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Toward a Scientific History
Robert Aunger
Many scales: Many schools:
• Marxist• Structuralist• Deconstructionist • Anthropological • Psychological • Economic• Feminist
• Local• World/global• ‘Big’ history
No consensus?
History as ‘story-telling’
narrative interpretation of past events emphasizing –
• contingency
• human agency
• ‘big men’
(Etymology: Latin historia = “narrative, account, tale, story”)
No theory please!
“After a century of grand theory, from Marxism and Social Darwinism to structuralism and postmodernism, most
historians have abandoned the belief in general laws. We no longer search for grand designs and dialectics.
Instead, we concentrate on the particular and sometimes even the microscopic…not because we think we can see
the universe in a grain of sand but because we have developed an increased sensitivity to the complexities that differentiate one society or one subculture from
another.”
(Darnton 1999)
Historical regularities
(Turchin, 2005)
Historical progress
‘Arrow’ in history, resulting in increasing complexity over time
This progress attributed to –
• energy flows (LH Morgan, Leslie White, Chaisson)
• information accumulation (Lenski, Wright)
• dialectical mechanism (Hegel, Marx, Wallerstein)
(often related to technological advances)
Cycles in history
Recurrent patterns in the rising and falling sequence of some variable:
• civilizations/dynasties (Vico, Gibbon, Toynbee)
• population size/demographics (Turchin)
• economic waves (Kondratiev)
• spiritual development (Sarkar)
Leap theories
• Punctuated equilibrium (Eldredge & Gould)
• ‘Hopeful monster’ saltationism (Goldschmidt)
• Major transitions (Maynard Smith & Szathmáry)
Identification strategies1. Define periods of specific length beginning from
some arbitrary starting point
– e.g., BCE, the 60s
2. Pick events by particular characteristics
– e.g., the Middle Ages, the French Revolution
3. Begin with a theoretical criterion
– e.g., change in level of social organisation
‘Atoms’ of History
A science of history is possible if can identify class of objects analogous to the ‘natural kinds’ in other sciences (i.e., atoms, stars, genes, beliefs)
– periods?
Choices1. Use theoretical criterion as most likely to be scientific
2. Largest possible temporal scale to maximize chances of finding patterning in events – universal/cosmic
3. The only ‘currency’ applicable to cosmic/planetary/biological/cultural scale is energy; the common currency paying for all biological information or organisation in a self-organizing system is energy flow (Morowitz, 1979); transitions in rates of energy flow through thermodynamic systems are already established as the currency of ‘big’ history (Chaisson/Spiers)
4. Interested in explaining long-term increase in complexity, not medium-term cycles – so linear or leap theory
Maximum Entropy Production
(Paltridge, 1975; Attard, 2006; Niven, 2009)• A generalization of Boltzmann’s probability distribution to non-
equilibrium case• States that open thermodynamic systems with sufficient degrees of freedom (i.e., many elements) will converge to a
steady state at which the production of entropy is maximized given the constraints of the system
Systems far from equilibriumA wide variety of dynamical systems adjust to maximize
entropy production as a fundamental physical
necessity (Lineweaver, 2005)
Quantitative predictions
As an optimization solution, MEP ignores details of system dynamics, but nevertheless gives precise
predictions
E.g., MEP models of Earth’s climate include accurate predictions of –
• mean latitudinal air temperature
• fractional cloud cover
• meridional & vertical heat flux
• mean vertical air temperature profile
• historical latitudinal air temperature gradients
(Lorenz, 2004)
Darwinian Thermodynamics
(Ao 2005; Sella and Hirsch, 2005)
Applies Sewall Wright’s adaptive landscape idea to statistical
mechanics
Fitness or entropy is maximized, subject to constraints
Provides an intrinsically dynamic foundation for non-equilibrium thermodynamics,
which is currently limited by its foundation in equilibrium and steady-state descriptions (Ao,
2005)
MEP vs DT• Maximum Entropy Production Principle:
• a logical generalization of the second law of thermodynamics to non-equilibrium processes
• Empirically confirmed in studies of various systems of physical, chemical or biological origin
• Not contradicted by Prigorine’s minimum entropy production principle (which is a consequence of MEP in special circumstances)
• Appropriate when dynamics are difficult to observe(Martyushev and Seleznev, 2006)
• Darwinian Thermodynamic Theory:• the maximum entropy outcome is produced by the dynamics;
MEP doesn’t have a single outcome; instead the canonical (maximum) solution must be ‘chosen’ by convention
• not restricted to the linear near-equilibrium (Onsager) regime (Ao, 2005)
Steady-state response
Small flux perturbations cause control mechanisms to stabilize energy flow
rates, maintaining existing steady-state
(Kleidon, 2009)
Transitional response
If the boundary conditions shaping the optimum change, then a perturbation of the state can be amplified until the new
optimum is reached (i.e., positive feedback to the perturbation leads to a search for a new output level)
(Kleidon, 2009)
Dual responsesComplex thermodynamic systems are able
to –
• stabilize in the face of small fluctuations in energy flow (and so persist for some time)
• respond optimally to large fluctuations by finding a new steady state with higher (maximal) entropy production
The result historically should be a pattern of periodic jumps to new, higher levels of entropy production
Result
the fundamental unit of scientific ‘big’ history should be associated with a ‘leap’ in energy flow through a system far from
thermodynamic equilibrium
What are the internal mechanics of such leaps?
Control volume analysis
energy flows entropy
white elements = negative entropy
producers (i.e., perform useful
work)
black elements = positive entropy
producers (i.e. regions of high dissipation)
(Niven, 2009)
Flow-controlled system
Energy novelty
energy flows entropy
A random fluctuation in internal flux or external force will generally not lead to a sustained increase in entropy production
Energy novelty
entropy
A significant change in output is more likely to start with a new kind or level of energy flow in a system, due to a new way of extracting energy from the system’s surroundings. (Smil, 1994; Vermeij, 2004)
energy flow
energy flow
Structural adjustment
energy flow
entropy
Increased energy flow can cause new physical structures to form. Thermodynamic disequilibrium at point X induces a flow of energy toward an energy sink at point Y, producing a transient flow which increases disequilibrium at point Y. This new situation can cause a new or modified transport structure to form linking them (e.g., Belusov–Zhabotinsky reaction, self-organising nanostructures).
X Y
energy flow
Control
energy flow
entropy
• Control occurs in dynamical systems when a mechanism manipulates the inputs such that the stability of its output is increased. In particular, controls constrain the flow of energy through structures via feedback or interaction effects.
• Adjustments of control typically must follow the creation of new structures, eliminating some structures and modifying others to make them appropriate within the context of the system as a whole. (Freeman & Louçã, 2001; Turchin, 1977)
X Y
energy flow
A major energy novelty invokes several consequences, suggesting
that these groups of events are linked together and so should be
treated as inter-independent.
Such structured processes can be called ‘non-equilibrium steady-state
transitions’ (NESSTs).
NESSTs identify the time/place/mechanisms for the birth of new historical periods.
NESSTs
Scan the events covered by ‘big’ history (cosmological, geological, biological, cultural)
looking for this pattern:
Event associated with a sustained increase in energy flow (with entropy production) through a
system
followed ‘rapidly’ in the same area by an organisational event and a control event
which can be causally linked
Criteria for identifying historical ‘leap’
‘Energy flow density’:
the amount of free energy flowing through a gram of the relevant structure per second,
in ergs (Chaisson, 2001)
(NOTE: free energy [to do work] and entropy are linearly related in the Gibbs, Helmholtz and negative Massieu formulations, and so
good enough measure for general trends; e.g., S = (E-F)/T where ; S = entropy; E = internal energy; F = Helmholtz free energy; and T =
absolute temperature)
Entropy production measure
Cosmological transitions
NESSTENERGY
INNOVATION ORGANISATION CONTROLBEGINNIN
G DATEDURATION
(years)
ENERGY FLOW
DENSITY(ergs)
Atomic electron capture atomselectro-
magnetic forces
13.6997 BYA 1 x 10-11 0.005
Molecular charge exchange moleculesintra/inter-molecular
forces13.6996
BYA 1 x 10-5 .001
Stellar Proton-proton chain reaction
first generation stars
gravity vs gas pressure 13.5 BYA 100 2
Planetary Carbon/Oxygen/ Nitrogen cycle
solar systems with planets
gravity vs solar wind 13.3 BYA 100,000 20
Galaxy quasars/ black holes galaxies
gravity vs interstellar
wind13.0 BYA 100 M 50
Cosmological NESST
TRANSITION ASPECT NOVELTY FUNCTION DATE PLACE
Electron (Atomic)
Transitionenergy
electron capture by
nuclei
neutralize atomic charge, separate
matter and energy
13.6997 BYA
‘our’ universe
organisationatoms
(hydrogen, helium)
electrically neutral and hence
complex, stable matter
13.6997 BYA
‘our’ universe
controlelectro-
magnetic forces
nucleus/electron structural mediation
13.6997 BYA
‘our’ universe
Geological Transition
NESSTENERGY
INNOVATION ORGANISATION CONTROLBEGINNING
DATEDURATION
(years)
ENERGY FLOW
DENSITY(ergs)
Complex planet heat convection
layers of solid/liquid/ga
seous material
temperature/ pressure
differentials11.8 BYA 1 B 75
Population genetics = statistical mechanics
‘There is a close analogy between statistical thermodynamics and the evolution of allele frequencies under mutation, selection and random drift. This analogy brings together previous ideas in a general framework, and
justifies a maximum entropy approximation to the dynamics of quantitative traits.’
• Wright's formula for the stationary distribution of allele frequencies is analogous to the Boltzmann distribution in statistical physics.
• Population size plays the role of the inverse temperature and determines the magnitude of random fluctuations.
• Log mean fitness tends to increase under selection, and is analogous to (negative) energy.
• An entropy can be defined which measures the deviation from the distribution of allele frequencies expected under random drift alone.
• The sum gives a free fitness that increases as the population evolves towards its stationary distribution.
(Barton & Coe, 2009; building on Ao 2005, 2008)
Inevitable life
Self-organised systems produce more entropy, more quickly, than a disordered collection; thus MEP favours increasing
degrees of order
E.g., under the conditions of early Earth, life was the best way to release the build-up of geothermal energy. (Morowitz &
Smith, 2007)
Dissipative structures which produce high entropy – including living systems – can be viewed as highly probable
phenomena. (Dewar, 2005)
Biological Transitions
NESSTENERGY
INNOVATION ORGANISATION CONTROLBEGINNING
DATEDURATION
(years)
ENERGY FLOW
DENSITY(ergs)
Cell metabolism cell genetic code 3.5 BYA 500 M 1,000
Complex cell
mitochondria, chloroplasts,
lipidsEukaryote recombi-
nation/ sex 2 BYA 250 M 3,000
Multi-cellsecondary
aerobic reactions
multi-cellular organism
pattern codes;
neuronal nets
700 MYA 150 M 10,000
Biological NESST TRANSITION ASPECT NOVELTY FUNCTION DATE PLACE
Metabolic(Cell)
Transition
energy metabolism (e.g., photo-synthesis)
chemical reaction that produces
energy in a form harnessed by other
processes
3.75 BYA Planet Earth; deep sea vents?
organisation membrane protected micro-environment
3.6 BYA Planet Earth
control genetic code (e.g., RNA,
DNA)
self-catalyzing inter-generational
information storage system
3.25 BYA(?)
Planet Earth
‘metabolism first/compartment intermediate/replication last’ position
‘Culture evolves as the amount of energy harnessed per capita per year is increased,
or as the efficiency of the instrumental means of putting energy to work is
increased’
(Leslie White, 1949)
Cultural Transitions
NESSTENERGY
INNOVATION ORGANISATION CONTROLBEGINNING
DATEDURATION
(years)
ENERGY FLOW
DENSITY(ergs)
Tool(learned) tool
use and manufacture
parental unit cluster
tonally variant call system 25 MYA 10 M 20,000
Fire fire use bandnormative
social systems (culture)
2.5 MYA 1 M 40,000
Multi-tool tool kits tribe grammatical language 500,000 200,000 60,000
Complex tool
compound tools (e.g., bow-and-
arrow)
‘Big Man’ society
iconic representation
(cave art)50,000 20,000 70,000
Agriculturecultural
symbiosis (animal/plant
domestication)
chiefdom/ city-state
symbolic representation
(writing, mathematics)
10,000 5,000 85,000
Cultural NESST TRANSITION ASPECT NOVELTY FUNCTION DATE PLACEAgricultural (Chiefdom)Transition
energy animal domestication/
plant cultivation;metallurgy;
irrigation
increased regularity of dietary intake
(domesticated species); stronger tools; increased
ecological capacity
10,000 YBP
Middle East, Central
America, Asia
organisation chiefdom/ city-state
institutionalized leadership with power to
collect, store, and distribute surplus
resources
7,500 YBP
Middle East, Central
America, Asia
control symbolic representation
(cuneiform writing,
alphabet); legal system;
mathematics; money
sophisticated extra-somatic memory;
regulation of social relations on principles
other than kinship; system for managing technical
information; coordination of market exchange
5,000YBP
Middle East, Central
America, Asia
Technological Transitions
NESSTENERGY
INNOVATION ORGANISATION CONTROLBEGINNING
DATEDURATION
(years)GAP
(years)
ENERGY FLOW
DENSITY(ergs)
Machine
watermill/windmill; ‘Medieval agriculture
cycle’
autocratic (feudal) state
measuring instruments (e.g., clock)
1,000 500 3,000 100,000
Steam steam enginedemocratic
nation-state; corporation
canal, road and rail systems
250 50 250 500,000
Electric electricityinternational
cartels; industrial
research labs
telegraph/telephone;
railroad networks;
bureaucracy
150 30 50 1 M
Engineoil/internal combustion
engine
multinational agency/corporat
ion
mass media (radio, TV);
mass production
90 30 30 1.6 M
Nuclear nuclear reactors
global markets; World Wide
Webdigital media 40 25 20 3.2 M
Technological NESST TRANSITION ASPECT NOVELTY FUNCTION DATE PLACE
Machine (Second
Agricultural) Transition
energy watermill/ windmill; Medieval
“agriculture transition”
muscles largely replaced as energy
source; higher productivity levels
1,000 YBP
Europe, China
organisation autocratic (feudal) state
centrally directed super-institution
800 YBP
Europe, China
control “measuring instruments”
(mechanical clock, astrolabe);
printing press; science
“active” information processing by
artifacts; widespread
dissemination of information;
organized knowledge acquisition
500YBP
Europe, China
Hierarchy of timeEON ERA PERIODCosmological Physical Atomic
StellarPlanetaryGalaxy
Geological Complex planetaryAtmospheric
Terrestrial Biological CellComplex cellMulti-cell
Cultural ToolFireMulti-toolComplex toolAgriculture
Technological MachineSteamElectricEngineNuclear
Perasmology
‘Big’ history covers cosmological, planetary, biological and cultural history
– but as a single narrative (Christian, 2004)
Use of the NESST concept and non-equilibrium thermodynamics produces a
scientific ‘big’ history(Etymology: Perasma = Gk “transition or crossing”)
TechnologicalCulturalBiologicalPhysicalCosmological
0
1
2
3
4
5
6
7
8
9
10
024681012
Log Years Before Present
Lo
g Y
ears
/Erg
s
NESST Duration
NESST Gap
Energy Flow Density
Origin of life
-10
-8
-6
-4
-2
0
2
4
6
8
10
NESST Gap
NESST Duration
Energy Flow Density
10.14 10.13 10.12 10.11
Eras in PerasmologyGeo-
logical
Trend analysis
increasing complexity (more degrees of freedom)increasing hierarchyincreasing levels of energy disequilibrium
TIME
Terrestrial/OrganicNESSTs
accelerate
CosmologicalNESSTs
decelerate
Eras in Perasmology
ERA SOURCE OF POWER
INFORMATION INHERITANCE
SYSTEMSPhysical stellar fusion ––Geological convection ––Biological metabolism geneticCultural (wielded) tools + socialTechnological machines + artefactual
Major transitions in biology
Generalizing MTT
• NESST model extends ‘major transition theory’ (Maynard Smith and Szathmáry, 1995) into cosmological and geological time
• Turns MTT transitions into components of a larger explanatory framework (NESST)
• Shows how ‘egalitarian’ and ‘fraternal’ transitions (Queller, 1997) are related – as organisational and control elements of NESSTs, respectively
• e.g., multi-cellular organism is organisation in multi-cellular NESST; sexual reproduction is control element of complex cell NESST
Conclusions• The NESST concept and non-equilibrium
thermodynamics provide a framework – strongly grounded in physical and biological theory – which can be used to explain macro-historical trends since the origin of the universe
• This framework allows rigorous identification of historical ‘leaps’, which define periods beginning with a NESST
• ‘Big’ history becomes a scientific discipline: ‘Perasmology’
• Historical trends in energy, organisation and information are linked in a single framework, generalizing MTT
• NESSTs accelerate once information inheritance is invented
Acknowledgements
• Prof. Walter Alvarez, Earth and Planetary Sciences, University of California, Berkeley
• Prof. Ping Ao, Mechanical Engineering, University of Washington
• Prof. Eric Chaisson, Physics and Astronomy, Tufts University
ReferencesAo, Ping (2005) Laws in Darwinian evolutionary theory. Physics of Life Reviews 2, 117–
156
Attard, P. (2006) Theory for non-equilibrium statistical mechanics, Phys. Chem. Chem. Phys. 8: 3585-3611
Barton, N.H. and J.B. Coe (2009) On the application of statistical physics to evolutionary biology Journal of Theoretical Biology, 259:317-324
Barton, N.H. and de Vladar, H.P. (2009) "Statistical Mechanics and the Evolution of Polygenic Quantitative Traits" Genetics 18(3):997-1011
Martyushev, L.M. and V.D. Seleznev (2006) Maximum entropy production principle in physics, chemistry and biology Physics Reports 426:1 – 45
Niven, Robert K. (2009) Steady State of a Dissipative Flow-Controlled System and the Maximum Entropy Production Principle Phys. Rev. E 80, 021113
Paltridge, G.W. (1975) Quart. J. Royal Meteorol. Soc. 101, 475.
Sella, Guy and Aaron E. Hirsh (2005) The application of statistical physics to evolutionary biology PNAS 102: 9541–9546
Ziegler, H. (1983) An Introduction to Thermomechanics, North-Holland, Amsterdam,
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