Complex Adaptive Systems
Introduction to Computational Models of Social Life
John H. Miller; Scott E. Page
reviewed by Talha Oz
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
- 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
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
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)
- Party based
- Winning party takes all
- Blend by weighted votes
Models as maps
Snow's cholera map
Computation as theory
- 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.
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
- 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.
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
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
- How agents interact, when, with whom
- What information an agent has access to
- Rationally bounded. Learning Algorithms
- In natural systems, equilibria = death
Heterogeneous Agents and Asymmetry
- Heterogeneity and asymmetry accommodated easily
- Mathematical models for a few (duopolies) or many (perfect competition) agents
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
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