly kill you
Nature it seems doesnrsquot need youIndeed there isnrsquot any obvious reasonit would go to the trouble o creatingsomething as complex as a human beingcomplete with its differentiated organs and
top-down control systems And yet despiteour billion years o Naturersquos great ldquomehrdquohere you are alive multicellular complex
ndash even intelligent enough to ponder yourown existence
What was Nature thinking Accordingto one argument bacteria first boundtogether in colonies that enhanced cooper-ation and hence survival Eventually thosebacteria bound together physically as wellcreating the first multicellular lie Andso on
ldquoYou could say thatrsquos an answer but then you could go a bit urther and ask what isit exactly that makes you a better compet-
itorrdquo says David Krakauer who with SFIExternal Proessor Jessica Flack co-directs
the Wisconsin Institute or DiscoveryrsquosCenter or Complexity and CollectiveComputation or C4 and SFIrsquos Johnempleton Foundation-unded ldquoEvolutiono Complexity and Intelligence on Earthrdquoresearch project
ldquoWell yoursquore outsmarting everyone elserdquoKrakauer says
Simple versus complex
Despite its seeming indifference Naturedoes seem to have thought highly enough
Why Nature Went to the Trouble
of CreatinghellipYou BY NATHAN COLLINS
In a complex world where plants and animals and everything else are duking
it out to survive an organism stands to gain from becoming more complex
o complex structures to produce a ew othem and to have ratcheted up the complex-ity urther by embedding complex structures
within complex structures ndash animals withhearts and lungs and circulatory systems orgroups o people capable o building theirown social institutions
But why What purpose does it serveldquoWhy lie is hierarchically organized isnot at all obviousrdquo Flack says and howan organismrsquos or a societyrsquos complexity
relates to the complexity o its environ-ment remains unclear
Our anthropomorphized Nature mighthave started with one very simple idea
what Krakauer calls the reflection princi- ple which presupposes that living thingscanrsquot be more complex than their environ-ments an idea rooted in experiments ldquoI
you take organisms and you place themin simpler environments they just throweverything [superfluous] away Tey losegenesrdquo Krakauer says
At the same time the world does seem toavor an intelligent creature Even the tiniestliving things need to be able to comprehend predict and react to their environmentsthatrsquos what allows them to outsmart eachother he says In a complex world where
plants and animals and everything else areduking it out to survive an organism standsto gain rom becoming more complex
Tat tension between simplicity andcomplexity is the starting point or C4
postdoctoral ellow Christopher Ellison
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Watch David Krakauerrsquos interview at
wwwsantafeedulife
What does Nature care about hearts brains and other organs or for that
matter political parties ndash in other words structures within structures
ldquoWersquod like to understand the implica-tions this has or the environmentrdquo hesays ldquoFor example do simple or complexorganisms experience and live in simple orcomplex environmentsrdquo
Working with Flack and KrakauerEllison developed ldquoMarkov organismsrdquo
computer-simulated creatures that merge
insights rom biology with inormation processing techniques rom computerscience to help figure out how lie balancesthese trade-offs Rather than modeling real
organisms themselves he ocuses on howinormation flows in the ecological system
Itrsquos early days Ellison says but his simula-tions suggest that lie will ofen evolve tomatch its environmentrsquos complexity ndash findingsthat are in line with the reflection principlebut with some interesting caveats For one
thing evolving Markov organisms tend toovershoot their worldsrsquocomplexity and mighttake a long time to pruneunnecessary complexities
Teyrsquore also suscepti-ble to ldquobasis mismatchrdquo a
problem you know welli yoursquove ever tried to explain to a tourist howto get around in your hometown o youthere are just a ew steps but to the noviceitrsquos a complex process with many twists and
turns and every intersection represents a possible misdirection in sprawling cities likeLos Angeles a direction as simple as ldquotakeSunset to Vermont and turn lefrdquo becomesinfinitely complex Markov organisms arethe same i their way o solving a problem
doesnrsquot line up with how their environmentsconstructed it Ellisonrsquos simulations showMarkov organismsrsquo complexity keeps evolv-ing upward orever
But Ellisonrsquos inormation-centricapproach has some benefits One he says
ldquois that it attempts to answer the questiono how complexity evolves in an organ-ism-independent ashionrdquo meaning thatthe ideas apply equally well to anythingrom bacteria to politics Similarly Ellisonrsquosmethod allows the team to describe both anorganism and its environmentrsquos complexity
in the same terms because both derive romthe same underlying models o inormation
processing Surprisingly thatrsquos somethingew i any other researchers have done
Constructing predictability
So it appears Nature might avor multicel-lular lie i it affords a certain computational power not readily available to single-celledorganisms But what does Nature care abouthearts brains and other organs or or thatmatter political parties ndash in other words
structures within structureshe answer Flack says is that living
things like their worlds to be predictableand what makes cells and people more likelyto survive Nature avors
Much o the structure we observe in the world Flack says probably evolved becausestructure begets stability hence predictabilityGroups o genes cells or animals changetheir collective behaviors slowly compared
with individual genes cells or animals givingthe aster-moving individual components a
chance to anticipate changes more easilyBiologists call the idea that plants andanimals ndash and genes and organs and so on
ndash structure their environments to be morestable and predictable ldquoniche constructionrdquoand it usually applies to physical structurelike ants building nests But it also can beapplied to temporal structure
Politics offers perhaps a simple exampleEarly US politicians were explicit aboutdesigning Congress and the rest o govern-ment so that it would change gradually and
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April 2014 Santa Fe Institute Bulletin 9April 2014 Santa Fe Institute Bulletin 9
According to the reflection principle organisms are reflections of their environments Here the environment is represented as a prose excerpt from
Darwinrsquos Origin of Species wherein he contemplates an entangled bank filled with ldquoendless forms most beautiful and most wonderfulrdquo each evolved through natural selection Organism A matches the environmentrsquos complexity while Organism B is less complex than the environment and Organism C is
more complex than the environment The research team is asking when evolution satisfies or v iolates the reflection principle
be more stable Even when we complainabout our slow-moving government weare undoubtedly comorted by that verycharacteristic because slow is predictableAnd when our environment is predictable we know how to make sound decisions
So people construct relatively slow-mov-ing predictable institutions At the sametime those institutions help shape thebehavior o the individuals who createdthem points out C4 postdoctoral ellowPhilip Poon who is examining eedbackrom institutions to explain (in the govern-ment case) why or example Democrats andRepublicans seem to trade off controllingthe White House and Congress every ewterms A critical issue is o course howthat eedback works rom a mechanical
perspective ndash that is how the ways individ-uals perceive and understand institutionsinfluence their decision-making
Drawing on the theory o phase transi-tions the same one that explains whychanging a seemingly minor variable cansuddenly shif an entire system rom onestate to another Flack Krakauer and C4researcher and ormer SFI PostdoctoralFellow Bryan Daniels argue that hierarchi-cal structures bestow another advantageeicient inormation low rom the
collective to its individual parts Systems perched on the edge o a phase transitionare exquisitely sensitive so that a small orlocalized change ldquoleads to a large change inthe global dynamicsrdquo Daniels says Toughthat might seem unstable or chaotic systems
near the critical point where a phase transi-
tion begins are actually quite predictableGroups o macaque monkeys ndash one o
Flack and the teamrsquos earliest sources o data
and inspiration ndash are one system that appearsto be resting near a phase transition Whenmonkeys arenrsquot eeling especially aggres-sive Daniels says things are stable and ldquoi Isuddenly act out nothingrsquos going to happenhellipbut i [the group is] sitting at the critical
point then [my] contribution is always moreimportantrdquo and one extra monkey picking afight is enough to kick off a large-scale brawl
Below the critical point individualmonkeys act airly independently but rightat the transition their behavior is tightly
ldquoWhen monkeys arenrsquot feeling especially aggressive
things are stable but if the group is sitting at the
critical point one extra monkey picking a fight
is enough to kick off a large-scale brawlrdquo
coordinated and individual monkeys acttogether as one Tat Krakauer empha-sizes eases the flow o inormation romthe system as a whole to its constituent
parts making it all the more predictableNature is rie with examples one birdrsquos
sudden course correction changing the
direction o an entire flock alternatingDemocrat and Republican control oCongress and so on
Social circuitry
In the abstract Nature has good reasonto avor complexity and hierarchy ndash eachin its own way makes the tasks o compre-hension prediction and strategizingeasier But its a third concept circuitsthat grounds Flack and Krakauerrsquos teamand lays the practical oundation ormuch o their work
Usually the word ldquocircuitrdquo conjuresimages o the transistors microprocessors
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Time series representation of the dis tribution of fight sizes in a macaque society Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once An outburst by an individual wonrsquot normally tip the scales But when the system is perched at
a phase transition a brawl among many individuals can erupt
and lengths o copper wire that make upa computer Te analogy is apt Like anelectronic circuit individual components
ndash genes organs people ndash inormational-ly bind living things to orm a kind obiological or social computer In act the
circuit approach to describing a systemstems rom a hunch that the hierarchicalscales present throughout nature ldquoarise
Time
F i g h
t 1
F i g h
t 2
Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once
Vd
Pa
Je
Cb
Yv
Ta
Sg
Qs
Po
Mv
Ip
Fp
Eo
Dh
Tr
Pr
Fc
Zu
Vf
Fp
Ec
Zu
Zr
Iw
Is
Fp
Fc
Cc
Yv
Cd
Ta
Cc
Qv
Ip
Cb
Pa
Eb
Sb
Jc
Je
Hp
Lr
Jc
Zu
Po
Dh
Vf
Eo
Eo
Cd
Pa
Dh
Hh
At
Zu
Yv
Th
Ta
Fn
through a process o collective computa-tionrdquo creating slow-changing predictablesocial and biological structures Flack says
Circuits are more than just an analo-gy though ndash theyrsquore the tools that bridgethe gap between individual and collective
behavior And in a scientific field whereitrsquos easy to avoid real data circuits are one
way Flack and Krakauerrsquos research group
makes sure their theories are a good matchto the real world ldquoOur group is committedto an empirical approachrdquo Flack says ldquoWebelieve that only when these measures aredeveloped with an understanding o thedata generated by real systems will they
be useulrdquoTe process o building circuits begins
by analyzing how a systemrsquos individual parts work together Using the macaquefight data Flack Krakauer and ormerSFI Omidyar Fellow Simon DeDeo (nowat Indiana University Bloomington) devel-oped a statistical method they dubbedinductive game theory to analyze howthe monkeys reacted to othersrsquo fights Teresulting social circuit Flack says servesas a detailed model ldquoor how the micro-
scopic behavior maps to the unctionallyimportant macroscopic eatures o socialstructurerdquo such as the distribution o fightsizes In other words to construct a socialor biological circuit is to understand how agroup builds and maintains stable predict-able inormation hierarchies
Te final step is to produce a simplifiedsocial circuit what the researchers call a
ldquocognitive effective theoryrdquo that accurate-ly predicts how groups behave Te aimis to extract the key sorts o interactions
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April 2014 Santa Fe Institute Bulletin 11
responsible or power structures fight-sizedistributions or other macroscopic eaturesusing ldquowhat we know about individual orcomponent cognition to coarse grain orcompressrdquo social circuits Flack says Suchcompression is essential she says because
living things canrsquot base their decisions on what ever y other living thing is doing instead theyrsquore orced to pay attention to
just a ew patterns or details o whatrsquos goingon around them
Te key question here as collaboratorand Princeton University graduate studentEleanor Brush puts it is how little inor-mation individuals need to successullyoutsmart others
Accidental or inevitable
Answering questions like that one ndash or testingsome o the teamrsquos more abstract predic-tions ndash remains a central challenge Poonor example describes his studies o electioncycles and policy change as ldquotoy modelsrdquo ndashthey capture qualitative eatures o the datasuch as party switching but donrsquot stand upto more precise quantitative tests
Meanwhile Krakauer and others sayitrsquos not always clear how to test particularhypotheses such as the prediction that basismismatch leads to ever-increasing complexity
in living things ldquoWersquore still looking or somecompelling examplerdquo Ellison says ldquoPart othe issue is that one can ofen play the devilrsquosadvocate and call into question the examplerdquoone reason why his work to construct ormal
precise measures o complexity is so import-ant he says
New techniques or rapidly analyzinggenetic data Krakauer says might improvethe situation Combining those techniques
with laboratory-based ldquoexperimental evolutionrdquo
in which researchers study the effects o preciseenvironmental changes on small organismssuch as bacteria could help test some o theendeavorrsquos core ideas such as the reflection
principle or the role o phase transitionsAnother potential avenue is to use ldquodigital
sources like computer games where we cancontrol to a large extent the orm o the dataor the conditions under which they werecollectedrdquo Krakauer says
esting their theories is just one part o theteamrsquos ambitious aims Tey hope Flack says
People gather in Washington DC before Martin Luther King Jrrsquos ldquoI Have a Dreamrdquo speech on Aug 28 1963 to demand equalities
Individuals tend to move faster and be less predictable than the slow-moving institutions they create
to achieve nothing less than an understandingo why lie is organized the way it is rom thesmallest bacteria to the largest human insti-tutions Tat requires combining real-worldobservation and abstract mathematical theoryin novel and creative ways
And as i that wasnrsquot enough Krakauerhas one more question in mind is lie anaccident or is it inevitable And i lie isinevitable well are we alone
ldquoI itrsquos not a product o a series o random
accidents but therersquos an underlying law-likeregularity that would give us confidence inbelieving in the possibility o lie presenteverywhere in the universerdquo he says ldquoSo
when one asks why does it matter whetheritrsquos chance or necessity it matters i we care
whether wersquore alone are notrdquo
Nathan Collins is a feelance science writernew ather and film aficionado based inSan Francisco
ldquoLiving things canrsquot base their decisions on what
every other living thing is doing instead theyrsquore
forced to pay attention to just a few patternsrdquo
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12 Santa Fe Institute Bulletin Vol 28
ldquohellipin mere Time all things follow
one another and in mere Space
all things are side by side it is
accordingly only by the combi-
nation of Time and Space thatthe representation of coexistence
arisesrdquo
mdash ARTHUR SCHOPENHAUER On the Fourfold Root
of the Principle of Sufficient Reason 1813
Yves Tanguy Indefinite Divisibility 1942
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April 2014 Santa Fe Institute Bulletin 13
D I A C O M M O N S I N S E T H E N R I R O U S S E A U T H E
D R E A M 1 9 1 0 W I K I M E D I A C O M M O N S
Biological systems ndash rom cells totissues to individuals to societ-ies ndash are hierarchically organized
(eg Feldman amp Eschel 1982 Buss 1987Smith amp Szathmaacutery 1998 Valentineamp May 1996 Michod 2000 Frank2003) o many hierarchical organiza-tion suggests the nesting o components orindividuals into groups with these groupsaggregating into yet larger groups But this view ndash at least superficially ndash privilegesspace and matter over time and inorma-tion Many types o neural coding orexample require averaging or summing over neural firing ratesTe neuronsrsquo spatial location ndash that they are in proximity ndash iso course important but at least as important to the encoding
The explanation for the complex multi-scale structure of biological and social systems
lies in their manipulation of space and time to reduce uncertainty about the future
is their behavior in time Likewise insome monkey societies as I will discussin detail later in this review individualsestimate the uture cost o social inter-action by encoding the average outcomeo past interactions in special signals and
then summing over these signalsIn both examples inormation rom
events distributed in time as well as space(Figure 1) is captured with encodingsthat are used to control some behavioraloutput My collaborators and I in theCenter or Complexity amp Collective
Computation are exploring the idea that hierarchical organiza-tion at its core is a nesting o these kinds o unctional encodingsAs I will explain we think these unctional encodings result
LIFErsquoS INFORMATION HIERARCHY
BY JESSICA C FLACK
CO-DIRECTOR CENTER FOR COMPLEXITY amp COLLECTIVE COMPUTATION
WISCONSIN INSTITUTE FOR DISCOVERY UNIVERSITY OF WISCONSIN-MADISON
EXTERNAL PROFESSOR SANTA FE INSTITUTE
Jessica Flack
Number of spatial dimensions
N u m b e
r o f t i m e d i m e n s i o n s
0
0
1
2
3
4
5
1 2 3 4 5
UNSTABLE
TOO SIMPLE
UNPREDICTABLE (ELLIPTIC)
UNPREDICTABLE(ULTRAHYPERBOLIC)
TACHYONSONLY
UNSTABLE
Figure 1 The dimensionality of the time-space
continuum with properties postulated when x
does not equal 3 and y is larger than 1 Life on
earth exist s in three spatial dimensions and one
temporal dimension Biological systems effec-
tively ldquodiscretizerdquo time and space to reduce
environmental uncertainty by coarse-graining
and compressing environmental time series to
find regularities Components use the coarse-
grained descriptions to predict the future
tuning their behavior to their predictions
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rom biological systemsmanipulating space andtime (Figure 2) to acilitateinormation extraction which in turn acilitatesmore efficient extraction
o energy h i s inormation
hierarchy appears to bea universal property obiological systems andmay be the key to one oliersquos greatest mysteries ndashthe origins o biologicalcomplexity In this essayI review a body o work byDavid Krakauer myseland our research group
that has been inspiredby many years o work atthe Santa Fe Institute (eg Crutchfield 1994 Gell-Mann1996 Gell-Mann amp Lloyd 1996 Fontana amp Buss 1996 West Brown amp Enquist 1997 Fontana amp Schuster 1998Ancel amp Fontana 2000 Stadler Stadler Wagner amp Fontana2001 Smith 2003 Crutchfield amp Goumlrnerup 2006 Smith2008) Our work suggests that complexity and the multi-scalestructure o biological systems are the predictable outcomeo evolutionary dynamics driven by uncertainty minimiza-tion (Krakauer 2011 Flack 2012 Flack Erwin Elliot ampKrakauer 2013)
Tis recasting o the evolutionary process as an inerentialone1 (Bergstrom amp Rosvall 2009 Krakauer 2011) is based onthe premise that organisms and other biological systems canbe viewed as hypotheses about the present and uture environ-ments they or their offspring will encounter induced rom thehistory o past environmental states they or their ancestors have
experienced (eg Crutchfield amp Feldman 2001 Krakauer ampZannotto 2009 Ellison Flack amp Krakauer in prep) Tis premise o course only holds i the past is prologue ndash that ishas regularities and the regularities can be estimated and evenmanipulated (as in niche construction) by biological systems ortheir components to produce adaptive behavior (Flack ErwinElliot amp Krakauer 2013 Ellison Flack amp Krakauer in prep)
I these premises are correct lie at its core is computa-tional and a central question becomes How do systems andtheir components estimate and control the regularity in theirenvironments and use these estimates to tune their strategiesI suggest that the answer to this question and the explanation
or complexity is that biological systems manipulate spatialand temporal structure to produce order ndash low variance ndash atlocal scales
UNCERTAINTY REDUCTION
Te story I want to tell starts with the observation that witheach new level o organization typically comes new unction-ality ndash a new eature with positive payoff consequences or thesystem as a whole or or its components (Flack Erwin Elliot
1 Tis idea is related to work on Maxwel lrsquos Demon (eg Krakauer2011 Mandal 983121uan amp Jarzynski 2013) and the Carnot cycle ( egSmith 2003) but we do not yet understand the mapping
Figure 2 Biological systems ndash from
(left to right ) Volvox colonies to slime
molds to animal societies to large-scale
ecosystems such as reefs t o human
cities ndash are hierarchically organized with
multiple functionally important time and
space scales All feature 1) components
with only partially aligned interests
exhibiting coherent behavior at the
aggregate level 2) components that turn
over and that co-exist in the sys tem at
varying stages of development
3) social structure that persists but
component behavior that fluctuates and
4) macroscopic variation in temporal
and spatial structure and coupling with
microscopic behavior which has func-
tional impl ications when the components
can perceive ndash in evolutionary develop-
mental or ecological time ndash regularities
at the macroscopic scale
1 2
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April 2014 Santa Fe Institute Bulletin 15
amp Krakauer 2013) Policing in a pigtailed macaque group is anexample Once a heavy tailed distribution o power ndash defined asthe degree o consensus in the group that an individual can winfights (see Flack amp Krakauer 2006 Boehm amp Flack 2010 BrushKrakauer amp Flack2013) ndash becomes effec-
tively institutionalized(here meaning hard tochange) policing (anintrinsically costlystrategy) becomesaffordable at least tothose animals that sitin the tail o the powerdistribution thosesuper powerul monkeys who are rarely or never challenged when they break up fights (Flack de Waal amp Krakauer 2005Flack Girvan de Waal amp Krakauer 2006)
My collaborators and I propose that a primary driver o theemergence o new unctionality such as policing is the reduc-tion o environmental uncertainty through the constructiono nested dynamical processes with a range o characteris-tic time constants (Flack Erwin Elliot amp Krakauer 2013)Tese nested dynamical processes arise as components extractregularities rom ast microscopic behavior by coarse-grain-ing (or compressing) the history o events to which they havebeen exposed
Proteins or example can have a long hal-lie relative toRNA transcripts and can be thought o as the summed outputo translation Cells have a long hal-lie relative to proteins
and are a unction o the summed output o arrays o spatiallystructured proteins Both proteins and cells represent someaverage measure o the noisier activity o their constituentsSimilarly a pigtailed macaquersquos estimate o its power is a kind
o average measure othe collective percep-
tion in the groupthat the macaque iscapable o winningights and this is abetter predictor othe cost the macaque will pay during fightsthan the outcome oany single melee as
these outcomes can fluctuate or contextual reasons Tesecoarse-grainings or averages are encoded as slow variables(Flack amp de Waal 2007 Flack 2012 Flack Erwin Elliot
amp Krakauer 2013 see also Feret Danos Krivine Harner ampFontana 2009 or a similar idea) Slow variables may have aspatial component as well as a temporal component as in the protein and cell examples (Figure 6) or minimally only atemporal component as in the monkey example
As a consequence o integrating over abundant microscopic processes slow variables provide better predictors o the localuture configuration o a system than the states o the fluctuatingmicroscopic components In doing so they promote acceleratedrates o microscopic adaptation Slow variables acilitate adapta-tion in two ways Tey allow components to fine-tune theirbehavior and they ree components to search at low cost a larger
3 4
Slow variables provide better predictors
of the local future configuration of a
system than the states of the fluctuating
microscopic componentsrdquo
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Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and
proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since
fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous
starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of
relevant genes and proteins and the output is the target phenot ypic feature
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April 2014 Santa Fe Institute Bulletin 17
space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 19
A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
8122019 How Life Got Complex
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2428
22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2528
April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
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generate the insights that matter
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ITrsquoS SIMPLE
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8122019 How Life Got Complex
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8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 7April 2014 Santa Fe Institute Bulletin 7
Y pestis the bacterium that causes bubonic
plague (aka The Black Death of the Middle Ages)
Lie on earth began some 35 billion
years ago not all that long afer the planet itsel first ormed and or 15
billion years it chugged along single-celledcreatures sel-replicating dividing diversi-ying Remarkably it took that long ndash 7500times longer than all o human history ndash orthe first multicellular lie to emerge and stilllonger or it to evolve into lie as we know it
Despite that the question manyresearchers ask isnrsquot what took so long
but rather why complex lie would haveevolved in the first place Consider thissingle-celled organisms make up more thanhal o the biomass on earth and even oneo the tiniest organisms ndash Y pestis betterknown as bubonic plague ndash can effortlesslythoughtlessly kill you
Nature it seems doesnrsquot need youIndeed there isnrsquot any obvious reasonit would go to the trouble o creatingsomething as complex as a human beingcomplete with its differentiated organs and
top-down control systems And yet despiteour billion years o Naturersquos great ldquomehrdquohere you are alive multicellular complex
ndash even intelligent enough to ponder yourown existence
What was Nature thinking Accordingto one argument bacteria first boundtogether in colonies that enhanced cooper-ation and hence survival Eventually thosebacteria bound together physically as wellcreating the first multicellular lie Andso on
ldquoYou could say thatrsquos an answer but then you could go a bit urther and ask what isit exactly that makes you a better compet-
itorrdquo says David Krakauer who with SFIExternal Proessor Jessica Flack co-directs
the Wisconsin Institute or DiscoveryrsquosCenter or Complexity and CollectiveComputation or C4 and SFIrsquos Johnempleton Foundation-unded ldquoEvolutiono Complexity and Intelligence on Earthrdquoresearch project
ldquoWell yoursquore outsmarting everyone elserdquoKrakauer says
Simple versus complex
Despite its seeming indifference Naturedoes seem to have thought highly enough
Why Nature Went to the Trouble
of CreatinghellipYou BY NATHAN COLLINS
In a complex world where plants and animals and everything else are duking
it out to survive an organism stands to gain from becoming more complex
o complex structures to produce a ew othem and to have ratcheted up the complex-ity urther by embedding complex structures
within complex structures ndash animals withhearts and lungs and circulatory systems orgroups o people capable o building theirown social institutions
But why What purpose does it serveldquoWhy lie is hierarchically organized isnot at all obviousrdquo Flack says and howan organismrsquos or a societyrsquos complexity
relates to the complexity o its environ-ment remains unclear
Our anthropomorphized Nature mighthave started with one very simple idea
what Krakauer calls the reflection princi- ple which presupposes that living thingscanrsquot be more complex than their environ-ments an idea rooted in experiments ldquoI
you take organisms and you place themin simpler environments they just throweverything [superfluous] away Tey losegenesrdquo Krakauer says
At the same time the world does seem toavor an intelligent creature Even the tiniestliving things need to be able to comprehend predict and react to their environmentsthatrsquos what allows them to outsmart eachother he says In a complex world where
plants and animals and everything else areduking it out to survive an organism standsto gain rom becoming more complex
Tat tension between simplicity andcomplexity is the starting point or C4
postdoctoral ellow Christopher Ellison
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 1028
8 Santa Fe Institute Bulletin Vol 28
Watch David Krakauerrsquos interview at
wwwsantafeedulife
What does Nature care about hearts brains and other organs or for that
matter political parties ndash in other words structures within structures
ldquoWersquod like to understand the implica-tions this has or the environmentrdquo hesays ldquoFor example do simple or complexorganisms experience and live in simple orcomplex environmentsrdquo
Working with Flack and KrakauerEllison developed ldquoMarkov organismsrdquo
computer-simulated creatures that merge
insights rom biology with inormation processing techniques rom computerscience to help figure out how lie balancesthese trade-offs Rather than modeling real
organisms themselves he ocuses on howinormation flows in the ecological system
Itrsquos early days Ellison says but his simula-tions suggest that lie will ofen evolve tomatch its environmentrsquos complexity ndash findingsthat are in line with the reflection principlebut with some interesting caveats For one
thing evolving Markov organisms tend toovershoot their worldsrsquocomplexity and mighttake a long time to pruneunnecessary complexities
Teyrsquore also suscepti-ble to ldquobasis mismatchrdquo a
problem you know welli yoursquove ever tried to explain to a tourist howto get around in your hometown o youthere are just a ew steps but to the noviceitrsquos a complex process with many twists and
turns and every intersection represents a possible misdirection in sprawling cities likeLos Angeles a direction as simple as ldquotakeSunset to Vermont and turn lefrdquo becomesinfinitely complex Markov organisms arethe same i their way o solving a problem
doesnrsquot line up with how their environmentsconstructed it Ellisonrsquos simulations showMarkov organismsrsquo complexity keeps evolv-ing upward orever
But Ellisonrsquos inormation-centricapproach has some benefits One he says
ldquois that it attempts to answer the questiono how complexity evolves in an organ-ism-independent ashionrdquo meaning thatthe ideas apply equally well to anythingrom bacteria to politics Similarly Ellisonrsquosmethod allows the team to describe both anorganism and its environmentrsquos complexity
in the same terms because both derive romthe same underlying models o inormation
processing Surprisingly thatrsquos somethingew i any other researchers have done
Constructing predictability
So it appears Nature might avor multicel-lular lie i it affords a certain computational power not readily available to single-celledorganisms But what does Nature care abouthearts brains and other organs or or thatmatter political parties ndash in other words
structures within structureshe answer Flack says is that living
things like their worlds to be predictableand what makes cells and people more likelyto survive Nature avors
Much o the structure we observe in the world Flack says probably evolved becausestructure begets stability hence predictabilityGroups o genes cells or animals changetheir collective behaviors slowly compared
with individual genes cells or animals givingthe aster-moving individual components a
chance to anticipate changes more easilyBiologists call the idea that plants andanimals ndash and genes and organs and so on
ndash structure their environments to be morestable and predictable ldquoniche constructionrdquoand it usually applies to physical structurelike ants building nests But it also can beapplied to temporal structure
Politics offers perhaps a simple exampleEarly US politicians were explicit aboutdesigning Congress and the rest o govern-ment so that it would change gradually and
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 9April 2014 Santa Fe Institute Bulletin 9
According to the reflection principle organisms are reflections of their environments Here the environment is represented as a prose excerpt from
Darwinrsquos Origin of Species wherein he contemplates an entangled bank filled with ldquoendless forms most beautiful and most wonderfulrdquo each evolved through natural selection Organism A matches the environmentrsquos complexity while Organism B is less complex than the environment and Organism C is
more complex than the environment The research team is asking when evolution satisfies or v iolates the reflection principle
be more stable Even when we complainabout our slow-moving government weare undoubtedly comorted by that verycharacteristic because slow is predictableAnd when our environment is predictable we know how to make sound decisions
So people construct relatively slow-mov-ing predictable institutions At the sametime those institutions help shape thebehavior o the individuals who createdthem points out C4 postdoctoral ellowPhilip Poon who is examining eedbackrom institutions to explain (in the govern-ment case) why or example Democrats andRepublicans seem to trade off controllingthe White House and Congress every ewterms A critical issue is o course howthat eedback works rom a mechanical
perspective ndash that is how the ways individ-uals perceive and understand institutionsinfluence their decision-making
Drawing on the theory o phase transi-tions the same one that explains whychanging a seemingly minor variable cansuddenly shif an entire system rom onestate to another Flack Krakauer and C4researcher and ormer SFI PostdoctoralFellow Bryan Daniels argue that hierarchi-cal structures bestow another advantageeicient inormation low rom the
collective to its individual parts Systems perched on the edge o a phase transitionare exquisitely sensitive so that a small orlocalized change ldquoleads to a large change inthe global dynamicsrdquo Daniels says Toughthat might seem unstable or chaotic systems
near the critical point where a phase transi-
tion begins are actually quite predictableGroups o macaque monkeys ndash one o
Flack and the teamrsquos earliest sources o data
and inspiration ndash are one system that appearsto be resting near a phase transition Whenmonkeys arenrsquot eeling especially aggres-sive Daniels says things are stable and ldquoi Isuddenly act out nothingrsquos going to happenhellipbut i [the group is] sitting at the critical
point then [my] contribution is always moreimportantrdquo and one extra monkey picking afight is enough to kick off a large-scale brawl
Below the critical point individualmonkeys act airly independently but rightat the transition their behavior is tightly
ldquoWhen monkeys arenrsquot feeling especially aggressive
things are stable but if the group is sitting at the
critical point one extra monkey picking a fight
is enough to kick off a large-scale brawlrdquo
coordinated and individual monkeys acttogether as one Tat Krakauer empha-sizes eases the flow o inormation romthe system as a whole to its constituent
parts making it all the more predictableNature is rie with examples one birdrsquos
sudden course correction changing the
direction o an entire flock alternatingDemocrat and Republican control oCongress and so on
Social circuitry
In the abstract Nature has good reasonto avor complexity and hierarchy ndash eachin its own way makes the tasks o compre-hension prediction and strategizingeasier But its a third concept circuitsthat grounds Flack and Krakauerrsquos teamand lays the practical oundation ormuch o their work
Usually the word ldquocircuitrdquo conjuresimages o the transistors microprocessors
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10 Santa Fe Institute Bulletin Vol 28
Time series representation of the dis tribution of fight sizes in a macaque society Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once An outburst by an individual wonrsquot normally tip the scales But when the system is perched at
a phase transition a brawl among many individuals can erupt
and lengths o copper wire that make upa computer Te analogy is apt Like anelectronic circuit individual components
ndash genes organs people ndash inormational-ly bind living things to orm a kind obiological or social computer In act the
circuit approach to describing a systemstems rom a hunch that the hierarchicalscales present throughout nature ldquoarise
Time
F i g h
t 1
F i g h
t 2
Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once
Vd
Pa
Je
Cb
Yv
Ta
Sg
Qs
Po
Mv
Ip
Fp
Eo
Dh
Tr
Pr
Fc
Zu
Vf
Fp
Ec
Zu
Zr
Iw
Is
Fp
Fc
Cc
Yv
Cd
Ta
Cc
Qv
Ip
Cb
Pa
Eb
Sb
Jc
Je
Hp
Lr
Jc
Zu
Po
Dh
Vf
Eo
Eo
Cd
Pa
Dh
Hh
At
Zu
Yv
Th
Ta
Fn
through a process o collective computa-tionrdquo creating slow-changing predictablesocial and biological structures Flack says
Circuits are more than just an analo-gy though ndash theyrsquore the tools that bridgethe gap between individual and collective
behavior And in a scientific field whereitrsquos easy to avoid real data circuits are one
way Flack and Krakauerrsquos research group
makes sure their theories are a good matchto the real world ldquoOur group is committedto an empirical approachrdquo Flack says ldquoWebelieve that only when these measures aredeveloped with an understanding o thedata generated by real systems will they
be useulrdquoTe process o building circuits begins
by analyzing how a systemrsquos individual parts work together Using the macaquefight data Flack Krakauer and ormerSFI Omidyar Fellow Simon DeDeo (nowat Indiana University Bloomington) devel-oped a statistical method they dubbedinductive game theory to analyze howthe monkeys reacted to othersrsquo fights Teresulting social circuit Flack says servesas a detailed model ldquoor how the micro-
scopic behavior maps to the unctionallyimportant macroscopic eatures o socialstructurerdquo such as the distribution o fightsizes In other words to construct a socialor biological circuit is to understand how agroup builds and maintains stable predict-able inormation hierarchies
Te final step is to produce a simplifiedsocial circuit what the researchers call a
ldquocognitive effective theoryrdquo that accurate-ly predicts how groups behave Te aimis to extract the key sorts o interactions
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 11
responsible or power structures fight-sizedistributions or other macroscopic eaturesusing ldquowhat we know about individual orcomponent cognition to coarse grain orcompressrdquo social circuits Flack says Suchcompression is essential she says because
living things canrsquot base their decisions on what ever y other living thing is doing instead theyrsquore orced to pay attention to
just a ew patterns or details o whatrsquos goingon around them
Te key question here as collaboratorand Princeton University graduate studentEleanor Brush puts it is how little inor-mation individuals need to successullyoutsmart others
Accidental or inevitable
Answering questions like that one ndash or testingsome o the teamrsquos more abstract predic-tions ndash remains a central challenge Poonor example describes his studies o electioncycles and policy change as ldquotoy modelsrdquo ndashthey capture qualitative eatures o the datasuch as party switching but donrsquot stand upto more precise quantitative tests
Meanwhile Krakauer and others sayitrsquos not always clear how to test particularhypotheses such as the prediction that basismismatch leads to ever-increasing complexity
in living things ldquoWersquore still looking or somecompelling examplerdquo Ellison says ldquoPart othe issue is that one can ofen play the devilrsquosadvocate and call into question the examplerdquoone reason why his work to construct ormal
precise measures o complexity is so import-ant he says
New techniques or rapidly analyzinggenetic data Krakauer says might improvethe situation Combining those techniques
with laboratory-based ldquoexperimental evolutionrdquo
in which researchers study the effects o preciseenvironmental changes on small organismssuch as bacteria could help test some o theendeavorrsquos core ideas such as the reflection
principle or the role o phase transitionsAnother potential avenue is to use ldquodigital
sources like computer games where we cancontrol to a large extent the orm o the dataor the conditions under which they werecollectedrdquo Krakauer says
esting their theories is just one part o theteamrsquos ambitious aims Tey hope Flack says
People gather in Washington DC before Martin Luther King Jrrsquos ldquoI Have a Dreamrdquo speech on Aug 28 1963 to demand equalities
Individuals tend to move faster and be less predictable than the slow-moving institutions they create
to achieve nothing less than an understandingo why lie is organized the way it is rom thesmallest bacteria to the largest human insti-tutions Tat requires combining real-worldobservation and abstract mathematical theoryin novel and creative ways
And as i that wasnrsquot enough Krakauerhas one more question in mind is lie anaccident or is it inevitable And i lie isinevitable well are we alone
ldquoI itrsquos not a product o a series o random
accidents but therersquos an underlying law-likeregularity that would give us confidence inbelieving in the possibility o lie presenteverywhere in the universerdquo he says ldquoSo
when one asks why does it matter whetheritrsquos chance or necessity it matters i we care
whether wersquore alone are notrdquo
Nathan Collins is a feelance science writernew ather and film aficionado based inSan Francisco
ldquoLiving things canrsquot base their decisions on what
every other living thing is doing instead theyrsquore
forced to pay attention to just a few patternsrdquo
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12 Santa Fe Institute Bulletin Vol 28
ldquohellipin mere Time all things follow
one another and in mere Space
all things are side by side it is
accordingly only by the combi-
nation of Time and Space thatthe representation of coexistence
arisesrdquo
mdash ARTHUR SCHOPENHAUER On the Fourfold Root
of the Principle of Sufficient Reason 1813
Yves Tanguy Indefinite Divisibility 1942
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 13
D I A C O M M O N S I N S E T H E N R I R O U S S E A U T H E
D R E A M 1 9 1 0 W I K I M E D I A C O M M O N S
Biological systems ndash rom cells totissues to individuals to societ-ies ndash are hierarchically organized
(eg Feldman amp Eschel 1982 Buss 1987Smith amp Szathmaacutery 1998 Valentineamp May 1996 Michod 2000 Frank2003) o many hierarchical organiza-tion suggests the nesting o components orindividuals into groups with these groupsaggregating into yet larger groups But this view ndash at least superficially ndash privilegesspace and matter over time and inorma-tion Many types o neural coding orexample require averaging or summing over neural firing ratesTe neuronsrsquo spatial location ndash that they are in proximity ndash iso course important but at least as important to the encoding
The explanation for the complex multi-scale structure of biological and social systems
lies in their manipulation of space and time to reduce uncertainty about the future
is their behavior in time Likewise insome monkey societies as I will discussin detail later in this review individualsestimate the uture cost o social inter-action by encoding the average outcomeo past interactions in special signals and
then summing over these signalsIn both examples inormation rom
events distributed in time as well as space(Figure 1) is captured with encodingsthat are used to control some behavioraloutput My collaborators and I in theCenter or Complexity amp Collective
Computation are exploring the idea that hierarchical organiza-tion at its core is a nesting o these kinds o unctional encodingsAs I will explain we think these unctional encodings result
LIFErsquoS INFORMATION HIERARCHY
BY JESSICA C FLACK
CO-DIRECTOR CENTER FOR COMPLEXITY amp COLLECTIVE COMPUTATION
WISCONSIN INSTITUTE FOR DISCOVERY UNIVERSITY OF WISCONSIN-MADISON
EXTERNAL PROFESSOR SANTA FE INSTITUTE
Jessica Flack
Number of spatial dimensions
N u m b e
r o f t i m e d i m e n s i o n s
0
0
1
2
3
4
5
1 2 3 4 5
UNSTABLE
TOO SIMPLE
UNPREDICTABLE (ELLIPTIC)
UNPREDICTABLE(ULTRAHYPERBOLIC)
TACHYONSONLY
UNSTABLE
Figure 1 The dimensionality of the time-space
continuum with properties postulated when x
does not equal 3 and y is larger than 1 Life on
earth exist s in three spatial dimensions and one
temporal dimension Biological systems effec-
tively ldquodiscretizerdquo time and space to reduce
environmental uncertainty by coarse-graining
and compressing environmental time series to
find regularities Components use the coarse-
grained descriptions to predict the future
tuning their behavior to their predictions
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rom biological systemsmanipulating space andtime (Figure 2) to acilitateinormation extraction which in turn acilitatesmore efficient extraction
o energy h i s inormation
hierarchy appears to bea universal property obiological systems andmay be the key to one oliersquos greatest mysteries ndashthe origins o biologicalcomplexity In this essayI review a body o work byDavid Krakauer myseland our research group
that has been inspiredby many years o work atthe Santa Fe Institute (eg Crutchfield 1994 Gell-Mann1996 Gell-Mann amp Lloyd 1996 Fontana amp Buss 1996 West Brown amp Enquist 1997 Fontana amp Schuster 1998Ancel amp Fontana 2000 Stadler Stadler Wagner amp Fontana2001 Smith 2003 Crutchfield amp Goumlrnerup 2006 Smith2008) Our work suggests that complexity and the multi-scalestructure o biological systems are the predictable outcomeo evolutionary dynamics driven by uncertainty minimiza-tion (Krakauer 2011 Flack 2012 Flack Erwin Elliot ampKrakauer 2013)
Tis recasting o the evolutionary process as an inerentialone1 (Bergstrom amp Rosvall 2009 Krakauer 2011) is based onthe premise that organisms and other biological systems canbe viewed as hypotheses about the present and uture environ-ments they or their offspring will encounter induced rom thehistory o past environmental states they or their ancestors have
experienced (eg Crutchfield amp Feldman 2001 Krakauer ampZannotto 2009 Ellison Flack amp Krakauer in prep) Tis premise o course only holds i the past is prologue ndash that ishas regularities and the regularities can be estimated and evenmanipulated (as in niche construction) by biological systems ortheir components to produce adaptive behavior (Flack ErwinElliot amp Krakauer 2013 Ellison Flack amp Krakauer in prep)
I these premises are correct lie at its core is computa-tional and a central question becomes How do systems andtheir components estimate and control the regularity in theirenvironments and use these estimates to tune their strategiesI suggest that the answer to this question and the explanation
or complexity is that biological systems manipulate spatialand temporal structure to produce order ndash low variance ndash atlocal scales
UNCERTAINTY REDUCTION
Te story I want to tell starts with the observation that witheach new level o organization typically comes new unction-ality ndash a new eature with positive payoff consequences or thesystem as a whole or or its components (Flack Erwin Elliot
1 Tis idea is related to work on Maxwel lrsquos Demon (eg Krakauer2011 Mandal 983121uan amp Jarzynski 2013) and the Carnot cycle ( egSmith 2003) but we do not yet understand the mapping
Figure 2 Biological systems ndash from
(left to right ) Volvox colonies to slime
molds to animal societies to large-scale
ecosystems such as reefs t o human
cities ndash are hierarchically organized with
multiple functionally important time and
space scales All feature 1) components
with only partially aligned interests
exhibiting coherent behavior at the
aggregate level 2) components that turn
over and that co-exist in the sys tem at
varying stages of development
3) social structure that persists but
component behavior that fluctuates and
4) macroscopic variation in temporal
and spatial structure and coupling with
microscopic behavior which has func-
tional impl ications when the components
can perceive ndash in evolutionary develop-
mental or ecological time ndash regularities
at the macroscopic scale
1 2
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April 2014 Santa Fe Institute Bulletin 15
amp Krakauer 2013) Policing in a pigtailed macaque group is anexample Once a heavy tailed distribution o power ndash defined asthe degree o consensus in the group that an individual can winfights (see Flack amp Krakauer 2006 Boehm amp Flack 2010 BrushKrakauer amp Flack2013) ndash becomes effec-
tively institutionalized(here meaning hard tochange) policing (anintrinsically costlystrategy) becomesaffordable at least tothose animals that sitin the tail o the powerdistribution thosesuper powerul monkeys who are rarely or never challenged when they break up fights (Flack de Waal amp Krakauer 2005Flack Girvan de Waal amp Krakauer 2006)
My collaborators and I propose that a primary driver o theemergence o new unctionality such as policing is the reduc-tion o environmental uncertainty through the constructiono nested dynamical processes with a range o characteris-tic time constants (Flack Erwin Elliot amp Krakauer 2013)Tese nested dynamical processes arise as components extractregularities rom ast microscopic behavior by coarse-grain-ing (or compressing) the history o events to which they havebeen exposed
Proteins or example can have a long hal-lie relative toRNA transcripts and can be thought o as the summed outputo translation Cells have a long hal-lie relative to proteins
and are a unction o the summed output o arrays o spatiallystructured proteins Both proteins and cells represent someaverage measure o the noisier activity o their constituentsSimilarly a pigtailed macaquersquos estimate o its power is a kind
o average measure othe collective percep-
tion in the groupthat the macaque iscapable o winningights and this is abetter predictor othe cost the macaque will pay during fightsthan the outcome oany single melee as
these outcomes can fluctuate or contextual reasons Tesecoarse-grainings or averages are encoded as slow variables(Flack amp de Waal 2007 Flack 2012 Flack Erwin Elliot
amp Krakauer 2013 see also Feret Danos Krivine Harner ampFontana 2009 or a similar idea) Slow variables may have aspatial component as well as a temporal component as in the protein and cell examples (Figure 6) or minimally only atemporal component as in the monkey example
As a consequence o integrating over abundant microscopic processes slow variables provide better predictors o the localuture configuration o a system than the states o the fluctuatingmicroscopic components In doing so they promote acceleratedrates o microscopic adaptation Slow variables acilitate adapta-tion in two ways Tey allow components to fine-tune theirbehavior and they ree components to search at low cost a larger
3 4
Slow variables provide better predictors
of the local future configuration of a
system than the states of the fluctuating
microscopic componentsrdquo
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Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and
proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since
fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous
starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of
relevant genes and proteins and the output is the target phenot ypic feature
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 17
space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
8122019 How Life Got Complex
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 19
A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
8122019 How Life Got Complex
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
8122019 How Life Got Complex
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22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2528
April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
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8122019 How Life Got Complex
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8122019 How Life Got Complex
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8 Santa Fe Institute Bulletin Vol 28
Watch David Krakauerrsquos interview at
wwwsantafeedulife
What does Nature care about hearts brains and other organs or for that
matter political parties ndash in other words structures within structures
ldquoWersquod like to understand the implica-tions this has or the environmentrdquo hesays ldquoFor example do simple or complexorganisms experience and live in simple orcomplex environmentsrdquo
Working with Flack and KrakauerEllison developed ldquoMarkov organismsrdquo
computer-simulated creatures that merge
insights rom biology with inormation processing techniques rom computerscience to help figure out how lie balancesthese trade-offs Rather than modeling real
organisms themselves he ocuses on howinormation flows in the ecological system
Itrsquos early days Ellison says but his simula-tions suggest that lie will ofen evolve tomatch its environmentrsquos complexity ndash findingsthat are in line with the reflection principlebut with some interesting caveats For one
thing evolving Markov organisms tend toovershoot their worldsrsquocomplexity and mighttake a long time to pruneunnecessary complexities
Teyrsquore also suscepti-ble to ldquobasis mismatchrdquo a
problem you know welli yoursquove ever tried to explain to a tourist howto get around in your hometown o youthere are just a ew steps but to the noviceitrsquos a complex process with many twists and
turns and every intersection represents a possible misdirection in sprawling cities likeLos Angeles a direction as simple as ldquotakeSunset to Vermont and turn lefrdquo becomesinfinitely complex Markov organisms arethe same i their way o solving a problem
doesnrsquot line up with how their environmentsconstructed it Ellisonrsquos simulations showMarkov organismsrsquo complexity keeps evolv-ing upward orever
But Ellisonrsquos inormation-centricapproach has some benefits One he says
ldquois that it attempts to answer the questiono how complexity evolves in an organ-ism-independent ashionrdquo meaning thatthe ideas apply equally well to anythingrom bacteria to politics Similarly Ellisonrsquosmethod allows the team to describe both anorganism and its environmentrsquos complexity
in the same terms because both derive romthe same underlying models o inormation
processing Surprisingly thatrsquos somethingew i any other researchers have done
Constructing predictability
So it appears Nature might avor multicel-lular lie i it affords a certain computational power not readily available to single-celledorganisms But what does Nature care abouthearts brains and other organs or or thatmatter political parties ndash in other words
structures within structureshe answer Flack says is that living
things like their worlds to be predictableand what makes cells and people more likelyto survive Nature avors
Much o the structure we observe in the world Flack says probably evolved becausestructure begets stability hence predictabilityGroups o genes cells or animals changetheir collective behaviors slowly compared
with individual genes cells or animals givingthe aster-moving individual components a
chance to anticipate changes more easilyBiologists call the idea that plants andanimals ndash and genes and organs and so on
ndash structure their environments to be morestable and predictable ldquoniche constructionrdquoand it usually applies to physical structurelike ants building nests But it also can beapplied to temporal structure
Politics offers perhaps a simple exampleEarly US politicians were explicit aboutdesigning Congress and the rest o govern-ment so that it would change gradually and
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 1128
April 2014 Santa Fe Institute Bulletin 9April 2014 Santa Fe Institute Bulletin 9
According to the reflection principle organisms are reflections of their environments Here the environment is represented as a prose excerpt from
Darwinrsquos Origin of Species wherein he contemplates an entangled bank filled with ldquoendless forms most beautiful and most wonderfulrdquo each evolved through natural selection Organism A matches the environmentrsquos complexity while Organism B is less complex than the environment and Organism C is
more complex than the environment The research team is asking when evolution satisfies or v iolates the reflection principle
be more stable Even when we complainabout our slow-moving government weare undoubtedly comorted by that verycharacteristic because slow is predictableAnd when our environment is predictable we know how to make sound decisions
So people construct relatively slow-mov-ing predictable institutions At the sametime those institutions help shape thebehavior o the individuals who createdthem points out C4 postdoctoral ellowPhilip Poon who is examining eedbackrom institutions to explain (in the govern-ment case) why or example Democrats andRepublicans seem to trade off controllingthe White House and Congress every ewterms A critical issue is o course howthat eedback works rom a mechanical
perspective ndash that is how the ways individ-uals perceive and understand institutionsinfluence their decision-making
Drawing on the theory o phase transi-tions the same one that explains whychanging a seemingly minor variable cansuddenly shif an entire system rom onestate to another Flack Krakauer and C4researcher and ormer SFI PostdoctoralFellow Bryan Daniels argue that hierarchi-cal structures bestow another advantageeicient inormation low rom the
collective to its individual parts Systems perched on the edge o a phase transitionare exquisitely sensitive so that a small orlocalized change ldquoleads to a large change inthe global dynamicsrdquo Daniels says Toughthat might seem unstable or chaotic systems
near the critical point where a phase transi-
tion begins are actually quite predictableGroups o macaque monkeys ndash one o
Flack and the teamrsquos earliest sources o data
and inspiration ndash are one system that appearsto be resting near a phase transition Whenmonkeys arenrsquot eeling especially aggres-sive Daniels says things are stable and ldquoi Isuddenly act out nothingrsquos going to happenhellipbut i [the group is] sitting at the critical
point then [my] contribution is always moreimportantrdquo and one extra monkey picking afight is enough to kick off a large-scale brawl
Below the critical point individualmonkeys act airly independently but rightat the transition their behavior is tightly
ldquoWhen monkeys arenrsquot feeling especially aggressive
things are stable but if the group is sitting at the
critical point one extra monkey picking a fight
is enough to kick off a large-scale brawlrdquo
coordinated and individual monkeys acttogether as one Tat Krakauer empha-sizes eases the flow o inormation romthe system as a whole to its constituent
parts making it all the more predictableNature is rie with examples one birdrsquos
sudden course correction changing the
direction o an entire flock alternatingDemocrat and Republican control oCongress and so on
Social circuitry
In the abstract Nature has good reasonto avor complexity and hierarchy ndash eachin its own way makes the tasks o compre-hension prediction and strategizingeasier But its a third concept circuitsthat grounds Flack and Krakauerrsquos teamand lays the practical oundation ormuch o their work
Usually the word ldquocircuitrdquo conjuresimages o the transistors microprocessors
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 1228
10 Santa Fe Institute Bulletin Vol 28
Time series representation of the dis tribution of fight sizes in a macaque society Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once An outburst by an individual wonrsquot normally tip the scales But when the system is perched at
a phase transition a brawl among many individuals can erupt
and lengths o copper wire that make upa computer Te analogy is apt Like anelectronic circuit individual components
ndash genes organs people ndash inormational-ly bind living things to orm a kind obiological or social computer In act the
circuit approach to describing a systemstems rom a hunch that the hierarchicalscales present throughout nature ldquoarise
Time
F i g h
t 1
F i g h
t 2
Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once
Vd
Pa
Je
Cb
Yv
Ta
Sg
Qs
Po
Mv
Ip
Fp
Eo
Dh
Tr
Pr
Fc
Zu
Vf
Fp
Ec
Zu
Zr
Iw
Is
Fp
Fc
Cc
Yv
Cd
Ta
Cc
Qv
Ip
Cb
Pa
Eb
Sb
Jc
Je
Hp
Lr
Jc
Zu
Po
Dh
Vf
Eo
Eo
Cd
Pa
Dh
Hh
At
Zu
Yv
Th
Ta
Fn
through a process o collective computa-tionrdquo creating slow-changing predictablesocial and biological structures Flack says
Circuits are more than just an analo-gy though ndash theyrsquore the tools that bridgethe gap between individual and collective
behavior And in a scientific field whereitrsquos easy to avoid real data circuits are one
way Flack and Krakauerrsquos research group
makes sure their theories are a good matchto the real world ldquoOur group is committedto an empirical approachrdquo Flack says ldquoWebelieve that only when these measures aredeveloped with an understanding o thedata generated by real systems will they
be useulrdquoTe process o building circuits begins
by analyzing how a systemrsquos individual parts work together Using the macaquefight data Flack Krakauer and ormerSFI Omidyar Fellow Simon DeDeo (nowat Indiana University Bloomington) devel-oped a statistical method they dubbedinductive game theory to analyze howthe monkeys reacted to othersrsquo fights Teresulting social circuit Flack says servesas a detailed model ldquoor how the micro-
scopic behavior maps to the unctionallyimportant macroscopic eatures o socialstructurerdquo such as the distribution o fightsizes In other words to construct a socialor biological circuit is to understand how agroup builds and maintains stable predict-able inormation hierarchies
Te final step is to produce a simplifiedsocial circuit what the researchers call a
ldquocognitive effective theoryrdquo that accurate-ly predicts how groups behave Te aimis to extract the key sorts o interactions
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 1328
April 2014 Santa Fe Institute Bulletin 11
responsible or power structures fight-sizedistributions or other macroscopic eaturesusing ldquowhat we know about individual orcomponent cognition to coarse grain orcompressrdquo social circuits Flack says Suchcompression is essential she says because
living things canrsquot base their decisions on what ever y other living thing is doing instead theyrsquore orced to pay attention to
just a ew patterns or details o whatrsquos goingon around them
Te key question here as collaboratorand Princeton University graduate studentEleanor Brush puts it is how little inor-mation individuals need to successullyoutsmart others
Accidental or inevitable
Answering questions like that one ndash or testingsome o the teamrsquos more abstract predic-tions ndash remains a central challenge Poonor example describes his studies o electioncycles and policy change as ldquotoy modelsrdquo ndashthey capture qualitative eatures o the datasuch as party switching but donrsquot stand upto more precise quantitative tests
Meanwhile Krakauer and others sayitrsquos not always clear how to test particularhypotheses such as the prediction that basismismatch leads to ever-increasing complexity
in living things ldquoWersquore still looking or somecompelling examplerdquo Ellison says ldquoPart othe issue is that one can ofen play the devilrsquosadvocate and call into question the examplerdquoone reason why his work to construct ormal
precise measures o complexity is so import-ant he says
New techniques or rapidly analyzinggenetic data Krakauer says might improvethe situation Combining those techniques
with laboratory-based ldquoexperimental evolutionrdquo
in which researchers study the effects o preciseenvironmental changes on small organismssuch as bacteria could help test some o theendeavorrsquos core ideas such as the reflection
principle or the role o phase transitionsAnother potential avenue is to use ldquodigital
sources like computer games where we cancontrol to a large extent the orm o the dataor the conditions under which they werecollectedrdquo Krakauer says
esting their theories is just one part o theteamrsquos ambitious aims Tey hope Flack says
People gather in Washington DC before Martin Luther King Jrrsquos ldquoI Have a Dreamrdquo speech on Aug 28 1963 to demand equalities
Individuals tend to move faster and be less predictable than the slow-moving institutions they create
to achieve nothing less than an understandingo why lie is organized the way it is rom thesmallest bacteria to the largest human insti-tutions Tat requires combining real-worldobservation and abstract mathematical theoryin novel and creative ways
And as i that wasnrsquot enough Krakauerhas one more question in mind is lie anaccident or is it inevitable And i lie isinevitable well are we alone
ldquoI itrsquos not a product o a series o random
accidents but therersquos an underlying law-likeregularity that would give us confidence inbelieving in the possibility o lie presenteverywhere in the universerdquo he says ldquoSo
when one asks why does it matter whetheritrsquos chance or necessity it matters i we care
whether wersquore alone are notrdquo
Nathan Collins is a feelance science writernew ather and film aficionado based inSan Francisco
ldquoLiving things canrsquot base their decisions on what
every other living thing is doing instead theyrsquore
forced to pay attention to just a few patternsrdquo
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12 Santa Fe Institute Bulletin Vol 28
ldquohellipin mere Time all things follow
one another and in mere Space
all things are side by side it is
accordingly only by the combi-
nation of Time and Space thatthe representation of coexistence
arisesrdquo
mdash ARTHUR SCHOPENHAUER On the Fourfold Root
of the Principle of Sufficient Reason 1813
Yves Tanguy Indefinite Divisibility 1942
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 13
D I A C O M M O N S I N S E T H E N R I R O U S S E A U T H E
D R E A M 1 9 1 0 W I K I M E D I A C O M M O N S
Biological systems ndash rom cells totissues to individuals to societ-ies ndash are hierarchically organized
(eg Feldman amp Eschel 1982 Buss 1987Smith amp Szathmaacutery 1998 Valentineamp May 1996 Michod 2000 Frank2003) o many hierarchical organiza-tion suggests the nesting o components orindividuals into groups with these groupsaggregating into yet larger groups But this view ndash at least superficially ndash privilegesspace and matter over time and inorma-tion Many types o neural coding orexample require averaging or summing over neural firing ratesTe neuronsrsquo spatial location ndash that they are in proximity ndash iso course important but at least as important to the encoding
The explanation for the complex multi-scale structure of biological and social systems
lies in their manipulation of space and time to reduce uncertainty about the future
is their behavior in time Likewise insome monkey societies as I will discussin detail later in this review individualsestimate the uture cost o social inter-action by encoding the average outcomeo past interactions in special signals and
then summing over these signalsIn both examples inormation rom
events distributed in time as well as space(Figure 1) is captured with encodingsthat are used to control some behavioraloutput My collaborators and I in theCenter or Complexity amp Collective
Computation are exploring the idea that hierarchical organiza-tion at its core is a nesting o these kinds o unctional encodingsAs I will explain we think these unctional encodings result
LIFErsquoS INFORMATION HIERARCHY
BY JESSICA C FLACK
CO-DIRECTOR CENTER FOR COMPLEXITY amp COLLECTIVE COMPUTATION
WISCONSIN INSTITUTE FOR DISCOVERY UNIVERSITY OF WISCONSIN-MADISON
EXTERNAL PROFESSOR SANTA FE INSTITUTE
Jessica Flack
Number of spatial dimensions
N u m b e
r o f t i m e d i m e n s i o n s
0
0
1
2
3
4
5
1 2 3 4 5
UNSTABLE
TOO SIMPLE
UNPREDICTABLE (ELLIPTIC)
UNPREDICTABLE(ULTRAHYPERBOLIC)
TACHYONSONLY
UNSTABLE
Figure 1 The dimensionality of the time-space
continuum with properties postulated when x
does not equal 3 and y is larger than 1 Life on
earth exist s in three spatial dimensions and one
temporal dimension Biological systems effec-
tively ldquodiscretizerdquo time and space to reduce
environmental uncertainty by coarse-graining
and compressing environmental time series to
find regularities Components use the coarse-
grained descriptions to predict the future
tuning their behavior to their predictions
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14 Santa Fe Institute Bulletin Vol 28
rom biological systemsmanipulating space andtime (Figure 2) to acilitateinormation extraction which in turn acilitatesmore efficient extraction
o energy h i s inormation
hierarchy appears to bea universal property obiological systems andmay be the key to one oliersquos greatest mysteries ndashthe origins o biologicalcomplexity In this essayI review a body o work byDavid Krakauer myseland our research group
that has been inspiredby many years o work atthe Santa Fe Institute (eg Crutchfield 1994 Gell-Mann1996 Gell-Mann amp Lloyd 1996 Fontana amp Buss 1996 West Brown amp Enquist 1997 Fontana amp Schuster 1998Ancel amp Fontana 2000 Stadler Stadler Wagner amp Fontana2001 Smith 2003 Crutchfield amp Goumlrnerup 2006 Smith2008) Our work suggests that complexity and the multi-scalestructure o biological systems are the predictable outcomeo evolutionary dynamics driven by uncertainty minimiza-tion (Krakauer 2011 Flack 2012 Flack Erwin Elliot ampKrakauer 2013)
Tis recasting o the evolutionary process as an inerentialone1 (Bergstrom amp Rosvall 2009 Krakauer 2011) is based onthe premise that organisms and other biological systems canbe viewed as hypotheses about the present and uture environ-ments they or their offspring will encounter induced rom thehistory o past environmental states they or their ancestors have
experienced (eg Crutchfield amp Feldman 2001 Krakauer ampZannotto 2009 Ellison Flack amp Krakauer in prep) Tis premise o course only holds i the past is prologue ndash that ishas regularities and the regularities can be estimated and evenmanipulated (as in niche construction) by biological systems ortheir components to produce adaptive behavior (Flack ErwinElliot amp Krakauer 2013 Ellison Flack amp Krakauer in prep)
I these premises are correct lie at its core is computa-tional and a central question becomes How do systems andtheir components estimate and control the regularity in theirenvironments and use these estimates to tune their strategiesI suggest that the answer to this question and the explanation
or complexity is that biological systems manipulate spatialand temporal structure to produce order ndash low variance ndash atlocal scales
UNCERTAINTY REDUCTION
Te story I want to tell starts with the observation that witheach new level o organization typically comes new unction-ality ndash a new eature with positive payoff consequences or thesystem as a whole or or its components (Flack Erwin Elliot
1 Tis idea is related to work on Maxwel lrsquos Demon (eg Krakauer2011 Mandal 983121uan amp Jarzynski 2013) and the Carnot cycle ( egSmith 2003) but we do not yet understand the mapping
Figure 2 Biological systems ndash from
(left to right ) Volvox colonies to slime
molds to animal societies to large-scale
ecosystems such as reefs t o human
cities ndash are hierarchically organized with
multiple functionally important time and
space scales All feature 1) components
with only partially aligned interests
exhibiting coherent behavior at the
aggregate level 2) components that turn
over and that co-exist in the sys tem at
varying stages of development
3) social structure that persists but
component behavior that fluctuates and
4) macroscopic variation in temporal
and spatial structure and coupling with
microscopic behavior which has func-
tional impl ications when the components
can perceive ndash in evolutionary develop-
mental or ecological time ndash regularities
at the macroscopic scale
1 2
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April 2014 Santa Fe Institute Bulletin 15
amp Krakauer 2013) Policing in a pigtailed macaque group is anexample Once a heavy tailed distribution o power ndash defined asthe degree o consensus in the group that an individual can winfights (see Flack amp Krakauer 2006 Boehm amp Flack 2010 BrushKrakauer amp Flack2013) ndash becomes effec-
tively institutionalized(here meaning hard tochange) policing (anintrinsically costlystrategy) becomesaffordable at least tothose animals that sitin the tail o the powerdistribution thosesuper powerul monkeys who are rarely or never challenged when they break up fights (Flack de Waal amp Krakauer 2005Flack Girvan de Waal amp Krakauer 2006)
My collaborators and I propose that a primary driver o theemergence o new unctionality such as policing is the reduc-tion o environmental uncertainty through the constructiono nested dynamical processes with a range o characteris-tic time constants (Flack Erwin Elliot amp Krakauer 2013)Tese nested dynamical processes arise as components extractregularities rom ast microscopic behavior by coarse-grain-ing (or compressing) the history o events to which they havebeen exposed
Proteins or example can have a long hal-lie relative toRNA transcripts and can be thought o as the summed outputo translation Cells have a long hal-lie relative to proteins
and are a unction o the summed output o arrays o spatiallystructured proteins Both proteins and cells represent someaverage measure o the noisier activity o their constituentsSimilarly a pigtailed macaquersquos estimate o its power is a kind
o average measure othe collective percep-
tion in the groupthat the macaque iscapable o winningights and this is abetter predictor othe cost the macaque will pay during fightsthan the outcome oany single melee as
these outcomes can fluctuate or contextual reasons Tesecoarse-grainings or averages are encoded as slow variables(Flack amp de Waal 2007 Flack 2012 Flack Erwin Elliot
amp Krakauer 2013 see also Feret Danos Krivine Harner ampFontana 2009 or a similar idea) Slow variables may have aspatial component as well as a temporal component as in the protein and cell examples (Figure 6) or minimally only atemporal component as in the monkey example
As a consequence o integrating over abundant microscopic processes slow variables provide better predictors o the localuture configuration o a system than the states o the fluctuatingmicroscopic components In doing so they promote acceleratedrates o microscopic adaptation Slow variables acilitate adapta-tion in two ways Tey allow components to fine-tune theirbehavior and they ree components to search at low cost a larger
3 4
Slow variables provide better predictors
of the local future configuration of a
system than the states of the fluctuating
microscopic componentsrdquo
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16 Santa Fe Institute Bulletin Vol 28
Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and
proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since
fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous
starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of
relevant genes and proteins and the output is the target phenot ypic feature
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 17
space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
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April 2014 Santa Fe Institute Bulletin 19
A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
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April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
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22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2528
April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
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foundations If you believe
complex systems science can
generate the insights that matter
most for science and society makea gift today Visit our website at
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ITrsquoS SIMPLE
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ComplexityScience
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2828
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 1128
April 2014 Santa Fe Institute Bulletin 9April 2014 Santa Fe Institute Bulletin 9
According to the reflection principle organisms are reflections of their environments Here the environment is represented as a prose excerpt from
Darwinrsquos Origin of Species wherein he contemplates an entangled bank filled with ldquoendless forms most beautiful and most wonderfulrdquo each evolved through natural selection Organism A matches the environmentrsquos complexity while Organism B is less complex than the environment and Organism C is
more complex than the environment The research team is asking when evolution satisfies or v iolates the reflection principle
be more stable Even when we complainabout our slow-moving government weare undoubtedly comorted by that verycharacteristic because slow is predictableAnd when our environment is predictable we know how to make sound decisions
So people construct relatively slow-mov-ing predictable institutions At the sametime those institutions help shape thebehavior o the individuals who createdthem points out C4 postdoctoral ellowPhilip Poon who is examining eedbackrom institutions to explain (in the govern-ment case) why or example Democrats andRepublicans seem to trade off controllingthe White House and Congress every ewterms A critical issue is o course howthat eedback works rom a mechanical
perspective ndash that is how the ways individ-uals perceive and understand institutionsinfluence their decision-making
Drawing on the theory o phase transi-tions the same one that explains whychanging a seemingly minor variable cansuddenly shif an entire system rom onestate to another Flack Krakauer and C4researcher and ormer SFI PostdoctoralFellow Bryan Daniels argue that hierarchi-cal structures bestow another advantageeicient inormation low rom the
collective to its individual parts Systems perched on the edge o a phase transitionare exquisitely sensitive so that a small orlocalized change ldquoleads to a large change inthe global dynamicsrdquo Daniels says Toughthat might seem unstable or chaotic systems
near the critical point where a phase transi-
tion begins are actually quite predictableGroups o macaque monkeys ndash one o
Flack and the teamrsquos earliest sources o data
and inspiration ndash are one system that appearsto be resting near a phase transition Whenmonkeys arenrsquot eeling especially aggres-sive Daniels says things are stable and ldquoi Isuddenly act out nothingrsquos going to happenhellipbut i [the group is] sitting at the critical
point then [my] contribution is always moreimportantrdquo and one extra monkey picking afight is enough to kick off a large-scale brawl
Below the critical point individualmonkeys act airly independently but rightat the transition their behavior is tightly
ldquoWhen monkeys arenrsquot feeling especially aggressive
things are stable but if the group is sitting at the
critical point one extra monkey picking a fight
is enough to kick off a large-scale brawlrdquo
coordinated and individual monkeys acttogether as one Tat Krakauer empha-sizes eases the flow o inormation romthe system as a whole to its constituent
parts making it all the more predictableNature is rie with examples one birdrsquos
sudden course correction changing the
direction o an entire flock alternatingDemocrat and Republican control oCongress and so on
Social circuitry
In the abstract Nature has good reasonto avor complexity and hierarchy ndash eachin its own way makes the tasks o compre-hension prediction and strategizingeasier But its a third concept circuitsthat grounds Flack and Krakauerrsquos teamand lays the practical oundation ormuch o their work
Usually the word ldquocircuitrdquo conjuresimages o the transistors microprocessors
8122019 How Life Got Complex
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10 Santa Fe Institute Bulletin Vol 28
Time series representation of the dis tribution of fight sizes in a macaque society Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once An outburst by an individual wonrsquot normally tip the scales But when the system is perched at
a phase transition a brawl among many individuals can erupt
and lengths o copper wire that make upa computer Te analogy is apt Like anelectronic circuit individual components
ndash genes organs people ndash inormational-ly bind living things to orm a kind obiological or social computer In act the
circuit approach to describing a systemstems rom a hunch that the hierarchicalscales present throughout nature ldquoarise
Time
F i g h
t 1
F i g h
t 2
Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once
Vd
Pa
Je
Cb
Yv
Ta
Sg
Qs
Po
Mv
Ip
Fp
Eo
Dh
Tr
Pr
Fc
Zu
Vf
Fp
Ec
Zu
Zr
Iw
Is
Fp
Fc
Cc
Yv
Cd
Ta
Cc
Qv
Ip
Cb
Pa
Eb
Sb
Jc
Je
Hp
Lr
Jc
Zu
Po
Dh
Vf
Eo
Eo
Cd
Pa
Dh
Hh
At
Zu
Yv
Th
Ta
Fn
through a process o collective computa-tionrdquo creating slow-changing predictablesocial and biological structures Flack says
Circuits are more than just an analo-gy though ndash theyrsquore the tools that bridgethe gap between individual and collective
behavior And in a scientific field whereitrsquos easy to avoid real data circuits are one
way Flack and Krakauerrsquos research group
makes sure their theories are a good matchto the real world ldquoOur group is committedto an empirical approachrdquo Flack says ldquoWebelieve that only when these measures aredeveloped with an understanding o thedata generated by real systems will they
be useulrdquoTe process o building circuits begins
by analyzing how a systemrsquos individual parts work together Using the macaquefight data Flack Krakauer and ormerSFI Omidyar Fellow Simon DeDeo (nowat Indiana University Bloomington) devel-oped a statistical method they dubbedinductive game theory to analyze howthe monkeys reacted to othersrsquo fights Teresulting social circuit Flack says servesas a detailed model ldquoor how the micro-
scopic behavior maps to the unctionallyimportant macroscopic eatures o socialstructurerdquo such as the distribution o fightsizes In other words to construct a socialor biological circuit is to understand how agroup builds and maintains stable predict-able inormation hierarchies
Te final step is to produce a simplifiedsocial circuit what the researchers call a
ldquocognitive effective theoryrdquo that accurate-ly predicts how groups behave Te aimis to extract the key sorts o interactions
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 1328
April 2014 Santa Fe Institute Bulletin 11
responsible or power structures fight-sizedistributions or other macroscopic eaturesusing ldquowhat we know about individual orcomponent cognition to coarse grain orcompressrdquo social circuits Flack says Suchcompression is essential she says because
living things canrsquot base their decisions on what ever y other living thing is doing instead theyrsquore orced to pay attention to
just a ew patterns or details o whatrsquos goingon around them
Te key question here as collaboratorand Princeton University graduate studentEleanor Brush puts it is how little inor-mation individuals need to successullyoutsmart others
Accidental or inevitable
Answering questions like that one ndash or testingsome o the teamrsquos more abstract predic-tions ndash remains a central challenge Poonor example describes his studies o electioncycles and policy change as ldquotoy modelsrdquo ndashthey capture qualitative eatures o the datasuch as party switching but donrsquot stand upto more precise quantitative tests
Meanwhile Krakauer and others sayitrsquos not always clear how to test particularhypotheses such as the prediction that basismismatch leads to ever-increasing complexity
in living things ldquoWersquore still looking or somecompelling examplerdquo Ellison says ldquoPart othe issue is that one can ofen play the devilrsquosadvocate and call into question the examplerdquoone reason why his work to construct ormal
precise measures o complexity is so import-ant he says
New techniques or rapidly analyzinggenetic data Krakauer says might improvethe situation Combining those techniques
with laboratory-based ldquoexperimental evolutionrdquo
in which researchers study the effects o preciseenvironmental changes on small organismssuch as bacteria could help test some o theendeavorrsquos core ideas such as the reflection
principle or the role o phase transitionsAnother potential avenue is to use ldquodigital
sources like computer games where we cancontrol to a large extent the orm o the dataor the conditions under which they werecollectedrdquo Krakauer says
esting their theories is just one part o theteamrsquos ambitious aims Tey hope Flack says
People gather in Washington DC before Martin Luther King Jrrsquos ldquoI Have a Dreamrdquo speech on Aug 28 1963 to demand equalities
Individuals tend to move faster and be less predictable than the slow-moving institutions they create
to achieve nothing less than an understandingo why lie is organized the way it is rom thesmallest bacteria to the largest human insti-tutions Tat requires combining real-worldobservation and abstract mathematical theoryin novel and creative ways
And as i that wasnrsquot enough Krakauerhas one more question in mind is lie anaccident or is it inevitable And i lie isinevitable well are we alone
ldquoI itrsquos not a product o a series o random
accidents but therersquos an underlying law-likeregularity that would give us confidence inbelieving in the possibility o lie presenteverywhere in the universerdquo he says ldquoSo
when one asks why does it matter whetheritrsquos chance or necessity it matters i we care
whether wersquore alone are notrdquo
Nathan Collins is a feelance science writernew ather and film aficionado based inSan Francisco
ldquoLiving things canrsquot base their decisions on what
every other living thing is doing instead theyrsquore
forced to pay attention to just a few patternsrdquo
8122019 How Life Got Complex
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12 Santa Fe Institute Bulletin Vol 28
ldquohellipin mere Time all things follow
one another and in mere Space
all things are side by side it is
accordingly only by the combi-
nation of Time and Space thatthe representation of coexistence
arisesrdquo
mdash ARTHUR SCHOPENHAUER On the Fourfold Root
of the Principle of Sufficient Reason 1813
Yves Tanguy Indefinite Divisibility 1942
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 1528
April 2014 Santa Fe Institute Bulletin 13
D I A C O M M O N S I N S E T H E N R I R O U S S E A U T H E
D R E A M 1 9 1 0 W I K I M E D I A C O M M O N S
Biological systems ndash rom cells totissues to individuals to societ-ies ndash are hierarchically organized
(eg Feldman amp Eschel 1982 Buss 1987Smith amp Szathmaacutery 1998 Valentineamp May 1996 Michod 2000 Frank2003) o many hierarchical organiza-tion suggests the nesting o components orindividuals into groups with these groupsaggregating into yet larger groups But this view ndash at least superficially ndash privilegesspace and matter over time and inorma-tion Many types o neural coding orexample require averaging or summing over neural firing ratesTe neuronsrsquo spatial location ndash that they are in proximity ndash iso course important but at least as important to the encoding
The explanation for the complex multi-scale structure of biological and social systems
lies in their manipulation of space and time to reduce uncertainty about the future
is their behavior in time Likewise insome monkey societies as I will discussin detail later in this review individualsestimate the uture cost o social inter-action by encoding the average outcomeo past interactions in special signals and
then summing over these signalsIn both examples inormation rom
events distributed in time as well as space(Figure 1) is captured with encodingsthat are used to control some behavioraloutput My collaborators and I in theCenter or Complexity amp Collective
Computation are exploring the idea that hierarchical organiza-tion at its core is a nesting o these kinds o unctional encodingsAs I will explain we think these unctional encodings result
LIFErsquoS INFORMATION HIERARCHY
BY JESSICA C FLACK
CO-DIRECTOR CENTER FOR COMPLEXITY amp COLLECTIVE COMPUTATION
WISCONSIN INSTITUTE FOR DISCOVERY UNIVERSITY OF WISCONSIN-MADISON
EXTERNAL PROFESSOR SANTA FE INSTITUTE
Jessica Flack
Number of spatial dimensions
N u m b e
r o f t i m e d i m e n s i o n s
0
0
1
2
3
4
5
1 2 3 4 5
UNSTABLE
TOO SIMPLE
UNPREDICTABLE (ELLIPTIC)
UNPREDICTABLE(ULTRAHYPERBOLIC)
TACHYONSONLY
UNSTABLE
Figure 1 The dimensionality of the time-space
continuum with properties postulated when x
does not equal 3 and y is larger than 1 Life on
earth exist s in three spatial dimensions and one
temporal dimension Biological systems effec-
tively ldquodiscretizerdquo time and space to reduce
environmental uncertainty by coarse-graining
and compressing environmental time series to
find regularities Components use the coarse-
grained descriptions to predict the future
tuning their behavior to their predictions
8122019 How Life Got Complex
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14 Santa Fe Institute Bulletin Vol 28
rom biological systemsmanipulating space andtime (Figure 2) to acilitateinormation extraction which in turn acilitatesmore efficient extraction
o energy h i s inormation
hierarchy appears to bea universal property obiological systems andmay be the key to one oliersquos greatest mysteries ndashthe origins o biologicalcomplexity In this essayI review a body o work byDavid Krakauer myseland our research group
that has been inspiredby many years o work atthe Santa Fe Institute (eg Crutchfield 1994 Gell-Mann1996 Gell-Mann amp Lloyd 1996 Fontana amp Buss 1996 West Brown amp Enquist 1997 Fontana amp Schuster 1998Ancel amp Fontana 2000 Stadler Stadler Wagner amp Fontana2001 Smith 2003 Crutchfield amp Goumlrnerup 2006 Smith2008) Our work suggests that complexity and the multi-scalestructure o biological systems are the predictable outcomeo evolutionary dynamics driven by uncertainty minimiza-tion (Krakauer 2011 Flack 2012 Flack Erwin Elliot ampKrakauer 2013)
Tis recasting o the evolutionary process as an inerentialone1 (Bergstrom amp Rosvall 2009 Krakauer 2011) is based onthe premise that organisms and other biological systems canbe viewed as hypotheses about the present and uture environ-ments they or their offspring will encounter induced rom thehistory o past environmental states they or their ancestors have
experienced (eg Crutchfield amp Feldman 2001 Krakauer ampZannotto 2009 Ellison Flack amp Krakauer in prep) Tis premise o course only holds i the past is prologue ndash that ishas regularities and the regularities can be estimated and evenmanipulated (as in niche construction) by biological systems ortheir components to produce adaptive behavior (Flack ErwinElliot amp Krakauer 2013 Ellison Flack amp Krakauer in prep)
I these premises are correct lie at its core is computa-tional and a central question becomes How do systems andtheir components estimate and control the regularity in theirenvironments and use these estimates to tune their strategiesI suggest that the answer to this question and the explanation
or complexity is that biological systems manipulate spatialand temporal structure to produce order ndash low variance ndash atlocal scales
UNCERTAINTY REDUCTION
Te story I want to tell starts with the observation that witheach new level o organization typically comes new unction-ality ndash a new eature with positive payoff consequences or thesystem as a whole or or its components (Flack Erwin Elliot
1 Tis idea is related to work on Maxwel lrsquos Demon (eg Krakauer2011 Mandal 983121uan amp Jarzynski 2013) and the Carnot cycle ( egSmith 2003) but we do not yet understand the mapping
Figure 2 Biological systems ndash from
(left to right ) Volvox colonies to slime
molds to animal societies to large-scale
ecosystems such as reefs t o human
cities ndash are hierarchically organized with
multiple functionally important time and
space scales All feature 1) components
with only partially aligned interests
exhibiting coherent behavior at the
aggregate level 2) components that turn
over and that co-exist in the sys tem at
varying stages of development
3) social structure that persists but
component behavior that fluctuates and
4) macroscopic variation in temporal
and spatial structure and coupling with
microscopic behavior which has func-
tional impl ications when the components
can perceive ndash in evolutionary develop-
mental or ecological time ndash regularities
at the macroscopic scale
1 2
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 15
amp Krakauer 2013) Policing in a pigtailed macaque group is anexample Once a heavy tailed distribution o power ndash defined asthe degree o consensus in the group that an individual can winfights (see Flack amp Krakauer 2006 Boehm amp Flack 2010 BrushKrakauer amp Flack2013) ndash becomes effec-
tively institutionalized(here meaning hard tochange) policing (anintrinsically costlystrategy) becomesaffordable at least tothose animals that sitin the tail o the powerdistribution thosesuper powerul monkeys who are rarely or never challenged when they break up fights (Flack de Waal amp Krakauer 2005Flack Girvan de Waal amp Krakauer 2006)
My collaborators and I propose that a primary driver o theemergence o new unctionality such as policing is the reduc-tion o environmental uncertainty through the constructiono nested dynamical processes with a range o characteris-tic time constants (Flack Erwin Elliot amp Krakauer 2013)Tese nested dynamical processes arise as components extractregularities rom ast microscopic behavior by coarse-grain-ing (or compressing) the history o events to which they havebeen exposed
Proteins or example can have a long hal-lie relative toRNA transcripts and can be thought o as the summed outputo translation Cells have a long hal-lie relative to proteins
and are a unction o the summed output o arrays o spatiallystructured proteins Both proteins and cells represent someaverage measure o the noisier activity o their constituentsSimilarly a pigtailed macaquersquos estimate o its power is a kind
o average measure othe collective percep-
tion in the groupthat the macaque iscapable o winningights and this is abetter predictor othe cost the macaque will pay during fightsthan the outcome oany single melee as
these outcomes can fluctuate or contextual reasons Tesecoarse-grainings or averages are encoded as slow variables(Flack amp de Waal 2007 Flack 2012 Flack Erwin Elliot
amp Krakauer 2013 see also Feret Danos Krivine Harner ampFontana 2009 or a similar idea) Slow variables may have aspatial component as well as a temporal component as in the protein and cell examples (Figure 6) or minimally only atemporal component as in the monkey example
As a consequence o integrating over abundant microscopic processes slow variables provide better predictors o the localuture configuration o a system than the states o the fluctuatingmicroscopic components In doing so they promote acceleratedrates o microscopic adaptation Slow variables acilitate adapta-tion in two ways Tey allow components to fine-tune theirbehavior and they ree components to search at low cost a larger
3 4
Slow variables provide better predictors
of the local future configuration of a
system than the states of the fluctuating
microscopic componentsrdquo
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Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and
proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since
fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous
starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of
relevant genes and proteins and the output is the target phenot ypic feature
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April 2014 Santa Fe Institute Bulletin 17
space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
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April 2014 Santa Fe Institute Bulletin 19
A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
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April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
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22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
More than 70 of our annual
operating budget comes from
private sources - forward-thinking
individuals corporations and
foundations If you believe
complex systems science can
generate the insights that matter
most for science and society makea gift today Visit our website at
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8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2828
8122019 How Life Got Complex
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10 Santa Fe Institute Bulletin Vol 28
Time series representation of the dis tribution of fight sizes in a macaque society Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once An outburst by an individual wonrsquot normally tip the scales But when the system is perched at
a phase transition a brawl among many individuals can erupt
and lengths o copper wire that make upa computer Te analogy is apt Like anelectronic circuit individual components
ndash genes organs people ndash inormational-ly bind living things to orm a kind obiological or social computer In act the
circuit approach to describing a systemstems rom a hunch that the hierarchicalscales present throughout nature ldquoarise
Time
F i g h
t 1
F i g h
t 2
Individuals who fought more than once are represented by a color
Grey squares represent individuals who fought only once
Vd
Pa
Je
Cb
Yv
Ta
Sg
Qs
Po
Mv
Ip
Fp
Eo
Dh
Tr
Pr
Fc
Zu
Vf
Fp
Ec
Zu
Zr
Iw
Is
Fp
Fc
Cc
Yv
Cd
Ta
Cc
Qv
Ip
Cb
Pa
Eb
Sb
Jc
Je
Hp
Lr
Jc
Zu
Po
Dh
Vf
Eo
Eo
Cd
Pa
Dh
Hh
At
Zu
Yv
Th
Ta
Fn
through a process o collective computa-tionrdquo creating slow-changing predictablesocial and biological structures Flack says
Circuits are more than just an analo-gy though ndash theyrsquore the tools that bridgethe gap between individual and collective
behavior And in a scientific field whereitrsquos easy to avoid real data circuits are one
way Flack and Krakauerrsquos research group
makes sure their theories are a good matchto the real world ldquoOur group is committedto an empirical approachrdquo Flack says ldquoWebelieve that only when these measures aredeveloped with an understanding o thedata generated by real systems will they
be useulrdquoTe process o building circuits begins
by analyzing how a systemrsquos individual parts work together Using the macaquefight data Flack Krakauer and ormerSFI Omidyar Fellow Simon DeDeo (nowat Indiana University Bloomington) devel-oped a statistical method they dubbedinductive game theory to analyze howthe monkeys reacted to othersrsquo fights Teresulting social circuit Flack says servesas a detailed model ldquoor how the micro-
scopic behavior maps to the unctionallyimportant macroscopic eatures o socialstructurerdquo such as the distribution o fightsizes In other words to construct a socialor biological circuit is to understand how agroup builds and maintains stable predict-able inormation hierarchies
Te final step is to produce a simplifiedsocial circuit what the researchers call a
ldquocognitive effective theoryrdquo that accurate-ly predicts how groups behave Te aimis to extract the key sorts o interactions
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 1328
April 2014 Santa Fe Institute Bulletin 11
responsible or power structures fight-sizedistributions or other macroscopic eaturesusing ldquowhat we know about individual orcomponent cognition to coarse grain orcompressrdquo social circuits Flack says Suchcompression is essential she says because
living things canrsquot base their decisions on what ever y other living thing is doing instead theyrsquore orced to pay attention to
just a ew patterns or details o whatrsquos goingon around them
Te key question here as collaboratorand Princeton University graduate studentEleanor Brush puts it is how little inor-mation individuals need to successullyoutsmart others
Accidental or inevitable
Answering questions like that one ndash or testingsome o the teamrsquos more abstract predic-tions ndash remains a central challenge Poonor example describes his studies o electioncycles and policy change as ldquotoy modelsrdquo ndashthey capture qualitative eatures o the datasuch as party switching but donrsquot stand upto more precise quantitative tests
Meanwhile Krakauer and others sayitrsquos not always clear how to test particularhypotheses such as the prediction that basismismatch leads to ever-increasing complexity
in living things ldquoWersquore still looking or somecompelling examplerdquo Ellison says ldquoPart othe issue is that one can ofen play the devilrsquosadvocate and call into question the examplerdquoone reason why his work to construct ormal
precise measures o complexity is so import-ant he says
New techniques or rapidly analyzinggenetic data Krakauer says might improvethe situation Combining those techniques
with laboratory-based ldquoexperimental evolutionrdquo
in which researchers study the effects o preciseenvironmental changes on small organismssuch as bacteria could help test some o theendeavorrsquos core ideas such as the reflection
principle or the role o phase transitionsAnother potential avenue is to use ldquodigital
sources like computer games where we cancontrol to a large extent the orm o the dataor the conditions under which they werecollectedrdquo Krakauer says
esting their theories is just one part o theteamrsquos ambitious aims Tey hope Flack says
People gather in Washington DC before Martin Luther King Jrrsquos ldquoI Have a Dreamrdquo speech on Aug 28 1963 to demand equalities
Individuals tend to move faster and be less predictable than the slow-moving institutions they create
to achieve nothing less than an understandingo why lie is organized the way it is rom thesmallest bacteria to the largest human insti-tutions Tat requires combining real-worldobservation and abstract mathematical theoryin novel and creative ways
And as i that wasnrsquot enough Krakauerhas one more question in mind is lie anaccident or is it inevitable And i lie isinevitable well are we alone
ldquoI itrsquos not a product o a series o random
accidents but therersquos an underlying law-likeregularity that would give us confidence inbelieving in the possibility o lie presenteverywhere in the universerdquo he says ldquoSo
when one asks why does it matter whetheritrsquos chance or necessity it matters i we care
whether wersquore alone are notrdquo
Nathan Collins is a feelance science writernew ather and film aficionado based inSan Francisco
ldquoLiving things canrsquot base their decisions on what
every other living thing is doing instead theyrsquore
forced to pay attention to just a few patternsrdquo
8122019 How Life Got Complex
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12 Santa Fe Institute Bulletin Vol 28
ldquohellipin mere Time all things follow
one another and in mere Space
all things are side by side it is
accordingly only by the combi-
nation of Time and Space thatthe representation of coexistence
arisesrdquo
mdash ARTHUR SCHOPENHAUER On the Fourfold Root
of the Principle of Sufficient Reason 1813
Yves Tanguy Indefinite Divisibility 1942
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 13
D I A C O M M O N S I N S E T H E N R I R O U S S E A U T H E
D R E A M 1 9 1 0 W I K I M E D I A C O M M O N S
Biological systems ndash rom cells totissues to individuals to societ-ies ndash are hierarchically organized
(eg Feldman amp Eschel 1982 Buss 1987Smith amp Szathmaacutery 1998 Valentineamp May 1996 Michod 2000 Frank2003) o many hierarchical organiza-tion suggests the nesting o components orindividuals into groups with these groupsaggregating into yet larger groups But this view ndash at least superficially ndash privilegesspace and matter over time and inorma-tion Many types o neural coding orexample require averaging or summing over neural firing ratesTe neuronsrsquo spatial location ndash that they are in proximity ndash iso course important but at least as important to the encoding
The explanation for the complex multi-scale structure of biological and social systems
lies in their manipulation of space and time to reduce uncertainty about the future
is their behavior in time Likewise insome monkey societies as I will discussin detail later in this review individualsestimate the uture cost o social inter-action by encoding the average outcomeo past interactions in special signals and
then summing over these signalsIn both examples inormation rom
events distributed in time as well as space(Figure 1) is captured with encodingsthat are used to control some behavioraloutput My collaborators and I in theCenter or Complexity amp Collective
Computation are exploring the idea that hierarchical organiza-tion at its core is a nesting o these kinds o unctional encodingsAs I will explain we think these unctional encodings result
LIFErsquoS INFORMATION HIERARCHY
BY JESSICA C FLACK
CO-DIRECTOR CENTER FOR COMPLEXITY amp COLLECTIVE COMPUTATION
WISCONSIN INSTITUTE FOR DISCOVERY UNIVERSITY OF WISCONSIN-MADISON
EXTERNAL PROFESSOR SANTA FE INSTITUTE
Jessica Flack
Number of spatial dimensions
N u m b e
r o f t i m e d i m e n s i o n s
0
0
1
2
3
4
5
1 2 3 4 5
UNSTABLE
TOO SIMPLE
UNPREDICTABLE (ELLIPTIC)
UNPREDICTABLE(ULTRAHYPERBOLIC)
TACHYONSONLY
UNSTABLE
Figure 1 The dimensionality of the time-space
continuum with properties postulated when x
does not equal 3 and y is larger than 1 Life on
earth exist s in three spatial dimensions and one
temporal dimension Biological systems effec-
tively ldquodiscretizerdquo time and space to reduce
environmental uncertainty by coarse-graining
and compressing environmental time series to
find regularities Components use the coarse-
grained descriptions to predict the future
tuning their behavior to their predictions
8122019 How Life Got Complex
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14 Santa Fe Institute Bulletin Vol 28
rom biological systemsmanipulating space andtime (Figure 2) to acilitateinormation extraction which in turn acilitatesmore efficient extraction
o energy h i s inormation
hierarchy appears to bea universal property obiological systems andmay be the key to one oliersquos greatest mysteries ndashthe origins o biologicalcomplexity In this essayI review a body o work byDavid Krakauer myseland our research group
that has been inspiredby many years o work atthe Santa Fe Institute (eg Crutchfield 1994 Gell-Mann1996 Gell-Mann amp Lloyd 1996 Fontana amp Buss 1996 West Brown amp Enquist 1997 Fontana amp Schuster 1998Ancel amp Fontana 2000 Stadler Stadler Wagner amp Fontana2001 Smith 2003 Crutchfield amp Goumlrnerup 2006 Smith2008) Our work suggests that complexity and the multi-scalestructure o biological systems are the predictable outcomeo evolutionary dynamics driven by uncertainty minimiza-tion (Krakauer 2011 Flack 2012 Flack Erwin Elliot ampKrakauer 2013)
Tis recasting o the evolutionary process as an inerentialone1 (Bergstrom amp Rosvall 2009 Krakauer 2011) is based onthe premise that organisms and other biological systems canbe viewed as hypotheses about the present and uture environ-ments they or their offspring will encounter induced rom thehistory o past environmental states they or their ancestors have
experienced (eg Crutchfield amp Feldman 2001 Krakauer ampZannotto 2009 Ellison Flack amp Krakauer in prep) Tis premise o course only holds i the past is prologue ndash that ishas regularities and the regularities can be estimated and evenmanipulated (as in niche construction) by biological systems ortheir components to produce adaptive behavior (Flack ErwinElliot amp Krakauer 2013 Ellison Flack amp Krakauer in prep)
I these premises are correct lie at its core is computa-tional and a central question becomes How do systems andtheir components estimate and control the regularity in theirenvironments and use these estimates to tune their strategiesI suggest that the answer to this question and the explanation
or complexity is that biological systems manipulate spatialand temporal structure to produce order ndash low variance ndash atlocal scales
UNCERTAINTY REDUCTION
Te story I want to tell starts with the observation that witheach new level o organization typically comes new unction-ality ndash a new eature with positive payoff consequences or thesystem as a whole or or its components (Flack Erwin Elliot
1 Tis idea is related to work on Maxwel lrsquos Demon (eg Krakauer2011 Mandal 983121uan amp Jarzynski 2013) and the Carnot cycle ( egSmith 2003) but we do not yet understand the mapping
Figure 2 Biological systems ndash from
(left to right ) Volvox colonies to slime
molds to animal societies to large-scale
ecosystems such as reefs t o human
cities ndash are hierarchically organized with
multiple functionally important time and
space scales All feature 1) components
with only partially aligned interests
exhibiting coherent behavior at the
aggregate level 2) components that turn
over and that co-exist in the sys tem at
varying stages of development
3) social structure that persists but
component behavior that fluctuates and
4) macroscopic variation in temporal
and spatial structure and coupling with
microscopic behavior which has func-
tional impl ications when the components
can perceive ndash in evolutionary develop-
mental or ecological time ndash regularities
at the macroscopic scale
1 2
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 15
amp Krakauer 2013) Policing in a pigtailed macaque group is anexample Once a heavy tailed distribution o power ndash defined asthe degree o consensus in the group that an individual can winfights (see Flack amp Krakauer 2006 Boehm amp Flack 2010 BrushKrakauer amp Flack2013) ndash becomes effec-
tively institutionalized(here meaning hard tochange) policing (anintrinsically costlystrategy) becomesaffordable at least tothose animals that sitin the tail o the powerdistribution thosesuper powerul monkeys who are rarely or never challenged when they break up fights (Flack de Waal amp Krakauer 2005Flack Girvan de Waal amp Krakauer 2006)
My collaborators and I propose that a primary driver o theemergence o new unctionality such as policing is the reduc-tion o environmental uncertainty through the constructiono nested dynamical processes with a range o characteris-tic time constants (Flack Erwin Elliot amp Krakauer 2013)Tese nested dynamical processes arise as components extractregularities rom ast microscopic behavior by coarse-grain-ing (or compressing) the history o events to which they havebeen exposed
Proteins or example can have a long hal-lie relative toRNA transcripts and can be thought o as the summed outputo translation Cells have a long hal-lie relative to proteins
and are a unction o the summed output o arrays o spatiallystructured proteins Both proteins and cells represent someaverage measure o the noisier activity o their constituentsSimilarly a pigtailed macaquersquos estimate o its power is a kind
o average measure othe collective percep-
tion in the groupthat the macaque iscapable o winningights and this is abetter predictor othe cost the macaque will pay during fightsthan the outcome oany single melee as
these outcomes can fluctuate or contextual reasons Tesecoarse-grainings or averages are encoded as slow variables(Flack amp de Waal 2007 Flack 2012 Flack Erwin Elliot
amp Krakauer 2013 see also Feret Danos Krivine Harner ampFontana 2009 or a similar idea) Slow variables may have aspatial component as well as a temporal component as in the protein and cell examples (Figure 6) or minimally only atemporal component as in the monkey example
As a consequence o integrating over abundant microscopic processes slow variables provide better predictors o the localuture configuration o a system than the states o the fluctuatingmicroscopic components In doing so they promote acceleratedrates o microscopic adaptation Slow variables acilitate adapta-tion in two ways Tey allow components to fine-tune theirbehavior and they ree components to search at low cost a larger
3 4
Slow variables provide better predictors
of the local future configuration of a
system than the states of the fluctuating
microscopic componentsrdquo
8122019 How Life Got Complex
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16 Santa Fe Institute Bulletin Vol 28
Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and
proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since
fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous
starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of
relevant genes and proteins and the output is the target phenot ypic feature
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 17
space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
8122019 How Life Got Complex
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 19
A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
8122019 How Life Got Complex
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
8122019 How Life Got Complex
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22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
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foundations If you believe
complex systems science can
generate the insights that matter
most for science and society makea gift today Visit our website at
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8122019 How Life Got Complex
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8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 11
responsible or power structures fight-sizedistributions or other macroscopic eaturesusing ldquowhat we know about individual orcomponent cognition to coarse grain orcompressrdquo social circuits Flack says Suchcompression is essential she says because
living things canrsquot base their decisions on what ever y other living thing is doing instead theyrsquore orced to pay attention to
just a ew patterns or details o whatrsquos goingon around them
Te key question here as collaboratorand Princeton University graduate studentEleanor Brush puts it is how little inor-mation individuals need to successullyoutsmart others
Accidental or inevitable
Answering questions like that one ndash or testingsome o the teamrsquos more abstract predic-tions ndash remains a central challenge Poonor example describes his studies o electioncycles and policy change as ldquotoy modelsrdquo ndashthey capture qualitative eatures o the datasuch as party switching but donrsquot stand upto more precise quantitative tests
Meanwhile Krakauer and others sayitrsquos not always clear how to test particularhypotheses such as the prediction that basismismatch leads to ever-increasing complexity
in living things ldquoWersquore still looking or somecompelling examplerdquo Ellison says ldquoPart othe issue is that one can ofen play the devilrsquosadvocate and call into question the examplerdquoone reason why his work to construct ormal
precise measures o complexity is so import-ant he says
New techniques or rapidly analyzinggenetic data Krakauer says might improvethe situation Combining those techniques
with laboratory-based ldquoexperimental evolutionrdquo
in which researchers study the effects o preciseenvironmental changes on small organismssuch as bacteria could help test some o theendeavorrsquos core ideas such as the reflection
principle or the role o phase transitionsAnother potential avenue is to use ldquodigital
sources like computer games where we cancontrol to a large extent the orm o the dataor the conditions under which they werecollectedrdquo Krakauer says
esting their theories is just one part o theteamrsquos ambitious aims Tey hope Flack says
People gather in Washington DC before Martin Luther King Jrrsquos ldquoI Have a Dreamrdquo speech on Aug 28 1963 to demand equalities
Individuals tend to move faster and be less predictable than the slow-moving institutions they create
to achieve nothing less than an understandingo why lie is organized the way it is rom thesmallest bacteria to the largest human insti-tutions Tat requires combining real-worldobservation and abstract mathematical theoryin novel and creative ways
And as i that wasnrsquot enough Krakauerhas one more question in mind is lie anaccident or is it inevitable And i lie isinevitable well are we alone
ldquoI itrsquos not a product o a series o random
accidents but therersquos an underlying law-likeregularity that would give us confidence inbelieving in the possibility o lie presenteverywhere in the universerdquo he says ldquoSo
when one asks why does it matter whetheritrsquos chance or necessity it matters i we care
whether wersquore alone are notrdquo
Nathan Collins is a feelance science writernew ather and film aficionado based inSan Francisco
ldquoLiving things canrsquot base their decisions on what
every other living thing is doing instead theyrsquore
forced to pay attention to just a few patternsrdquo
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12 Santa Fe Institute Bulletin Vol 28
ldquohellipin mere Time all things follow
one another and in mere Space
all things are side by side it is
accordingly only by the combi-
nation of Time and Space thatthe representation of coexistence
arisesrdquo
mdash ARTHUR SCHOPENHAUER On the Fourfold Root
of the Principle of Sufficient Reason 1813
Yves Tanguy Indefinite Divisibility 1942
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 13
D I A C O M M O N S I N S E T H E N R I R O U S S E A U T H E
D R E A M 1 9 1 0 W I K I M E D I A C O M M O N S
Biological systems ndash rom cells totissues to individuals to societ-ies ndash are hierarchically organized
(eg Feldman amp Eschel 1982 Buss 1987Smith amp Szathmaacutery 1998 Valentineamp May 1996 Michod 2000 Frank2003) o many hierarchical organiza-tion suggests the nesting o components orindividuals into groups with these groupsaggregating into yet larger groups But this view ndash at least superficially ndash privilegesspace and matter over time and inorma-tion Many types o neural coding orexample require averaging or summing over neural firing ratesTe neuronsrsquo spatial location ndash that they are in proximity ndash iso course important but at least as important to the encoding
The explanation for the complex multi-scale structure of biological and social systems
lies in their manipulation of space and time to reduce uncertainty about the future
is their behavior in time Likewise insome monkey societies as I will discussin detail later in this review individualsestimate the uture cost o social inter-action by encoding the average outcomeo past interactions in special signals and
then summing over these signalsIn both examples inormation rom
events distributed in time as well as space(Figure 1) is captured with encodingsthat are used to control some behavioraloutput My collaborators and I in theCenter or Complexity amp Collective
Computation are exploring the idea that hierarchical organiza-tion at its core is a nesting o these kinds o unctional encodingsAs I will explain we think these unctional encodings result
LIFErsquoS INFORMATION HIERARCHY
BY JESSICA C FLACK
CO-DIRECTOR CENTER FOR COMPLEXITY amp COLLECTIVE COMPUTATION
WISCONSIN INSTITUTE FOR DISCOVERY UNIVERSITY OF WISCONSIN-MADISON
EXTERNAL PROFESSOR SANTA FE INSTITUTE
Jessica Flack
Number of spatial dimensions
N u m b e
r o f t i m e d i m e n s i o n s
0
0
1
2
3
4
5
1 2 3 4 5
UNSTABLE
TOO SIMPLE
UNPREDICTABLE (ELLIPTIC)
UNPREDICTABLE(ULTRAHYPERBOLIC)
TACHYONSONLY
UNSTABLE
Figure 1 The dimensionality of the time-space
continuum with properties postulated when x
does not equal 3 and y is larger than 1 Life on
earth exist s in three spatial dimensions and one
temporal dimension Biological systems effec-
tively ldquodiscretizerdquo time and space to reduce
environmental uncertainty by coarse-graining
and compressing environmental time series to
find regularities Components use the coarse-
grained descriptions to predict the future
tuning their behavior to their predictions
8122019 How Life Got Complex
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14 Santa Fe Institute Bulletin Vol 28
rom biological systemsmanipulating space andtime (Figure 2) to acilitateinormation extraction which in turn acilitatesmore efficient extraction
o energy h i s inormation
hierarchy appears to bea universal property obiological systems andmay be the key to one oliersquos greatest mysteries ndashthe origins o biologicalcomplexity In this essayI review a body o work byDavid Krakauer myseland our research group
that has been inspiredby many years o work atthe Santa Fe Institute (eg Crutchfield 1994 Gell-Mann1996 Gell-Mann amp Lloyd 1996 Fontana amp Buss 1996 West Brown amp Enquist 1997 Fontana amp Schuster 1998Ancel amp Fontana 2000 Stadler Stadler Wagner amp Fontana2001 Smith 2003 Crutchfield amp Goumlrnerup 2006 Smith2008) Our work suggests that complexity and the multi-scalestructure o biological systems are the predictable outcomeo evolutionary dynamics driven by uncertainty minimiza-tion (Krakauer 2011 Flack 2012 Flack Erwin Elliot ampKrakauer 2013)
Tis recasting o the evolutionary process as an inerentialone1 (Bergstrom amp Rosvall 2009 Krakauer 2011) is based onthe premise that organisms and other biological systems canbe viewed as hypotheses about the present and uture environ-ments they or their offspring will encounter induced rom thehistory o past environmental states they or their ancestors have
experienced (eg Crutchfield amp Feldman 2001 Krakauer ampZannotto 2009 Ellison Flack amp Krakauer in prep) Tis premise o course only holds i the past is prologue ndash that ishas regularities and the regularities can be estimated and evenmanipulated (as in niche construction) by biological systems ortheir components to produce adaptive behavior (Flack ErwinElliot amp Krakauer 2013 Ellison Flack amp Krakauer in prep)
I these premises are correct lie at its core is computa-tional and a central question becomes How do systems andtheir components estimate and control the regularity in theirenvironments and use these estimates to tune their strategiesI suggest that the answer to this question and the explanation
or complexity is that biological systems manipulate spatialand temporal structure to produce order ndash low variance ndash atlocal scales
UNCERTAINTY REDUCTION
Te story I want to tell starts with the observation that witheach new level o organization typically comes new unction-ality ndash a new eature with positive payoff consequences or thesystem as a whole or or its components (Flack Erwin Elliot
1 Tis idea is related to work on Maxwel lrsquos Demon (eg Krakauer2011 Mandal 983121uan amp Jarzynski 2013) and the Carnot cycle ( egSmith 2003) but we do not yet understand the mapping
Figure 2 Biological systems ndash from
(left to right ) Volvox colonies to slime
molds to animal societies to large-scale
ecosystems such as reefs t o human
cities ndash are hierarchically organized with
multiple functionally important time and
space scales All feature 1) components
with only partially aligned interests
exhibiting coherent behavior at the
aggregate level 2) components that turn
over and that co-exist in the sys tem at
varying stages of development
3) social structure that persists but
component behavior that fluctuates and
4) macroscopic variation in temporal
and spatial structure and coupling with
microscopic behavior which has func-
tional impl ications when the components
can perceive ndash in evolutionary develop-
mental or ecological time ndash regularities
at the macroscopic scale
1 2
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 15
amp Krakauer 2013) Policing in a pigtailed macaque group is anexample Once a heavy tailed distribution o power ndash defined asthe degree o consensus in the group that an individual can winfights (see Flack amp Krakauer 2006 Boehm amp Flack 2010 BrushKrakauer amp Flack2013) ndash becomes effec-
tively institutionalized(here meaning hard tochange) policing (anintrinsically costlystrategy) becomesaffordable at least tothose animals that sitin the tail o the powerdistribution thosesuper powerul monkeys who are rarely or never challenged when they break up fights (Flack de Waal amp Krakauer 2005Flack Girvan de Waal amp Krakauer 2006)
My collaborators and I propose that a primary driver o theemergence o new unctionality such as policing is the reduc-tion o environmental uncertainty through the constructiono nested dynamical processes with a range o characteris-tic time constants (Flack Erwin Elliot amp Krakauer 2013)Tese nested dynamical processes arise as components extractregularities rom ast microscopic behavior by coarse-grain-ing (or compressing) the history o events to which they havebeen exposed
Proteins or example can have a long hal-lie relative toRNA transcripts and can be thought o as the summed outputo translation Cells have a long hal-lie relative to proteins
and are a unction o the summed output o arrays o spatiallystructured proteins Both proteins and cells represent someaverage measure o the noisier activity o their constituentsSimilarly a pigtailed macaquersquos estimate o its power is a kind
o average measure othe collective percep-
tion in the groupthat the macaque iscapable o winningights and this is abetter predictor othe cost the macaque will pay during fightsthan the outcome oany single melee as
these outcomes can fluctuate or contextual reasons Tesecoarse-grainings or averages are encoded as slow variables(Flack amp de Waal 2007 Flack 2012 Flack Erwin Elliot
amp Krakauer 2013 see also Feret Danos Krivine Harner ampFontana 2009 or a similar idea) Slow variables may have aspatial component as well as a temporal component as in the protein and cell examples (Figure 6) or minimally only atemporal component as in the monkey example
As a consequence o integrating over abundant microscopic processes slow variables provide better predictors o the localuture configuration o a system than the states o the fluctuatingmicroscopic components In doing so they promote acceleratedrates o microscopic adaptation Slow variables acilitate adapta-tion in two ways Tey allow components to fine-tune theirbehavior and they ree components to search at low cost a larger
3 4
Slow variables provide better predictors
of the local future configuration of a
system than the states of the fluctuating
microscopic componentsrdquo
8122019 How Life Got Complex
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16 Santa Fe Institute Bulletin Vol 28
Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and
proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since
fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous
starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of
relevant genes and proteins and the output is the target phenot ypic feature
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April 2014 Santa Fe Institute Bulletin 17
space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
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April 2014 Santa Fe Institute Bulletin 19
A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
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April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
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22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 25
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8122019 How Life Got Complex
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8122019 How Life Got Complex
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12 Santa Fe Institute Bulletin Vol 28
ldquohellipin mere Time all things follow
one another and in mere Space
all things are side by side it is
accordingly only by the combi-
nation of Time and Space thatthe representation of coexistence
arisesrdquo
mdash ARTHUR SCHOPENHAUER On the Fourfold Root
of the Principle of Sufficient Reason 1813
Yves Tanguy Indefinite Divisibility 1942
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 13
D I A C O M M O N S I N S E T H E N R I R O U S S E A U T H E
D R E A M 1 9 1 0 W I K I M E D I A C O M M O N S
Biological systems ndash rom cells totissues to individuals to societ-ies ndash are hierarchically organized
(eg Feldman amp Eschel 1982 Buss 1987Smith amp Szathmaacutery 1998 Valentineamp May 1996 Michod 2000 Frank2003) o many hierarchical organiza-tion suggests the nesting o components orindividuals into groups with these groupsaggregating into yet larger groups But this view ndash at least superficially ndash privilegesspace and matter over time and inorma-tion Many types o neural coding orexample require averaging or summing over neural firing ratesTe neuronsrsquo spatial location ndash that they are in proximity ndash iso course important but at least as important to the encoding
The explanation for the complex multi-scale structure of biological and social systems
lies in their manipulation of space and time to reduce uncertainty about the future
is their behavior in time Likewise insome monkey societies as I will discussin detail later in this review individualsestimate the uture cost o social inter-action by encoding the average outcomeo past interactions in special signals and
then summing over these signalsIn both examples inormation rom
events distributed in time as well as space(Figure 1) is captured with encodingsthat are used to control some behavioraloutput My collaborators and I in theCenter or Complexity amp Collective
Computation are exploring the idea that hierarchical organiza-tion at its core is a nesting o these kinds o unctional encodingsAs I will explain we think these unctional encodings result
LIFErsquoS INFORMATION HIERARCHY
BY JESSICA C FLACK
CO-DIRECTOR CENTER FOR COMPLEXITY amp COLLECTIVE COMPUTATION
WISCONSIN INSTITUTE FOR DISCOVERY UNIVERSITY OF WISCONSIN-MADISON
EXTERNAL PROFESSOR SANTA FE INSTITUTE
Jessica Flack
Number of spatial dimensions
N u m b e
r o f t i m e d i m e n s i o n s
0
0
1
2
3
4
5
1 2 3 4 5
UNSTABLE
TOO SIMPLE
UNPREDICTABLE (ELLIPTIC)
UNPREDICTABLE(ULTRAHYPERBOLIC)
TACHYONSONLY
UNSTABLE
Figure 1 The dimensionality of the time-space
continuum with properties postulated when x
does not equal 3 and y is larger than 1 Life on
earth exist s in three spatial dimensions and one
temporal dimension Biological systems effec-
tively ldquodiscretizerdquo time and space to reduce
environmental uncertainty by coarse-graining
and compressing environmental time series to
find regularities Components use the coarse-
grained descriptions to predict the future
tuning their behavior to their predictions
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14 Santa Fe Institute Bulletin Vol 28
rom biological systemsmanipulating space andtime (Figure 2) to acilitateinormation extraction which in turn acilitatesmore efficient extraction
o energy h i s inormation
hierarchy appears to bea universal property obiological systems andmay be the key to one oliersquos greatest mysteries ndashthe origins o biologicalcomplexity In this essayI review a body o work byDavid Krakauer myseland our research group
that has been inspiredby many years o work atthe Santa Fe Institute (eg Crutchfield 1994 Gell-Mann1996 Gell-Mann amp Lloyd 1996 Fontana amp Buss 1996 West Brown amp Enquist 1997 Fontana amp Schuster 1998Ancel amp Fontana 2000 Stadler Stadler Wagner amp Fontana2001 Smith 2003 Crutchfield amp Goumlrnerup 2006 Smith2008) Our work suggests that complexity and the multi-scalestructure o biological systems are the predictable outcomeo evolutionary dynamics driven by uncertainty minimiza-tion (Krakauer 2011 Flack 2012 Flack Erwin Elliot ampKrakauer 2013)
Tis recasting o the evolutionary process as an inerentialone1 (Bergstrom amp Rosvall 2009 Krakauer 2011) is based onthe premise that organisms and other biological systems canbe viewed as hypotheses about the present and uture environ-ments they or their offspring will encounter induced rom thehistory o past environmental states they or their ancestors have
experienced (eg Crutchfield amp Feldman 2001 Krakauer ampZannotto 2009 Ellison Flack amp Krakauer in prep) Tis premise o course only holds i the past is prologue ndash that ishas regularities and the regularities can be estimated and evenmanipulated (as in niche construction) by biological systems ortheir components to produce adaptive behavior (Flack ErwinElliot amp Krakauer 2013 Ellison Flack amp Krakauer in prep)
I these premises are correct lie at its core is computa-tional and a central question becomes How do systems andtheir components estimate and control the regularity in theirenvironments and use these estimates to tune their strategiesI suggest that the answer to this question and the explanation
or complexity is that biological systems manipulate spatialand temporal structure to produce order ndash low variance ndash atlocal scales
UNCERTAINTY REDUCTION
Te story I want to tell starts with the observation that witheach new level o organization typically comes new unction-ality ndash a new eature with positive payoff consequences or thesystem as a whole or or its components (Flack Erwin Elliot
1 Tis idea is related to work on Maxwel lrsquos Demon (eg Krakauer2011 Mandal 983121uan amp Jarzynski 2013) and the Carnot cycle ( egSmith 2003) but we do not yet understand the mapping
Figure 2 Biological systems ndash from
(left to right ) Volvox colonies to slime
molds to animal societies to large-scale
ecosystems such as reefs t o human
cities ndash are hierarchically organized with
multiple functionally important time and
space scales All feature 1) components
with only partially aligned interests
exhibiting coherent behavior at the
aggregate level 2) components that turn
over and that co-exist in the sys tem at
varying stages of development
3) social structure that persists but
component behavior that fluctuates and
4) macroscopic variation in temporal
and spatial structure and coupling with
microscopic behavior which has func-
tional impl ications when the components
can perceive ndash in evolutionary develop-
mental or ecological time ndash regularities
at the macroscopic scale
1 2
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April 2014 Santa Fe Institute Bulletin 15
amp Krakauer 2013) Policing in a pigtailed macaque group is anexample Once a heavy tailed distribution o power ndash defined asthe degree o consensus in the group that an individual can winfights (see Flack amp Krakauer 2006 Boehm amp Flack 2010 BrushKrakauer amp Flack2013) ndash becomes effec-
tively institutionalized(here meaning hard tochange) policing (anintrinsically costlystrategy) becomesaffordable at least tothose animals that sitin the tail o the powerdistribution thosesuper powerul monkeys who are rarely or never challenged when they break up fights (Flack de Waal amp Krakauer 2005Flack Girvan de Waal amp Krakauer 2006)
My collaborators and I propose that a primary driver o theemergence o new unctionality such as policing is the reduc-tion o environmental uncertainty through the constructiono nested dynamical processes with a range o characteris-tic time constants (Flack Erwin Elliot amp Krakauer 2013)Tese nested dynamical processes arise as components extractregularities rom ast microscopic behavior by coarse-grain-ing (or compressing) the history o events to which they havebeen exposed
Proteins or example can have a long hal-lie relative toRNA transcripts and can be thought o as the summed outputo translation Cells have a long hal-lie relative to proteins
and are a unction o the summed output o arrays o spatiallystructured proteins Both proteins and cells represent someaverage measure o the noisier activity o their constituentsSimilarly a pigtailed macaquersquos estimate o its power is a kind
o average measure othe collective percep-
tion in the groupthat the macaque iscapable o winningights and this is abetter predictor othe cost the macaque will pay during fightsthan the outcome oany single melee as
these outcomes can fluctuate or contextual reasons Tesecoarse-grainings or averages are encoded as slow variables(Flack amp de Waal 2007 Flack 2012 Flack Erwin Elliot
amp Krakauer 2013 see also Feret Danos Krivine Harner ampFontana 2009 or a similar idea) Slow variables may have aspatial component as well as a temporal component as in the protein and cell examples (Figure 6) or minimally only atemporal component as in the monkey example
As a consequence o integrating over abundant microscopic processes slow variables provide better predictors o the localuture configuration o a system than the states o the fluctuatingmicroscopic components In doing so they promote acceleratedrates o microscopic adaptation Slow variables acilitate adapta-tion in two ways Tey allow components to fine-tune theirbehavior and they ree components to search at low cost a larger
3 4
Slow variables provide better predictors
of the local future configuration of a
system than the states of the fluctuating
microscopic componentsrdquo
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16 Santa Fe Institute Bulletin Vol 28
Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and
proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since
fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous
starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of
relevant genes and proteins and the output is the target phenot ypic feature
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 17
space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 19
A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
8122019 How Life Got Complex
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22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
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8122019 How Life Got Complex
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8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 13
D I A C O M M O N S I N S E T H E N R I R O U S S E A U T H E
D R E A M 1 9 1 0 W I K I M E D I A C O M M O N S
Biological systems ndash rom cells totissues to individuals to societ-ies ndash are hierarchically organized
(eg Feldman amp Eschel 1982 Buss 1987Smith amp Szathmaacutery 1998 Valentineamp May 1996 Michod 2000 Frank2003) o many hierarchical organiza-tion suggests the nesting o components orindividuals into groups with these groupsaggregating into yet larger groups But this view ndash at least superficially ndash privilegesspace and matter over time and inorma-tion Many types o neural coding orexample require averaging or summing over neural firing ratesTe neuronsrsquo spatial location ndash that they are in proximity ndash iso course important but at least as important to the encoding
The explanation for the complex multi-scale structure of biological and social systems
lies in their manipulation of space and time to reduce uncertainty about the future
is their behavior in time Likewise insome monkey societies as I will discussin detail later in this review individualsestimate the uture cost o social inter-action by encoding the average outcomeo past interactions in special signals and
then summing over these signalsIn both examples inormation rom
events distributed in time as well as space(Figure 1) is captured with encodingsthat are used to control some behavioraloutput My collaborators and I in theCenter or Complexity amp Collective
Computation are exploring the idea that hierarchical organiza-tion at its core is a nesting o these kinds o unctional encodingsAs I will explain we think these unctional encodings result
LIFErsquoS INFORMATION HIERARCHY
BY JESSICA C FLACK
CO-DIRECTOR CENTER FOR COMPLEXITY amp COLLECTIVE COMPUTATION
WISCONSIN INSTITUTE FOR DISCOVERY UNIVERSITY OF WISCONSIN-MADISON
EXTERNAL PROFESSOR SANTA FE INSTITUTE
Jessica Flack
Number of spatial dimensions
N u m b e
r o f t i m e d i m e n s i o n s
0
0
1
2
3
4
5
1 2 3 4 5
UNSTABLE
TOO SIMPLE
UNPREDICTABLE (ELLIPTIC)
UNPREDICTABLE(ULTRAHYPERBOLIC)
TACHYONSONLY
UNSTABLE
Figure 1 The dimensionality of the time-space
continuum with properties postulated when x
does not equal 3 and y is larger than 1 Life on
earth exist s in three spatial dimensions and one
temporal dimension Biological systems effec-
tively ldquodiscretizerdquo time and space to reduce
environmental uncertainty by coarse-graining
and compressing environmental time series to
find regularities Components use the coarse-
grained descriptions to predict the future
tuning their behavior to their predictions
8122019 How Life Got Complex
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14 Santa Fe Institute Bulletin Vol 28
rom biological systemsmanipulating space andtime (Figure 2) to acilitateinormation extraction which in turn acilitatesmore efficient extraction
o energy h i s inormation
hierarchy appears to bea universal property obiological systems andmay be the key to one oliersquos greatest mysteries ndashthe origins o biologicalcomplexity In this essayI review a body o work byDavid Krakauer myseland our research group
that has been inspiredby many years o work atthe Santa Fe Institute (eg Crutchfield 1994 Gell-Mann1996 Gell-Mann amp Lloyd 1996 Fontana amp Buss 1996 West Brown amp Enquist 1997 Fontana amp Schuster 1998Ancel amp Fontana 2000 Stadler Stadler Wagner amp Fontana2001 Smith 2003 Crutchfield amp Goumlrnerup 2006 Smith2008) Our work suggests that complexity and the multi-scalestructure o biological systems are the predictable outcomeo evolutionary dynamics driven by uncertainty minimiza-tion (Krakauer 2011 Flack 2012 Flack Erwin Elliot ampKrakauer 2013)
Tis recasting o the evolutionary process as an inerentialone1 (Bergstrom amp Rosvall 2009 Krakauer 2011) is based onthe premise that organisms and other biological systems canbe viewed as hypotheses about the present and uture environ-ments they or their offspring will encounter induced rom thehistory o past environmental states they or their ancestors have
experienced (eg Crutchfield amp Feldman 2001 Krakauer ampZannotto 2009 Ellison Flack amp Krakauer in prep) Tis premise o course only holds i the past is prologue ndash that ishas regularities and the regularities can be estimated and evenmanipulated (as in niche construction) by biological systems ortheir components to produce adaptive behavior (Flack ErwinElliot amp Krakauer 2013 Ellison Flack amp Krakauer in prep)
I these premises are correct lie at its core is computa-tional and a central question becomes How do systems andtheir components estimate and control the regularity in theirenvironments and use these estimates to tune their strategiesI suggest that the answer to this question and the explanation
or complexity is that biological systems manipulate spatialand temporal structure to produce order ndash low variance ndash atlocal scales
UNCERTAINTY REDUCTION
Te story I want to tell starts with the observation that witheach new level o organization typically comes new unction-ality ndash a new eature with positive payoff consequences or thesystem as a whole or or its components (Flack Erwin Elliot
1 Tis idea is related to work on Maxwel lrsquos Demon (eg Krakauer2011 Mandal 983121uan amp Jarzynski 2013) and the Carnot cycle ( egSmith 2003) but we do not yet understand the mapping
Figure 2 Biological systems ndash from
(left to right ) Volvox colonies to slime
molds to animal societies to large-scale
ecosystems such as reefs t o human
cities ndash are hierarchically organized with
multiple functionally important time and
space scales All feature 1) components
with only partially aligned interests
exhibiting coherent behavior at the
aggregate level 2) components that turn
over and that co-exist in the sys tem at
varying stages of development
3) social structure that persists but
component behavior that fluctuates and
4) macroscopic variation in temporal
and spatial structure and coupling with
microscopic behavior which has func-
tional impl ications when the components
can perceive ndash in evolutionary develop-
mental or ecological time ndash regularities
at the macroscopic scale
1 2
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 15
amp Krakauer 2013) Policing in a pigtailed macaque group is anexample Once a heavy tailed distribution o power ndash defined asthe degree o consensus in the group that an individual can winfights (see Flack amp Krakauer 2006 Boehm amp Flack 2010 BrushKrakauer amp Flack2013) ndash becomes effec-
tively institutionalized(here meaning hard tochange) policing (anintrinsically costlystrategy) becomesaffordable at least tothose animals that sitin the tail o the powerdistribution thosesuper powerul monkeys who are rarely or never challenged when they break up fights (Flack de Waal amp Krakauer 2005Flack Girvan de Waal amp Krakauer 2006)
My collaborators and I propose that a primary driver o theemergence o new unctionality such as policing is the reduc-tion o environmental uncertainty through the constructiono nested dynamical processes with a range o characteris-tic time constants (Flack Erwin Elliot amp Krakauer 2013)Tese nested dynamical processes arise as components extractregularities rom ast microscopic behavior by coarse-grain-ing (or compressing) the history o events to which they havebeen exposed
Proteins or example can have a long hal-lie relative toRNA transcripts and can be thought o as the summed outputo translation Cells have a long hal-lie relative to proteins
and are a unction o the summed output o arrays o spatiallystructured proteins Both proteins and cells represent someaverage measure o the noisier activity o their constituentsSimilarly a pigtailed macaquersquos estimate o its power is a kind
o average measure othe collective percep-
tion in the groupthat the macaque iscapable o winningights and this is abetter predictor othe cost the macaque will pay during fightsthan the outcome oany single melee as
these outcomes can fluctuate or contextual reasons Tesecoarse-grainings or averages are encoded as slow variables(Flack amp de Waal 2007 Flack 2012 Flack Erwin Elliot
amp Krakauer 2013 see also Feret Danos Krivine Harner ampFontana 2009 or a similar idea) Slow variables may have aspatial component as well as a temporal component as in the protein and cell examples (Figure 6) or minimally only atemporal component as in the monkey example
As a consequence o integrating over abundant microscopic processes slow variables provide better predictors o the localuture configuration o a system than the states o the fluctuatingmicroscopic components In doing so they promote acceleratedrates o microscopic adaptation Slow variables acilitate adapta-tion in two ways Tey allow components to fine-tune theirbehavior and they ree components to search at low cost a larger
3 4
Slow variables provide better predictors
of the local future configuration of a
system than the states of the fluctuating
microscopic componentsrdquo
8122019 How Life Got Complex
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16 Santa Fe Institute Bulletin Vol 28
Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and
proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since
fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous
starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of
relevant genes and proteins and the output is the target phenot ypic feature
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 17
space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
8122019 How Life Got Complex
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 19
A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
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April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
8122019 How Life Got Complex
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22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
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8122019 How Life Got Complex
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8122019 How Life Got Complex
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14 Santa Fe Institute Bulletin Vol 28
rom biological systemsmanipulating space andtime (Figure 2) to acilitateinormation extraction which in turn acilitatesmore efficient extraction
o energy h i s inormation
hierarchy appears to bea universal property obiological systems andmay be the key to one oliersquos greatest mysteries ndashthe origins o biologicalcomplexity In this essayI review a body o work byDavid Krakauer myseland our research group
that has been inspiredby many years o work atthe Santa Fe Institute (eg Crutchfield 1994 Gell-Mann1996 Gell-Mann amp Lloyd 1996 Fontana amp Buss 1996 West Brown amp Enquist 1997 Fontana amp Schuster 1998Ancel amp Fontana 2000 Stadler Stadler Wagner amp Fontana2001 Smith 2003 Crutchfield amp Goumlrnerup 2006 Smith2008) Our work suggests that complexity and the multi-scalestructure o biological systems are the predictable outcomeo evolutionary dynamics driven by uncertainty minimiza-tion (Krakauer 2011 Flack 2012 Flack Erwin Elliot ampKrakauer 2013)
Tis recasting o the evolutionary process as an inerentialone1 (Bergstrom amp Rosvall 2009 Krakauer 2011) is based onthe premise that organisms and other biological systems canbe viewed as hypotheses about the present and uture environ-ments they or their offspring will encounter induced rom thehistory o past environmental states they or their ancestors have
experienced (eg Crutchfield amp Feldman 2001 Krakauer ampZannotto 2009 Ellison Flack amp Krakauer in prep) Tis premise o course only holds i the past is prologue ndash that ishas regularities and the regularities can be estimated and evenmanipulated (as in niche construction) by biological systems ortheir components to produce adaptive behavior (Flack ErwinElliot amp Krakauer 2013 Ellison Flack amp Krakauer in prep)
I these premises are correct lie at its core is computa-tional and a central question becomes How do systems andtheir components estimate and control the regularity in theirenvironments and use these estimates to tune their strategiesI suggest that the answer to this question and the explanation
or complexity is that biological systems manipulate spatialand temporal structure to produce order ndash low variance ndash atlocal scales
UNCERTAINTY REDUCTION
Te story I want to tell starts with the observation that witheach new level o organization typically comes new unction-ality ndash a new eature with positive payoff consequences or thesystem as a whole or or its components (Flack Erwin Elliot
1 Tis idea is related to work on Maxwel lrsquos Demon (eg Krakauer2011 Mandal 983121uan amp Jarzynski 2013) and the Carnot cycle ( egSmith 2003) but we do not yet understand the mapping
Figure 2 Biological systems ndash from
(left to right ) Volvox colonies to slime
molds to animal societies to large-scale
ecosystems such as reefs t o human
cities ndash are hierarchically organized with
multiple functionally important time and
space scales All feature 1) components
with only partially aligned interests
exhibiting coherent behavior at the
aggregate level 2) components that turn
over and that co-exist in the sys tem at
varying stages of development
3) social structure that persists but
component behavior that fluctuates and
4) macroscopic variation in temporal
and spatial structure and coupling with
microscopic behavior which has func-
tional impl ications when the components
can perceive ndash in evolutionary develop-
mental or ecological time ndash regularities
at the macroscopic scale
1 2
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 15
amp Krakauer 2013) Policing in a pigtailed macaque group is anexample Once a heavy tailed distribution o power ndash defined asthe degree o consensus in the group that an individual can winfights (see Flack amp Krakauer 2006 Boehm amp Flack 2010 BrushKrakauer amp Flack2013) ndash becomes effec-
tively institutionalized(here meaning hard tochange) policing (anintrinsically costlystrategy) becomesaffordable at least tothose animals that sitin the tail o the powerdistribution thosesuper powerul monkeys who are rarely or never challenged when they break up fights (Flack de Waal amp Krakauer 2005Flack Girvan de Waal amp Krakauer 2006)
My collaborators and I propose that a primary driver o theemergence o new unctionality such as policing is the reduc-tion o environmental uncertainty through the constructiono nested dynamical processes with a range o characteris-tic time constants (Flack Erwin Elliot amp Krakauer 2013)Tese nested dynamical processes arise as components extractregularities rom ast microscopic behavior by coarse-grain-ing (or compressing) the history o events to which they havebeen exposed
Proteins or example can have a long hal-lie relative toRNA transcripts and can be thought o as the summed outputo translation Cells have a long hal-lie relative to proteins
and are a unction o the summed output o arrays o spatiallystructured proteins Both proteins and cells represent someaverage measure o the noisier activity o their constituentsSimilarly a pigtailed macaquersquos estimate o its power is a kind
o average measure othe collective percep-
tion in the groupthat the macaque iscapable o winningights and this is abetter predictor othe cost the macaque will pay during fightsthan the outcome oany single melee as
these outcomes can fluctuate or contextual reasons Tesecoarse-grainings or averages are encoded as slow variables(Flack amp de Waal 2007 Flack 2012 Flack Erwin Elliot
amp Krakauer 2013 see also Feret Danos Krivine Harner ampFontana 2009 or a similar idea) Slow variables may have aspatial component as well as a temporal component as in the protein and cell examples (Figure 6) or minimally only atemporal component as in the monkey example
As a consequence o integrating over abundant microscopic processes slow variables provide better predictors o the localuture configuration o a system than the states o the fluctuatingmicroscopic components In doing so they promote acceleratedrates o microscopic adaptation Slow variables acilitate adapta-tion in two ways Tey allow components to fine-tune theirbehavior and they ree components to search at low cost a larger
3 4
Slow variables provide better predictors
of the local future configuration of a
system than the states of the fluctuating
microscopic componentsrdquo
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16 Santa Fe Institute Bulletin Vol 28
Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and
proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since
fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous
starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of
relevant genes and proteins and the output is the target phenot ypic feature
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 17
space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
8122019 How Life Got Complex
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
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April 2014 Santa Fe Institute Bulletin 19
A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
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April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
8122019 How Life Got Complex
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22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
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8122019 How Life Got Complex
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8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 15
amp Krakauer 2013) Policing in a pigtailed macaque group is anexample Once a heavy tailed distribution o power ndash defined asthe degree o consensus in the group that an individual can winfights (see Flack amp Krakauer 2006 Boehm amp Flack 2010 BrushKrakauer amp Flack2013) ndash becomes effec-
tively institutionalized(here meaning hard tochange) policing (anintrinsically costlystrategy) becomesaffordable at least tothose animals that sitin the tail o the powerdistribution thosesuper powerul monkeys who are rarely or never challenged when they break up fights (Flack de Waal amp Krakauer 2005Flack Girvan de Waal amp Krakauer 2006)
My collaborators and I propose that a primary driver o theemergence o new unctionality such as policing is the reduc-tion o environmental uncertainty through the constructiono nested dynamical processes with a range o characteris-tic time constants (Flack Erwin Elliot amp Krakauer 2013)Tese nested dynamical processes arise as components extractregularities rom ast microscopic behavior by coarse-grain-ing (or compressing) the history o events to which they havebeen exposed
Proteins or example can have a long hal-lie relative toRNA transcripts and can be thought o as the summed outputo translation Cells have a long hal-lie relative to proteins
and are a unction o the summed output o arrays o spatiallystructured proteins Both proteins and cells represent someaverage measure o the noisier activity o their constituentsSimilarly a pigtailed macaquersquos estimate o its power is a kind
o average measure othe collective percep-
tion in the groupthat the macaque iscapable o winningights and this is abetter predictor othe cost the macaque will pay during fightsthan the outcome oany single melee as
these outcomes can fluctuate or contextual reasons Tesecoarse-grainings or averages are encoded as slow variables(Flack amp de Waal 2007 Flack 2012 Flack Erwin Elliot
amp Krakauer 2013 see also Feret Danos Krivine Harner ampFontana 2009 or a similar idea) Slow variables may have aspatial component as well as a temporal component as in the protein and cell examples (Figure 6) or minimally only atemporal component as in the monkey example
As a consequence o integrating over abundant microscopic processes slow variables provide better predictors o the localuture configuration o a system than the states o the fluctuatingmicroscopic components In doing so they promote acceleratedrates o microscopic adaptation Slow variables acilitate adapta-tion in two ways Tey allow components to fine-tune theirbehavior and they ree components to search at low cost a larger
3 4
Slow variables provide better predictors
of the local future configuration of a
system than the states of the fluctuating
microscopic componentsrdquo
8122019 How Life Got Complex
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16 Santa Fe Institute Bulletin Vol 28
Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and
proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since
fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous
starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of
relevant genes and proteins and the output is the target phenot ypic feature
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 17
space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
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April 2014 Santa Fe Institute Bulletin 19
A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
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April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
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22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
More than 70 of our annual
operating budget comes from
private sources - forward-thinking
individuals corporations and
foundations If you believe
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8122019 How Life Got Complex
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8122019 How Life Got Complex
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16 Santa Fe Institute Bulletin Vol 28
Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and
proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since
fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous
starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of
relevant genes and proteins and the output is the target phenot ypic feature
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 17
space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
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April 2014 Santa Fe Institute Bulletin 19
A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
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April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
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22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 25
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8122019 How Life Got Complex
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8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 17
space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in
RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)
NEW LEVELS OF ORGANIZATION
As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state
We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger
Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)
THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY
Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a
bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level
In physics
As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables
Termodynamics is the study o the macroscopic behavior
o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists
with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic
variables it privileges Tis is the job o statistical mechanics By
providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages
In biology
Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially
This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours
the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2
Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the
latter four of these features are no longer present by 21 hours Consult the other models
to see all the network elements and interactions in he correct temporal context
Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
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April 2014 Santa Fe Institute Bulletin 19
A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
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20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
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April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
8122019 How Life Got Complex
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22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
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April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
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private sources - forward-thinking
individuals corporations and
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generate the insights that matter
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ITrsquoS SIMPLE
Support
ComplexityScience
8122019 How Life Got Complex
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8122019 How Life Got Complex
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18 Santa Fe Institute Bulletin Vol 28
deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties
are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3
On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)
A STATISTICAL MECHANICS-COMPUTER SCIENCE-
INFORMATION THEORETIC HYBRID APPROACH
Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data
a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation
Elements of computation in biological systems
Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing
nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings
2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)
3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)
1000
0100
0010
0001
4 6 8 10 12 14i (Fight Size)
L o n g
F r a c t i o n N ( i ) N ( gt 2 )
Ud
Fo
Cd
Qs
Fp
EoCd + Qs
Eo + FoEo+Qs
Cd+Fp
Fp+Qs
Eo+Fp
Fo+Fp
Cd+Eo
Cd+FoCd+Ud
Qs+Ud
Fo+Qs
Fo+Ud
Eo+Ud
Fp+Ud
Figure 4 Cognitive effective theories for one macroscopic property of
a macaque society the distribution of fight sizes (a) To reduce circuit
complexity we return to the raw time series data and remove as much noise
as possible by compressing the data In the case of our macaque dataset this
reveals which individuals and subgroups are regular and predictable conflict
participants We then search for possible strategies in response to these
regular and predictable individuals and groups This approach returns a famil y
of circuits (b is an example) each of which has fewer nodes and edges than
the full circuit (c) These circuits are simpler and more cognitively parsimo-
nious We then test the reduced circuits against each other in simulation
to determine how well they recover the target macroscopic properties
a
b
c
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2128
April 2014 Santa Fe Institute Bulletin 19
A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2228
20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2328
April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2428
22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2528
April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
More than 70 of our annual
operating budget comes from
private sources - forward-thinking
individuals corporations and
foundations If you believe
complex systems science can
generate the insights that matter
most for science and society makea gift today Visit our website at
wwwsantafeedusupport and
help us achieve great things
ITrsquoS SIMPLE
Support
ComplexityScience
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2828
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2128
April 2014 Santa Fe Institute Bulletin 19
A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include
aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution
o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)
Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant
and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start
with a list o genes thathave been implicatedin the regulation o
cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights
Reconstructing the microscopic behavior
In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a
Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots
organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500
environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental
complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time
4
3
2
1
0
1
3 4
1 2
1 4
0
0 100 200 300 400 500 0 100 200 300 400 500
Non-Markov Environment εinfinMarkov Environment ε3
Statistical
Complexity
Probability
Ratio
C μ
( x 0 t k ) ( b i t s )
P ( Ο k )
O1
O2
O3
O4
ε j
Key
t (Generations)t (Generations)
In all biological systems there are
multiple components interacting and
simultaneously coarse graining to make
predictions about the futurerdquo
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2228
20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2328
April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2428
22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2528
April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
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private sources - forward-thinking
individuals corporations and
foundations If you believe
complex systems science can
generate the insights that matter
most for science and society makea gift today Visit our website at
wwwsantafeedusupport and
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ITrsquoS SIMPLE
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ComplexityScience
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2828
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2228
20 Santa Fe Institute Bulletin Vol 28
Phospholipidbilayer
target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)
When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp
Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data
Collective computation through stochastic circuits
In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make
predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies
in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)
Nodes in these circuits are the input to the computation
As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node
Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may
Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially
structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better
predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose
tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing
predictability promote accelerated rates of microscopic adaptation
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2328
April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2428
22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2528
April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
More than 70 of our annual
operating budget comes from
private sources - forward-thinking
individuals corporations and
foundations If you believe
complex systems science can
generate the insights that matter
most for science and society makea gift today Visit our website at
wwwsantafeedusupport and
help us achieve great things
ITrsquoS SIMPLE
Support
ComplexityScience
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2828
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2328
April 2014 Santa Fe Institute Bulletin 21
vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o
circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system
Cognitive effective theories for collective computation
Te circuits describing the microscopic behavior can be compli-
cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)
here are many wa ys to ap pr oac hthis problem Ourapproach it is to build
whatrsquos called in physics
an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the
effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing
We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how
we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate
regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)
As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the
ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives
Couplings information flow and macroscopic tuning
Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based
and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question
we must answer is what is the optimalcoupling between
macroscopic andmicroscopic change
and can systems by manipulating how components are organizedin space and time get close to this optimal coupling
One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level
Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic
property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)
I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments
A hypothesis we are exploring is thatsitting near the critical point means that
important changes at the microscopic scalewill be visible at the macroscopic scalerdquo
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2428
22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2528
April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
More than 70 of our annual
operating budget comes from
private sources - forward-thinking
individuals corporations and
foundations If you believe
complex systems science can
generate the insights that matter
most for science and society makea gift today Visit our website at
wwwsantafeedusupport and
help us achieve great things
ITrsquoS SIMPLE
Support
ComplexityScience
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2828
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2428
22 Santa Fe Institute Bulletin Vol 28
on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level
to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)
COMPLEXITY
Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases
the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation
I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective
theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies
Where are we though with respect to explaining the originso biological complexity
Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2528
April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
More than 70 of our annual
operating budget comes from
private sources - forward-thinking
individuals corporations and
foundations If you believe
complex systems science can
generate the insights that matter
most for science and society makea gift today Visit our website at
wwwsantafeedusupport and
help us achieve great things
ITrsquoS SIMPLE
Support
ComplexityScience
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2828
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2528
April 2014 Santa Fe Institute Bulletin 23
as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment
with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)
Acknowledgements
Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include
Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain
problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340
ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283
Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457
Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press
Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176
Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441
Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306
Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press
Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109
Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p
Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43
Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54
Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264
Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349
Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264
Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation
Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920
DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782
Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation
Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292
Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458
Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation
Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810
Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102
Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586
Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108
Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
More than 70 of our annual
operating budget comes from
private sources - forward-thinking
individuals corporations and
foundations If you believe
complex systems science can
generate the insights that matter
most for science and society makea gift today Visit our website at
wwwsantafeedusupport and
help us achieve great things
ITrsquoS SIMPLE
Support
ComplexityScience
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2828
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2628
24 Santa Fe Institute Bulletin Vol 28
Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press
Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion
ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press
Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429
Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley
Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455
Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705
Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp
www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014
Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53
Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3
Krakauer D C 2011 D arwinian d emons evolutionary com- plexity and inormation maximization Chaos 21 037110
Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an
Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review
Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661
Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press
Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17
Krakauer D C J P Collins D Erwin J C Flack W Fon-
tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276
Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping
strateg y to social structure In preparation
Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343
Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602
Michod R E 2000 Darwinian dynamics Evolutionary tran- sitions in itness and individualit y Princeton NJ PrincetonUniversity Press
Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM
Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302
Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487
Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697
Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press
Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639
Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In
preparation
Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44
Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation
Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452
Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114
Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx
doiorg101016jjtbi200802010 P DF
Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917
Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press
Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274
Sumpter D J 2006 he principles o collective animal be-
haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33
Valiant L 2013 Probably approximately correct New YorkBasic Books
West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276
122-126
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
More than 70 of our annual
operating budget comes from
private sources - forward-thinking
individuals corporations and
foundations If you believe
complex systems science can
generate the insights that matter
most for science and society makea gift today Visit our website at
wwwsantafeedusupport and
help us achieve great things
ITrsquoS SIMPLE
Support
ComplexityScience
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2828
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2728
April 2014 Santa Fe Institute Bulletin 25
More than 70 of our annual
operating budget comes from
private sources - forward-thinking
individuals corporations and
foundations If you believe
complex systems science can
generate the insights that matter
most for science and society makea gift today Visit our website at
wwwsantafeedusupport and
help us achieve great things
ITrsquoS SIMPLE
Support
ComplexityScience
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2828
8122019 How Life Got Complex
httpslidepdfcomreaderfullhow-life-got-complex 2828