Introduction to RePast and Tutorial I

Today’s agenda Introduction to Repast IPD Model Tutorial sequence

What is RePast? Recursive Porous Agent Simulation Toolkit http://repast.sourceforge.net Repast is an open-source software framework for creating agent-based simulations using Java Initially developed by the Social Science Research Computing at the University of Chicago Will be further developed by the RePast Organization for Architecture and Development (ROAD) and Argonne National Laboratory

Why RePast? Alternatives: Swarm, Ascape, NetLogo... “RePast is at the moment the most suitable simulation framework for the applied modeling of social interventions based on theories and data” (2004): http://jasss.soc.surrey.ac.uk/7/1/6.html Modeled on Swarm but easier to use and better documented Important criteria: abstraction, ease of use and user-friendliness flexibility and extensibility performance and scalability support for modeling, simulation & experimentation Interoperability (GIS, statistical packages, …)

What does RePast offer? Skeletons of agents and their environment Graphical user interface Scheduling of simulations Parameters management Behavior display Charting Data collection Batch and parallel runs Utilities and sample models

Iterated Prisoner’s Dilemma Cohen, Riolo, and Axelrod. 1999. “The Emergence of Social Organization in the Prisoner's Dilemma” (SFI Working Paper 99-01-002) http://www.santafe.edu/sfi/publications/99wplist.html In The Evolution of Cooperation, Robert Axelrod (1984) created a computer tournament of IPD cooperation sometimes emerges Tit For Tat a particularly effective strategy

Prisoner’s Dilemma Game Column: C D C 3,3 0,5 Row: D 5,0 1,1

One-Step Memory Strategies Strategy = (i, p, q) i = prob. of cooperating at t = 0 p = prob. of cooperating if opponent cooperated q = prob. of cooperating if opponent defected C p Memory: C D q C D D t-1 t

The Four Strategies

TFT meets ALLD Cumulated Payoff Row (TFT) Column (ALLD) 1 2 3 4 t p=1; q=0 + 1 + 1 + 1 = 3 Row (TFT) i=1 C D D D D Column (ALLD) C D i=0 5 + 1 + 1 + 1 = 8 p=0; q=0 1 2 3 4 t

Payoffs for 4 x 4 Strategies

Three crucial questions: 1. Variation: What are the actors’ characteristics? 2. Interaction: Who interacts with whom, when and where? 3. Selection: Which agents or strategies are retained, and which are destroyed? (see Axelrod and Cohen. 1999. Harnessing Complexity)

Experimental dimensions 2 strategy spaces: B, C 6 interaction processes: RWR, 2DK, FRN, FRNE, 2DS, Tag 3 adaptive processes: Imit, BMGA, 1FGA

“Soup-like” topology: RWR In each time period, a player interacts with four other random players. ATFT ALLC ALLD ALLD TFT TFT ALLC

2D-Grid Topology: 2DK The players are arranged on a fixed TFT ALLD ALLC ATFT The players are arranged on a fixed torus and interact with four neighbors in the von-Neumann neighborhood.

Fixed Random Network: FRN The players have four random neighbors in a fixed random network. The relations do not have to be symmetric. ALLC TFT ALLD ATFT

Adaptation through imitation ATFT ALLC ALLD ALLD TFT TFT? ALLC Neighbors at t

Adaptation with BMGA Comparison error (prob. 0.1) Genetic adaptation 6.0 Fixed spatial neighborhood 2.8 2.2 9.0 0.8

BMGA continued Copy error (prob. 0.04 per “bit”) Genetic adaptation 6.0 Fixed spatial neighborhood p=0; q=0 => p=1; q=0 2.8 6.0 9.0 0.8

Tutorial Sequence Today SimpleIPD: strategy space May 11 EvolIPD: RWR May 18 GraphIPD: charts and GUI May 25 GridIPD: 2DK June 1st ExperIPD: batch runs and parameter sweeps