1. Introduction to RePast and Tutorial I

2. Today’s agenda
Introduction to Repast
IPD Model
Tutorial sequence

3. What is RePast?
Recursive Porous Agent Simulation Toolkit
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

4. 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):
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, …)

5. 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

6. Iterated Prisoner’s Dilemma
Cohen, Riolo, and Axelrod “The Emergence of Social Organization in the Prisoner's Dilemma” (SFI Working Paper )
In The Evolution of Cooperation, Robert Axelrod (1984) created a computer tournament of IPD
cooperation sometimes emerges
Tit For Tat a particularly effective strategy

7. Prisoner’s Dilemma Game
Column:
C D
C , ,5
Row:
D , ,1

8. 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

9. The Four Strategies

10. 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

11. Payoffs for 4 x 4 Strategies

12. 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 Harnessing Complexity)

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

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

15. 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.

16. 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

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

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

19. 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

20. 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