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

  • Scott E. 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, self-organized 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 mid-1990s

    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)
    • Issue-by-issue
    • 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 real-world 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

      • Agent-based 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 black-box 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 lab-experiments 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 agreed-upon 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 Agent-Based 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, Self-Organized 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

      COMPLEX ADAPTIVESYSTEMSAN INTRODUCTION TOCOMPUTATIONAL MODELSOF SOCIAL LIFE

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