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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 - PowerPoint PPT Presentation
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The RePast Framework and Social Simulations
Presented by Tim Furlong
Overview
RePastSocial SimulationsSimulations implemented with RePast
Santa Fe Artificial Stock MarketEndogenizing Geopolitical Boundaries
RePast
REcursive Porous Agent Simulation ToolkitJava class libraryUniversity of Chicago
Social Science Research Computing
RePast: Framework
Base classes to be extendedEngine classAgent classEnvironment classGUI displays, charts, graphs
Utility classesSpatial representationsStatistical RNGs
Generic approachDiscrete event simulator
Easy implementationSugarScape(partial) : ~ 650 LOCGame of Life : ~ 750 LOC
RePast: Advantages
Facilitates implementationConvenient representation of heterogeneous agentsSupport for geometric world modelsGarbage collection
‘Powerful’ visualization techniquesLars-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
School voucher programsConsumer choiceDecision making in closed regimesModeling the size of warsVoting dynamicsSelf-organizing computer networksMulti-cellular tumors
Repast Homepage – Projects and Publications : http://repast.sourceforge.net/projects.html
Social Simulations
Goal is to simulate observed behaviors with hypothesized modelSeveral ‘flavors’ of simulation
Statistical : global variablesAgent-based : allows heterogeneous agents with varied and dynamic behavior
The Santa Fe Artificial Stock Market Re-Examined: Suggested Corrections
Norman Ehrentreich
SFI-ASM: Introduction
Simplistic stock market simulationIsolates learning speed of traders as critical parameterBased on original SFI-ASMFixes faulty mutation operator
Results not quite as compelling
Interesting RePast model
SFI-ASM: Original Model
N traders1 unit risky stock, 20 000 units cash
Each trader seeks to buy or sell stock based on expectations of profitProfit
Fixed return of rf on cash assets
Stock pays stochastic dividend
SFI-ASM: Stock
Only one ‘stock’ in marketStock has price pt and dividend dt
Dividend of stock at time t +1
Mean-reverting factor of (1 – ρ), but generally stochastic
),0(~
)(2
11
N
dddd ttt
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
tittiftttiti xpWrdpxW ,,11,1, 1
2,
11,,
1ˆ
dpt
fttttiti
rpdpEx
SFI-ASM: Expectation rules
Market has descriptor Dt
Bitstring of market conditions
Each trader has own set of 100 rulesRule comprised of:
ConditionForecastForecast accuracyFitness value
Condition is pattern matching ruleString of {0,1,#}Bits are technical or fundamental
Forecast for rule j: (aj,bj)
jittjittit bdpadpE ,,11,
Forecast Accuracy
Fitness Value
2,11,12
,,112
,, 1 jittjittjitjit bdpadpvv
yspecificitbitCostvCf jtjt 2,,
SFI-ASM: Rule Evolution
Genetic algorithm invoked after every K rounds of trading to evolve rules
Mutation (p=0.7)Crossover
SFI-ASM: Correction
Original had faulty mutation operatorBiased 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
Rules converge to all-# bitsReach 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
Faster learners exploit slower learners
Short-term trendsIn new model, only valid in beginning
Endogenizing Geopolitical Boundaries with Agent-based Modeling
Lars-Erik Cederman
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
Essentialist perspectiveIgnore change of actorsFixed entities with attributes
Sociational perspectiveDynamic actors and relationshipsContext-sensitive
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
Models conquest and expansion of statesVillages or counties on a finite 2d gridStates emerge as villages conquer neighbors
State has capital based on original villageResources gathered from the territories depends on distance to capital
EGB: EP turn structure
Five phases per turnResource allocationDecisionsInteractionResource updatingStructural change
Resource allocationAllocate troops to borders based on strength of neighbors
DecisionsReciprocate aggressive actionAttempt unprovoked attacks
InteractionResolve conflicts based on balance of power
Resource updatingStates gain resources from provinces
Structural changeStructure of defeated state altered by outcome of conflicts
NotesStates can spread too thin, inviting attack from other neighbors and opening multiple fronts to conflictCan extend the model to allow alliances between states
EGB: Democratic Peace
Adds categorical relationships to previous modelObserved that democracies do not fight each otherAdd ‘democracy’ label to some statesDemocracies do not fight each other, and form a defensive coalition
NotesDifference in balance of power produces significant resultsExample of adding ‘categorical social’ processes• Threat evaluation is still relational
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 valuesNation is a pattern string of traits with wildcards
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
Agent-based simulations are better at modeling complex phenomenae than conventional approachesTreating actors as themselves emergent and internally dynamic is necessary to good simulation over long time scales
Questions?