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Exploring Social Simulations with RePast Framework

Discover the powerful RePast framework for social simulations, including its key features, advantages, and applications in various fields. Learn about agent-based modeling, endogenizing boundaries, and the Santa Fe Artificial Stock Market. Dive into the world of simulation science with RePast!

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Exploring Social Simulations with RePast Framework

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  1. The RePast Framework and Social Simulations Presented by Tim Furlong

  2. Overview • RePast • Social Simulations • Simulations implemented with RePast • Santa Fe Artificial Stock Market • Endogenizing Geopolitical Boundaries

  3. RePast • REcursive Porous Agent Simulation Toolkit • Java class library • University of Chicago • Social Science Research Computing

  4. RePast: Framework • Base classes to be extended • Engine class • Agent class • Environment class • GUI displays, charts, graphs • Utility classes • Spatial representations • Statistical RNGs

  5. Generic approach • Discrete event simulator • Easy implementation • SugarScape(partial) : ~ 650 LOC • Game of Life : ~ 750 LOC

  6. RePast: Advantages • Facilitates implementation • Convenient representation of heterogeneous agents • Support for geometric world models • Garbage collection • ‘Powerful’ visualization techniques Lars-Erik Cederman, “Endogenizing Geopolitical Boundaries with Agent-based Modeling”, prepared for Sackler Colloquium on “Adaptive Agents, Intelligence, and Emergent Human Organization: Capturing Complexity through Agent-based Modelling”, Oct. 2001.

  7. RePast: Applications • School voucher programs • Consumer choice • Decision making in closed regimes • Modeling the size of wars • Voting dynamics • Self-organizing computer networks • Multi-cellular tumors Repast Homepage – Projects and Publications : http://repast.sourceforge.net/projects.html

  8. Social Simulations • Goal is to simulate observed behaviors with hypothesized model • Several ‘flavors’ of simulation • Statistical : global variables • Agent-based : allows heterogeneous agents with varied and dynamic behavior

  9. The Santa Fe Artificial Stock Market Re-Examined: Suggested Corrections Norman Ehrentreich

  10. SFI-ASM: Introduction • Simplistic stock market simulation • Isolates learning speed of traders as critical parameter • Based on original SFI-ASM • Fixes faulty mutation operator • Results not quite as compelling • Interesting RePast model

  11. SFI-ASM: Original Model • N traders • 1 unit risky stock, 20 000 units cash • Each trader seeks to buy or sell stock based on expectations of profit • Profit • Fixed return of rf on cash assets • Stock pays stochastic dividend

  12. SFI-ASM: Stock • Only one ‘stock’ in market • Stock has price pt and dividend dt • Dividend of stock at time t +1 • Mean-reverting factor of (1 – ρ), but generally stochastic

  13. SFI-ASM: Traders • Risk aversion factor of λi • Wealth at time t of Wi,t: stock + cash • Optimal amount of stock based on expectations of profit

  14. SFI-ASM: Expectation rules • Market has descriptor Dt • Bitstring of market conditions • Each trader has own set of 100 rules • Rule comprised of: • Condition • Forecast • Forecast accuracy • Fitness value

  15. Condition is pattern matching rule • String of {0,1,#} • Bits are technical or fundamental • Forecast for rule j: (aj,bj)

  16. Forecast Accuracy • Fitness Value

  17. SFI-ASM: Rule Evolution • Genetic algorithm invoked after every K rounds of trading to evolve rules • Mutation (p=0.7) • Crossover

  18. SFI-ASM: Correction • Original had faulty mutation operator • Biased results to higher number of non-# bits • Correct solution for rules is to converge to all-# bits • Dividend and price too random to classify • With new operator, rules always converge

  19. SFI-ASM: Results • Rules converge to all-# bits • Reach homogeneous rational expectation equilibrium eventually • With values for K < 100, complex trading emerges • Harder to persuade the model to do this with the new mutation operator

  20. Faster learners exploit slower learners • Short-term trends • In new model, only valid in beginning

  21. Endogenizing Geopolitical Boundaries with Agent-based Modeling Lars-Erik Cederman

  22. EGB: Introduction • Agent-based modeling has potential to avoid reification of actors • Reification: treating an abstract concept as concrete • Long-term simulations require “sociational endogenization” of actors • Actors must be internally dynamic

  23. EGB: Background • Essentialist perspective • Ignore change of actors • Fixed entities with attributes • Sociational perspective • Dynamic actors and relationships • Context-sensitive

  24. EGB: Endogenization • Presents series of models to illustrate progression from reified actors to endogenous ones • Modeling emergence of state borders • Emergent Polarization (EP) • Democratic Peace (DP) • Nationalist Systems Change (NSC)

  25. EGB: Emergent Polarization • Models conquest and expansion of states • Villages or counties on a finite 2d grid • States emerge as villages conquer neighbors • State has capital based on original village • Resources gathered from the territories depends on distance to capital

  26. EGB: EP turn structure • Five phases per turn • Resource allocation • Decisions • Interaction • Resource updating • Structural change

  27. Resource allocation • Allocate troops to borders based on strength of neighbors • Decisions • Reciprocate aggressive action • Attempt unprovoked attacks

  28. Interaction • Resolve conflicts based on balance of power • Resource updating • States gain resources from provinces • Structural change • Structure of defeated state altered by outcome of conflicts

  29. Notes • States can spread too thin, inviting attack from other neighbors and opening multiple fronts to conflict • Can extend the model to allow alliances between states

  30. EGB: Democratic Peace • Adds categorical relationships to previous model • Observed that democracies do not fight each other • Add ‘democracy’ label to some states • Democracies do not fight each other, and form a defensive coalition

  31. Notes • Difference in balance of power produces significant results • Example of adding ‘categorical social’ processes • Threat evaluation is still relational

  32. EGB: Nationalist Systems Change • Introduce concept of actors separate from states : nations • Nations and states sometimes coincide, but not always • Each village has ‘cultural’ identity : string of trait values • Nation is a pattern string of traits with wildcards

  33. Nations founded and joined by agents • Capitals more likely to found nations due to resources • National identities have major impact on inter-state relations • ‘irredentist’ invasions to conquer conationals not under ‘home rule’

  34. EGB: Conclusions • Agent-based simulations are better at modeling complex phenomenae than conventional approaches • Treating actors as themselves emergent and internally dynamic is necessary to good simulation over long time scales

  35. Questions?

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