Complex Adaptive Systems,

Computational Social Science

andIrregular Warfare

Rob AxtellChair, Dep’t. of Computational Social Science

George Mason Universityand

External Professor, Santa Fe Institute

Outline

Complex Adaptive Systems (5 minutes)

Computational Social Science (5 minutes)

Computational Public Policy (5 minutes)

Regular Warfare as Simple (5 minutes)

Irregular Warfare as Complex (5 minutes)

Modeling Irregular Warfare (5 minutes)

Complex Systems Thinking

Nonlinear dynamical systems (math):

Chaos (high irregularity from low-dimensional, deterministic system)

Distributed, decentralized systems (CS):

Complexity (simple regularity from high-dimensional, stochastic system)

Early Ideas of Complexity

(Santa Fe Institute style)

Physicists + computer scientists + economists + biologists (evolutionary, ecological)

Artificial life (von Neumann -> Burks -> Langton)

Self-organization, emergence, spontaneous order (including self-organized critical systems)

Evolutionary algorithms, genetic programming

Artificial markets, artificial societies

Social Complexity: Micro

Heterogeneous agents

Bounded rationality

Interaction through networks

Action away from equilibrium

Realized computationally

Social Complexity: Macro

Social systems are multi-level systems

Agent modeling is a macro-scope (C Langton)

Can’t infer properties of the macro from the micro specification: fallacy of composition

Can’t infer properties of the micro (agents) from what emerges at the macro-level: fallacy of division

Social sciences are the hard sciences (H Simon)

Simple ➜ Complex(20th C) (21st C)

Single decision-maker

Scalar value function, first-order conditions, numerical solutions

Decision theory

Mean field, averages

Equilibrium, fixed point theorems

Continuous, smooth mathematics

Command-and-control

Multiple agents

Heterogeneous utilities, purposive behavior, agent models+simulation

Game theory

Networks, extremes

Volatility, adaptation, co-evolution

Discrete mathematics, computation

Bottom-up emergence

Agent Computing

Population of adaptive software objects...

...That interact socially

...Following (simple) rules of behavior

...Macrostructure EMERGES from the interactions of the agents

OR ⊂ MAS

Operations ResearchSingle formal representationExtremize scalar value functionYields normative prescriptions (policies)

Agent modeling/Multi-agent SystemsEach agent has its own internal model......and acts to improve its value functionKey: what emerges at the social level?Both positive and normative aspects

Agents in Practice

Abstract models: Sugarscape

Empirical models: ‘Artificial Anasazi’

Policy-relevant models: 3 1/2 successes

Traffic

Epidemiology

Combat

Finance

SugarscapeAbstract model for developing intuitions

Simple agents who

forage for resources on a landscape

Emergent order

Simple maize-growers who occupied the Colorado Plateau for 1000 years

Disappeared c 1300

Artificial AnasaziEmpirical model for generating explanations

Pre 9-11: Mathematical

Post 9-11: Computational

Smallpox (White House)

SARS (WHO)

Flu (HHS)

Policy-makers have driven the adoption of computational tools

EpidemiologyPolicy model for making predictions

Finance

NASDAQ used to trade in 1/8s and 1/16s

SEC req’d decimalization

NASDAQ management commissioned an agent simulation in ADVANCE of the rule change

Predicted 6 of 8 statistical changes

Regular Warfare: Simple

Lanchester equations model force-on-force annihilation

Can be calibrated for hand-to-hand combat, armored combat (e.g., tank v tank), etc.

Warfare has evolved!

Regular vs Irregular Warfare

Regular warfare is like a parade: choreographed, scripted, orchestrated

Irregular warfare is like city life outside the parade: distributed, wild

Need to understand the society in which irregular war happens

Irregular Warfare is a Complex Adaptive

SystemAgent-based combat an active area of researchAgent models are new way to do social scienceThey are capable of representing ALL the main ideas of complex adaptive systemsBut we are very far away from complete models in social science

Agent Models for Afghanistan

Origin and growth of the Taliban

Poppy agriculture/opium production

IED construction, placement, sensing

Most GIS-savvy

What is Needed?

Better behavioral, cognitive, neural models: experimental economics, cog sci, neuroscience

Better social process models: tribal dynamics, identity choice, growth

More HSCB research

Coherent 10 year research effort req’d.

Digression on Research Funding in Economics

NSF:Math/Physics: $1.5BOcean + Polar: $1.0BSocial, Behavioral and Economic Sciences: $250MSocial+Economic Sci: $100MEconomics: $30M

Macro+Finance: $5-6M

Fed: $50-100M

Treasury + SEC: $0Cost of the Financial

Crisis: $2-20T

What is Not Yet Possible in Irregular

WarfareHigh-fidelity models

Prediction of point outcomes, e.g., specific event @ (x, t)

Small N forecasting

Quantitative comparison of tactics

Long run prognostication

How to Tell the Possible from the

ImpossibleProject team is majority physicists, computer scientists and engineers

Discussion focuses on IT

Behavior, is ‘representative,’ not variegated (e.g., SD)

Output is mostly ‘eye candy’

No matter what you ask for they can model it (i.e., no sense of hard vs easy tasks)

Unwillingness to ‘open source’ their code

Summary

Agent models, grounded behaviorally, are the way social science will be done, eventually

They are ‘normal science’: abstract, empirical and predictive/policy versions all possible

Problem is not that the methodology is limiting--there is nothing it can’t do!

Bottleneck is cognitive/behavioral foundations

Snake oil salesmen are out there!

A large, many-year research effort is needed