norman lee johnson chief scientist referentia system inc. honolulu hawaii [email protected]
DESCRIPTION
Complexity: A Diverse Description . Norman Lee Johnson Chief Scientist Referentia System Inc. Honolulu Hawaii [email protected] http:// CollectiveScience.com. ONR Workshop on Human Interactions in Irregular Warfare as a Complex System Apr 2011. My Background. Future of the internet - PowerPoint PPT PresentationTRANSCRIPT
Norman Lee JohnsonChief Scientist
Referentia System Inc.Honolulu Hawaii
[email protected]:// CollectiveScience.com
Complexity: A Diverse Description
ONR Workshop on Human Interactions in Irregular Warfare as a Complex System Apr 2011
Diversity
Star Wars Novel fusion device Novel diesel engine Hydrogen fuel program P&G – Diapers Large scale
Epidemiology – Flu modeling
Biological threat reduction
Bio-Risk assessment Cyber security
Future of the internet
Self-organizing collectives Diversity and collective
intelligence– Finance applications
Effects of rapid change– Finance applications
Group identity dynamics– Coexistence applications
Leadership models
Bio-cyber analogies
My Background
Counterinsurgency in Iraq:Theory and Practice, 2007
David Kilcullen
Available online at: http://smallwarsjournal.com/documents/kilcullencoinbrief26sep07.ppt
© David J. Kilcullen, 2007
Caveat: the logic of field observation in Iraq
+ Everyone sees Iraq differently, depending on when they served there, what they did, and where they worked. • The environment is highly complex, ambiguous and fluid• It is extremely hard to know what is happening – trying too hard to find
out can get you killed…and so can not knowing• “Observer effect” and data corruption create uncertainty, and invite bias • Knowledge of Iraq is very time-specific and location-specific• Prediction in complex systems (like insurgencies) is mathematically
impossible…but we can’t help ourselves, we do it anyway
+ Hence, observations from one time/place may or may not be applicable elsewhere, even in the same campaign in the same year: we must first understand the essentials of the environment, then determine whether analogous situations exist, before attempting to apply “lessons”.
Diversity
Levels of Social complexity
slime molds
“low” social
insects
“high” social
insects
social mammals
“low” apes
“high” apes humans
Identity, diverse, decentralized, collective survival and problem solvingCollectively adaptable, self-organizing, emergent properties
IndividualSelf-awareness &
Consciousness
Collective: Memory, intelligence, deception, tools
Individual: High intelligence, deception & emotions, tool making
From a workshop on “The Evolution of Social Behavior” which covered a wide range
of social organisms
Example: All social organism when stressed are
“programmed” to copy the behavior of others in the
“organism”
Complex Human Dynamics: Three Challenges
System Analysis
• How different analysis perspectives can simplify
the “complexity” of the system attributes or
dynamics?
Behavioral-Social Models
• How abstract models can help simplify the
“complexity” of individuals components
• OR provide relationships between components?
© David J. Kilcullen, 2007
“Getting it” is not enough
“[This] is a political as well as a military war…the ultimate goal is to regain the loyalty and cooperation of the people.” “It is abundantly clear that all political, military, economic and security (police) programs must be integrated in order to attain any kind of success”
Gen William C. Westmoreland, COMUSMACV, MACV Directive 525-4, 17 September 1965
Understanding by leaders is not enough: everyone needs to understand, and we need a framework, doctrine, a
system, processes and structures to enact this understanding.
Complex Human Dynamics: Three Challenges
System Analysis
How difference perspectives can simplify the
“complexity” of the system attributes or dynamics?
Behavioral-Social Model
How abstract models can help simplify the “complexity” of individuals components OR
provide relationships between components?
Tools for Decision Makers
How the above understandings lead to actionable knowledge for
decision makers?
Complex Human Dynamics: Three Solutions
System Analysis • Connection between local global
Behavioral-Social Model Features• Individual behavior models
Tools for decision makers
Habitual repetition: Classical conditioning theory (Pavlov), Operant conditioning theory
(Skinner) Individual optimization of decision:
Theory of reasoned action (Fishbein & Ajzen), Theory of planned behavior (Ajzen)
Socially aware: Social comparison theory (Festinger), Group comparisons
(Faucheux & Mascovici) Social imitation:
Social learning theory (Bandura), Social impact theory (Latané), Theory of normative conduct (Cialdini, Kalgren & Reno)
CONSUMAT model -- Marco Janssen & Wander Jager – Netherlands
Individual preference + Social drives + Options + Rationality = ?
What drives the changes?
Repeater Deliberator
Imitator Comparer
Satisfied Dissatisfied
Uncertain
Certain
Historicalcomparison
Increasedstress
Individual Behavior + Network = Global Dynamics
1000 Consumers with the same behavioral tendencybuying 10 products on a small-world network
Population of “Repeaters” - satisfied and certain
0
0.1
0.2
0.3
0.4
0.5
0.6
0 10 20 30 40 50 60 70 80 90 100time steps
market shares of products
Few products of equal distribution - highly stable
Repeater
Closest to “Rest state”
Population of “Imitators” - satisfied but uncertain
0
0.1
0.2
0.3
0.4
0.5
0.6
0 10 20 30 40 50 60 70 80 90 100time steps
Few products of unequal distribution - highly stable
Imitator
Transitional individual => social state
Population of “Deliberators” - dissatisfied but certain
0
0.1
0.2
0.3
0.40.5
0.6
0 10 20 30 40 50 60 70 80 90 100time steps
High volatility on all products
DeliberatorClosest to Homo Economicus
High rationality, low social
Population of “Comparers” - dissatisfied & uncertain
0
0.1
0.2
0.3
0.4
0.5
0.6
0 10 20 30 40 50 60 70 80 90 100time steps
market shares of products
Volatility over long times on few productsBut difficult to maintain - high energy state
ComparersSocial and Rational
“habitual” agent
Highly stable withsustained diversity
Homo Economicus
High volatility
Social and Rational
Longer time volatility - difficult to sustain
Socially driven
Highly stable - decreased diversity
Repeater Deliberator
Imitator Comparer
Complex Human Dynamics: Three Solutions
System Analysis • Connection between local global
• simple models can lead to complex global behavior
• Study of system thresholds
Behavioral-Social Model Features• Individual behavior models
• Drivers & threshold transitions• Habitual behavior
• Importance of habitual behavior & individual threshold transitions
Tools for decision makers• Focus on system thresholds first
• rather than quantifying the details between threshold states
Rat Studies of Maximum Carrying Capacity
Social order system can carry 8 times the optimal capacity before going over the threshold.
NIMH psychologist John B. Calhoun, 1971
Control group - no “rules” => Your worst nightmare
One “social” rule =>Cooperative social structure
Both systems loaded to 2 1/2 times the optimal capacity.
Diversity
Simple Ant Foraging Model
Key concepts:Emergence, Productivity, Diversity, Structure
Using NetLogo
Collective informationEvaporationDiffusionAgent internal state: Current direction Have food? Three rules of action:Carry foodDrop foodSearch n “Productive men”
n “Salaried men” n Innovator n Collective structure
Nest
Food supply
Diversity
Quantified Environmental Change
Infinite source moves at afixed radius
and fixed angular velocity
Diversity
Slowly changing environment
Productivity is only slightly less than an unchanging source
Herd effect allows for quick utilization of new resource location
Innovators are important at all times by sustaining optimal performance of the collective
0.00
0.20
0.40
0.60
0.80
1.00
1.20
0 1 2 3 4 5 6 7 8Rate of environmental change (tenth of degree/time unit)
Production rate (food units/time units)
Collective
Individual
Formative
Co-Operational
Condensed
Food Production Rate
Effect of Rate of Change on System Development
Complex Human Dynamics: Three Solutions
System Analysis • Connection between local global• Study of system thresholds• Developmental perspective• Emergent behavior via multi-level
analysis
Behavioral-Social Model Features• Individual behavior models
• Drivers & threshold transitions• Habitual behavior
• Emergent problem solving
Tools for decision makers• Focus on system threshold changes first
Diversity
Structural Efficiency - Boom and Bust
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
Rate = 0.3
Structural efficiency
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0 1000 2000 3000 4000 5000
Structural efficiency
Time (time unit)
Rate = 0.0
Rate = 0.8
Lower average production crash avoidance strategy
Bust is proceeded by increased production
Greater minimums and maximum when compared to extreme rates!
Diversity
Tota
l pro
duct
ion
(uni
ts o
f foo
d)
Time (time units)
For the slowest rate of change
0
500
1000
1500
2000
2500
0 500 1000 1500 2000 2500 3000
transient structure
sustained structure
Combination of Sustained Structure and Change
How does the retention of structure change the collective response?
Suggests that fixed evolutionary adaptations lead to inefficiencies in the presence of even small rates of change
What would be the effect of a faster worker?
What would be the effect of mass communication?
Prigogine’s Laws of stasis, change and evolution:
Original observations for chemical systems1. Equilibrium states are an attractor for
non-equilibrium states. 2. System near equilibrium cannot evolve
spontaneously to generate spatial−temporal (dissipative) structures
3. As the system is driven far from equilibrium, it may become unstable and generate spatial−temporal structure from nonlinear kinetic processes associated with flows of matter and energy.
4. The possibility of new structures is determined by the system size.
5. Bifurcation introduces “history” into the model (trajectories are replaced by processes). Every description of a system which has bifurcations will imply both deterministic and probabilistic elements.
Generalized observations 1. Perturbed systems will return to
their normal state. 2. The mechanisms for permanent
change are not accessible to systems near equilibrium.
3. Bang on a system hard enough, existing structures can be replaced by new structures.
4. The evolution of new structures are limited by system size.
5. Multiple outcomes change certainty into probabilities, and require a fundamentally different approach.
Complex Human Dynamics: Three Solutions
System Analysis • Connection between local global• Study of system thresholds• Developmental perspective• Emergent behavior via multi-level
analysis• Optimization vs. robustness
Behavioral-Social Model Features• Individual behavior models
• Drivers & threshold transitions• Habitual behavior
• Emergent problem solving• Individual and collective structure
Tools for decision makers• Focus on system threshold changes first
Structure in a system increases over time for decentralized, self-organizing collectives (nature, societies, technologies)
Structure(e.g., the rules
required to “run” the system)
Time
Structure declines because the number of new rules are limited by past rules.
Structure increases first by components developing structure
Structure increases rapidly as components build structure together
Diversity
The Structure of Structures
Description Determinesevolutionarypath directly
Retainedonce
expressed
Alwaysexpressed
Origins:Random,
Direct,Emergent
Experiential or transient featuresLearning – Ant position
R
Shallow surface featuresColoring – Collective solution
X R
Deep surface structures (frozen ``accidents’’)Specific DNA coding
X X D,E,R
Deep system structures (frozen organization)Digital coding, nucleus formation
X X X D,E
Features reflecting fundamental lawsHydrogen bonding
X X X D
Structures direct the evolution of the system by creating and limiting potential optionsTheir definition depends on the time constant of exogenous/endogenous change.
Options around Structure also change
Structure(the rules
required to “run” the system)
Time
Little initial structure means few Options
Options are greatest when structure connects the components
These ideas are captured by researchers studying “infodynamics”
Options are the free choices both created and limited by the structure (example: the rules of chess create an “environment” where many options are possible - while also limiting what choices are available)
Options
Options are reduced as more structure restricts options
Difference between Options and Diversity
Structure
Time
Diversity and Options are low
Diversity and Options are high
These ideas are captured by researchers studying “infodynamics”
• Diversity is the the unique variety in the system• Options are when the diversity has multiple expressions of
differences, often expressed as multiple connectivity in the network
Options
Diversity may be very high but Options are low
Adaptability and Robustness of System
Structure
Time
Diversity
Both adaptable and robust
These ideas are captured by researchers studying “infodynamics”
• Robustness is a system-level ability to sustain performance in the presence of change
• Adaptability is a component level ability to accommodate change
Options
Not robust or adaptable, but existing components can be rearranged for new features
Robustness achieved by component replacement
Diversity
Collective Response to Environmental Change
Unimpeded development
Innovators are essential
Collective actions lead to inefficiencies
Potential system-wide
failure
Condensed (optimization of
collective)
Co-Operational (synergism from
individuals)
Formative (creation of
individual features)
Featureless
Stable“no change”
Change slower than collective
response
Change faster than collective
response
Change faster than individual
response
Rate of Environmental Change
Stag
es in
Dev
elop
men
t
Diversity
Why Care about Structure-Options?
Studies of thresholds in structure:
– Prigogine’s Laws of Stasis, Change and Evolution– Joseph Schumpeter’s Creative Destruction – Foster and Kaplan "Creative Destruction: Why
Companies that are Built to Last Underperform the Market - And how to Successfully Transform Them”, 2001
– John Padgett life’s work on innovation in the Florentine (and world) finance system
Dynamic “structural” thresholds do the same
Complex Human Dynamics: Three Solutions
System Analysis • Connection between local global• Study of system thresholds• Developmental perspective• Multi-level analysis & emergence• Optimization vs. robustness• Interplay of structure and options
Behavioral-Social Model Features• Individual behavior models
• Drivers & threshold transitions• Habitual behavior
• Emergent problem solving• Individual and collective structure
Tools for decision makers• Focus on system threshold changes first• Capture structure and options:
• What has to be removed before change can occur
• Diversity is not the same as options!
© David J. Kilcullen, 2007
“15. Do not try to do too much with your own hands. Better the Arabs do it tolerably than that you do it perfectly. It is their war, and you are to help them, not to win it for them. Actually, also, under the very odd conditions of Arabia, your practical work will not be as good as, perhaps, you think it is.”
T.E. Lawrence, “Twenty-Seven Articles”, The Arab Bulletin, 20 August 1917
Remember article 15
Diversity
Expert Performance in Finance
Why can’t financial experts outperform the S&P 500 “collective” – good + bad – consistently?
• Professional money managers fail to beat the S&P 500 at an average rate of 70% per year.
• 90% trail the S&P over a 10-year period.
• Over decades are only a few – Soros, Miller, ….
“These are the people who have more knowledge and more training than the vast majority of investors. And yet, neither the superior knowledge nor the superior experience helps them in the long run.”
Bill Mann, TMFOtter
The ant colony (and individuals) finds the shortest path
Nest
Food
Nest
Food How does it work?
Ants Solving “HARD” problems
Start
EndIn “Learning” the maze, individuals create a diversity of experience.
A Model for Solving Hard Problems
How can groups > solve hard problems,> without coordination,> without cooperation, > without selection?
The Maze has many solutions > non-optimal and optimal.
Individuals > Solve a maze> Independently> Same capability
When individuals solve the maze again, they eliminate “extra” loops
But because a global perspective is missing, they cannot shorten their path. This is were diversity helps.
How collectives find the Shortest path
Paths of three ants Collective path
Unlike in natural selection, no one individual is the fittest!
Ensemble (Averaged) Behavior
.
Individuals in Collective Decision
Nor
mal
ized
num
ber o
f ste
ps
0 5 10 15 20
0.9
1.0
1.1
1.2
1.3
0.8
Average Individual
Using novice information, with two different collections
Usingestablishedinformation
Performance correlates with high unique diversity
Diversity
Expert Performance & Complexity
Where Experts Have Value
Simple ComplexDomain Complexity
Valu
e of
Exp
erts
Michael Mauboussin - Legg Mason Capital Management
Valu
e of
Col
lect
ives
Complexity Barrier
From a workshop on Complex Science for the Physician’s
Alliance
Effect of Complexity in Stable Systems
Structure(the rules
required to “run” the system)
time
System goes to optimization via“expert” route
“Complexity Barrier” requires
Collective Solutions
X
System goes to optimization via
“collective” route
Collective Error = Average Individual error
minusPrediction Diversity
“The Difference:How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies”
Diversity
Collective Performance
Where Experts Have Value
Simple ComplexDomain
Valu
e of
Exp
erts
Michael Mauboussin - Legg Mason Capital Management
Valu
e of
Col
lect
ives
Collective Error = Average Individual error
minusPrediction Diversity
Diversity
Options in infrastructure, societal structure, economies, etc.
Collectives in complex environments
begin
end• • • • • • • •
In complex domains: • People beginning points differ• Their final goals may differ• But local paths can overlay and find synergy
© David J. Kilcullen, 2007
Why counterinsurgency is population-centric
+ This is not about being “nice” to the population, it is a hard-headed recognition of certain basic facts, to wit:• The enemy needs the people to act in certain ways (sympathy,
acquiescence, silence, provocation) -- without this insurgents wither• The enemy is fluid; the population is fixed – therefore controlling the
population is do-able, destroying the enemy is not• Being fluid, the enemy can control his loss rate and can never be
eradicated by purely enemy-centric means (e.g. Vietnam VC losses) • In any given area, there are multiple threat groups but only one local
population – the enemy may not be identifiable but the population is.
Terrain-centric and enemy-centric actions are still vital and crucial to success. Enemy and Terrain still matter, but Population is the key.
Complex Human Dynamics: Three Solutions
System Analysis • Connection between local global• Study of system thresholds• Developmental perspective• Multi-level analysis & emergence• Optimization vs. robustness• Interplay of structure and options
Behavioral-Social Model Features• Individual behavior models
• Drivers & threshold transitions• Habitual behavior
• Emergent problem solving• Individual and collective structure• Conditions for synergy/conflict• Group identity models
Tools for decision makers• Focus on system threshold changes first• Capture structure and options• New social consensus tools
Diversity - source of conflict or synergy?
Diversity can lead to synergy when collectives have:• Common goals
• Common group identity
• Common worldview (agreement on options), but with different preferences or goals
Otherwise, diversity can lead to competition and conflict
Mor
e re
stric
tive
© David J. Kilcullen, 2007
Governance, development, democracy are not universal goods
“There is no such thing as impartial governance or humanitarian assistance. In this environment, every time you help someone, you hurt someone else.”
General Rupert Smith, Commander UNPROFOR 1995
The enemy will perceive actions by political staff, NGOs, economic and development staffs, PRTs and government officials as a direct challenge to
grass-roots control over the population, and will react with violence
Diversity
Why Care about Group Identity?
Social organisms have a strong drive to form group identity:
“... experiments show that competition is not necessary for group identification and even the most minimal group assignment can affect behavior. ‘Groups’ form by nothing more than random assignment of subjects to labels, such as even or odd.”
Group Identity can be the dominant factor of behavior: “Subjects are more likely to give rewards to those with the same label
than to those with other labels, even when choices are anonymous and have no impact on their own payoffs. Subjects also have higher opinions of members of their own group.”
Akerlof, G. A. and R. E. Kranton (2000). “Economics and Identity.” Quarterly Journal of Economics 115(3): 715-753.
Identity: Assertions and Definitions
Identity
Group Identity == Mechanism of Group Immunity Common definition: if someone does something to a person in your
identity group, it is the same as if they did it to you. Working definition: Identity is the individual behavioral bond/process that
creates a “group self” that has all of properties of an individual self. Assert: Group identity in higher social organisms can be an abstraction
that detaches from the origin of the identity group.
Despite the long-standing recognition of the importance of identity in social systems, most studies of identity are observations of identity's influence on individual and group behavior, rather than understanding the processes by which identity forms and modifies behaviors.
Questions from an Identity Perspective
Identity
What are the characteristics of identity groups? What are their dynamics? Formation, stability, coalescence,
expression, polarization, influence, dissolutionHow does group identity affect the acceptance, adaptation, spread and exploitation of an
idea? Does the rapid spread of an infectious idea (e.g., a fad) require a common group
identity? What are the conditions that cause identity groups to become destructive to other
groups or to factions within a group? How does a ”leaderless group” group use identity to self-organize? How does insurgent news/press of “violence on self” polarize the “self”? How does diversity affect identity formation? Performance? Stability? As diversity
decreases, will tolerance decrease? What are the conditions necessary for coexistence to emerge between identity groups? How does one really build a democratic nation from fractionalized groups?
Answers, or at least a beginning of an understanding, of questions like these will help to inform policy regarding intra and international negotiations and other actions designed to bring about the resolution of conflict. It will aid them in identifying unintended consequences of hasty actions, such as the use of extreme force. Or, how to prevent violent conflict and grow the conditions for peace.
Characteristics of Identity Groups
Identity Groups (IDGs) express a common worldview – an understanding of how the world works: what are options and what is forbidden
IDGs have a shared, unspoken knowledge that is typically unknowable outside the IDG
IDGs often have symbols of association such as dress or language differences, often unobserved by others
IDGs – when in larger and sustained groups – develop culture and civilizations
While most of us are born or develop within existing IDGs, we also form many identity groups during our lives.
Identity Groups under Stress
Stress – a heightened state of anxiety about one’s current state that might originate from outside the IDG (e.g., oppression) or from within (e.g., internal dissension) – can cause the IDG to act as a single organism (“circling up the wagons”)
Stressed Identity Groups:Are more likely to reject ideas coming from outside the IDGOppress, reduce, or prohibit expression of diverse ideas within the IDGMake irrational actions that are potentially self-destructiveCan “dehumanize” individuals and groups that represent opposition IDGsAre in a state that can lead to polarization, particularly if “outside” IDGs are
well defined, are in opposition and are creating the stress Can be strongly influenced by a leader (or idea!) that represents the IDG,
particularly potential “martyrs” that have received the brunt of the oppression or violence from the opposing IDGs.
Application - Tipping Point
Law of the FewIdentity largely determines the social networkIdentity coherence determines the success of
trendsetters to represent the the group and the sticky idea
Trendsetters often span multiple identity groups
Stickiness FactorThe “stickiness” strongly correlates with the
resonance with identity Ability to jump across multiple identity groups
determines widespread propagationAn idea that is sticky to an opposing identity group
will be aborted and demonized.
Complex Human Dynamics: Three Solutions
System Analysis • Connection between local global• Study of system thresholds• Developmental perspective• Multi-level analysis & emergence• Optimization vs. robustness• Interplay of structure and options
Behavioral-Social Model Features• Individual behavior models
• Drivers & threshold transitions• Habitual behavior
• Emergent problem solving• Individual and collective structure• Conditions for synergy/conflict• Group identity models• Descriptive vs. predictive models
Tools for decision makers• Focus on system threshold changes first• Capture structure and options• New social consensus tools• Tools are changing - data rich vs. poor• Validation requirements
•Prediction of collective behavior is generally easier at extremes of diversity or variation
Diversity and Collective Prediction
LowDiversity
HighDiversity
Locally and
Globally Predictabl
e
Globally Predictabl
eUnpredictabl
e
•How does this translate to distribution functions?
Diversity and Collective Prediction
LowDiversity
HighDiversity
Locally and
Globally Predictabl
e
Globally Predictabl
eUnpredictabl
eProblem distributions: • Discrete distributions • Multi-modal distributions • Long-tailed distributions
(e.g., power law, instead of Gaussian
statistics)
p(ø)
0 1
Analysis - Increasing levels of discovery: • Ontology or qualitative characterization • Statistical characterization• Dimensionless functionality (correlations)• Scaling - self-similarity – fractal structure• Descriptive-predictive “Laws”• Functional relationships• Static• Dynamic (governing equations of change)
• Higher moments (variation within)• Error generation - uncertainty quantification
Origin of the model or “The Theory”
Structure and options
Averages and outliers
Data generation
Discovery
Complex Human Dynamics: Three Solutions
System Analysis • Connection between local global• Study of system thresholds• Developmental perspective• Multi-level analysis & emergence• Optimization vs. robustness• Interplay of structure and options
Behavioral-Social Model Features• Individual behavior models
• Drivers & threshold transitions• Habitual behavior
• Emergent problem solving• Individual and collective structure• Conditions for synergy/conflict• Group identity models• Descriptive vs. predictive models
Tools for decision makers• Focus on system threshold changes first• Capture structure and options• New social consensus tools• Tools are changing - data rich vs. poor• Validation requirements • Cost-benefit assessment with transparency
and uncertainty quantification
Some References on Prediction and Complexity
Shalizi, Cosma R., “METHODS AND TECHNIQUES OF COMPLEX SYSTEMS SCIENCE: AN OVERVIEW”, Chapter 1 (pp. 33-114) in Thomas S. Deisboeck and J. Yasha Kresh (eds.), Complex Systems Science in Biomedicine (New York:Springer, 2006) http://arxiv.org/abs/nlin/0307015
Farmer, J. Doyne & John Geanakoplos, “Power laws in economics and elsewhere”, DRAFT April 4, 2005 (chapter from a preliminary draft of a book called “Beyond equilibrium and efficiency”) - Contact the authors for a copy.
Farmer, J. Doyne, “Power laws”, Santa Fe Institute Summer School June 29, 2005. Contact the author for a copy.
Holbrook, Morris B.. 2003. "Adventures in Complexity: An Essay on Dynamic Open Complex Adaptive Systems, Butterfly Effects, Self-Organizing Order, Co-evolution, the Ecological Perspective, Fitness Landscapes, Market Spaces, Emergent Beauty at the Edge of Chaos, and All That Jazz http://www.amsreview.org/articles/holbrook06-2003.pdf
White, Douglas R., “Civilizations as dynamic networks: Cities, hinterlands, populations, industries, trade and conflict”, European Conference on Complex Systems Paris, 14-18 November 2005. http://eclectic.ss.uci.edu/~drwhite/ ppt/CivilizationsasDynamicNetworksParis.ppt
For exceptional talks on Complexity in financial systems, see the Thought Leaders Forums:
• http://www.leggmason.com/thoughtleaderforum/2006/index.asp for 2003-2006
• http://www.capatcolumbia.com/CSFB%20Thought%20Leader%20Forum.htm for 2000-2003