Complex Adaptive Systems
Introduction to Computational Models of Social Life
John H. Miller; Scott E. Page
reviewed by Talha Oz
March 2014
Miller

John H. Miller
 B.A in economics, B.S. in finance ’82 Colorado
 Ph.D. in economics Michigan ’88
 Postdoc Santa Fe Institute ’90 (‘03 Research Professor)
 Prof. CMU Social & Decision Sciences ’90’95’00
PAGE
 Math B.A ’85 Michigan & M.A. ‘88 Wisconsin
 Ph.D. Managerial Economics & Decisions Sci. ’93 NWU
 CalTech, Iowa, Michigan ’93’97’03 complex systems, political science, and economics
 SFI external faculty since ’99
Synopsis

Concepts of CAS
 emergence, selforganized criticality, automata, networks, diversity, adaptation, and feedback

How CAS can be explored
 using methods ranging from mathematics to computational models of adaptive agents

Key tools and ideas that have emerged in the CAS field since the mid1990s
introduction

Complexity. Interactions of components

Adaptivity. Intelligence of components

CAS. So many such components
 Back. Adam Smith in the Wealth of Nations (1776)
 Last decade. Tools & techniques => theories
Complexity in Social Worlds

The Standing Ovation Problem
 Economics graduate students
 si(q) = q + εi if T1< si(q) then stand up
 if α>T2 then everyone stands up.
 Friendship, location
 Heterogeneity & Feedback vs Averaging
 Negative. Bees huddling and fanning. Stability thanks to genetic diversity. (Demo)
 Positive. Attack of the killer bees. Varying response threshold [1,100]
Tiebout Model (chili demo)
 Issuebyissue
 Party based
 Winning party takes all
 Blend by weighted votes
Preliminaries
Models as maps
Snow's cholera map
On emergence
Computation as theory

Theoretical tools
 detailed verbal descriptions such as Smith's (1776) invisible hand
 mathematical analysis like Arrow's (1951) possibility theorem
 thought experiments including Hotelling's (1929) railroad line
 mathematical models derived from a set of first principles (econ.)
 employ different tools for better theories. For supply and demand:
 thought experiments using Walrasian auctioneers,
 axiomatic derivations of optimal bidding behavior,
 computational models of adaptive agents, and
 experiments with human subjects.
cont'd

Computation as a theory (theoretical tool)
 abstractions maintain a close association with the realworld agents of interest
 uncovering the implications of these abstractions requires a sequential set of computations (not computers!) involving these abstractions

Neoclassical economics
 individuals optimize their behavior
 given mathematical constraints, underlying agents in the real system are subsumed into a single object (a representative agent)
 incorporate driving forces (such as system seeks an equilibrium)
 computation is used in these models for solving numerical methods
computation as theory  II

Agentbased objects (computation as a theoretical model)
 abstractions are not constrained by the limits of mathematics
 collection of agents solved by their interactions using computations

Good models vs simulation
 simple entities and interactions vs complicated
 implications robust to large class of changes vs less robust
 surprising results that motivates new predictions vs less surprising
 easily communicated to others vs may not be that easy
Objections to Computation as Theory
Q: answers are built in to the model, cannot learn anything new !

all tools build in answers. Clarity is key here. hidden or blackbox features are bad

a model is bounded by initial framework but it can allow for new theoretical insights
Q: computational models are brittle !

crashes are not unique to computational models

can be prevented by better designs
Q: computational models are hard to understand !

due to lack of commonly accepted means for communication. UML, ODD.
Cont'd
Q: computations lack discipline !

lack of constraints is indeed a great advantage. Mathematical models become unsolvable when practitioners break away from limited set of assumptions.

a discipline similar to the one required for labexperiments is being formed: Is the experiment elegant? Are there confounds? Can it be easily reproduced? Is it robust to differences in experimental techniques? Do the reported results hold up to additional scrutiny?
 flexibility. mathematical models solved by a set of solution techniques and verification mechanisms. Given the newness of many computational approaches it will take some time to agreedupon standards for verification and validation
CONT'd
Q: They are only approximations to specific circumstances !
 Giving exact answer might not be that important; relying on approximations may be perfectly acceptable in some cases.
 Generalizability is tied to the way model created, not the medium. Bad mathematical models may not be extended beyond their initial structure too.
Why AgentBased Objects?

Flexibility versus Precision
 Verbal to mathematical tools

Process Oriented
 How agents interact, when, with whom
 What information an agent has access to

Adaptive Agents
 Rationally bounded. Learning Algorithms

Inherently Dynamic
 In natural systems, equilibria = death

Heterogeneous Agents and Asymmetry
 Heterogeneity and asymmetry accommodated easily
 Scalability
 Mathematical models for a few (duopolies) or many (perfect competition) agents
CONT'D

Repeatable and Recoverable
 Initial state can be recovered; experiments can be repeated precisely

Constructive (analogy: proof by construction vs proof by contradiction)
 Generative approach is a distinct and powerful way to do social science

Low Cost (create. Repeat)

Economic E. coli (E. coni?)
Further Investigation of cas on models
Forest Fire Model, Abbott’s Flatland, Cellular Automata, Social Cellular Automata, Majority Rules, The Edge of Chaos, A Roving Agent, Segregation, The Beach Problem, City Formation, Networks, SelfOrganized Criticality and Power Laws, Agent Behavior, Adaptation, A Taxonomy of 2 × 2 Games, Games Theory: One Agent, Many Games, Evolving Communication, The Full Monty, ...
COMPLEX ADAPTIVESYSTEMSAN INTRODUCTION TOCOMPUTATIONAL MODELSOF SOCIAL LIFE
By Talha OZ
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