The repast framework and social simulations
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The RePast Framework and Social Simulations. Presented by Tim Furlong. Overview. RePast Social Simulations Simulations implemented with RePast Santa Fe Artificial Stock Market Endogenizing Geopolitical Boundaries. RePast. REcursive Porous Agent Simulation Toolkit Java class library

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The RePast Framework and Social Simulations

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The repast framework and social simulations

The RePast Framework and Social Simulations

Presented by Tim Furlong



  • RePast

  • Social Simulations

  • Simulations implemented with RePast

    • Santa Fe Artificial Stock Market

    • Endogenizing Geopolitical Boundaries



  • REcursive Porous Agent Simulation Toolkit

  • Java class library

  • University of Chicago

    • Social Science Research Computing

Repast framework

RePast: Framework

  • Base classes to be extended

    • Engine class

    • Agent class

    • Environment class

    • GUI displays, charts, graphs

  • Utility classes

    • Spatial representations

    • Statistical RNGs

The repast framework and social simulations

  • Generic approach

    • Discrete event simulator

  • Easy implementation

    • SugarScape(partial) : ~ 650 LOC

    • Game of Life : ~ 750 LOC

Repast advantages

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.

Repast applications

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 :

Social simulations

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

The santa fe artificial stock market re examined suggested corrections

The Santa Fe Artificial Stock Market Re-Examined: Suggested Corrections

Norman Ehrentreich

Sfi asm introduction

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

Sfi asm original model

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

Sfi asm stock

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

Sfi asm traders

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

Sfi asm expectation rules

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

The repast framework and social simulations

  • Condition is pattern matching rule

    • String of {0,1,#}

    • Bits are technical or fundamental

  • Forecast for rule j: (aj,bj)

The repast framework and social simulations

  • Forecast Accuracy

  • Fitness Value

Sfi asm rule evolution

SFI-ASM: Rule Evolution

  • Genetic algorithm invoked after every K rounds of trading to evolve rules

    • Mutation (p=0.7)

    • Crossover

Sfi asm correction

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

Sfi asm results

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

The repast framework and social simulations

  • Faster learners exploit slower learners

    • Short-term trends

    • In new model, only valid in beginning

Endogenizing geopolitical boundaries with agent based modeling

Endogenizing Geopolitical Boundaries with Agent-based Modeling

Lars-Erik Cederman

Egb introduction

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

Egb background

EGB: Background

  • Essentialist perspective

    • Ignore change of actors

    • Fixed entities with attributes

  • Sociational perspective

    • Dynamic actors and relationships

    • Context-sensitive

Egb endogenization

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)

Egb emergent polarization

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

Egb ep turn structure

EGB: EP turn structure

  • Five phases per turn

    • Resource allocation

    • Decisions

    • Interaction

    • Resource updating

    • Structural change

The repast framework and social simulations

  • Resource allocation

    • Allocate troops to borders based on strength of neighbors

  • Decisions

    • Reciprocate aggressive action

    • Attempt unprovoked attacks

The repast framework and social simulations

  • 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

The repast framework and social simulations

  • 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

Egb democratic peace

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

The repast framework and social simulations

  • Notes

    • Difference in balance of power produces significant results

    • Example of adding ‘categorical social’ processes

      • Threat evaluation is still relational

Egb nationalist systems change

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

The repast framework and social simulations

  • 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’

Egb conclusions

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



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