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Exit for Cooperation --A Simulation Study-- Yuhsuke Koyama (Tokyo Tech) Hirokuni Ooura (Teikyo) Jun Kobayashi (Chicago) August 15, 2004 ASA, San Francisco.

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Presentation on theme: "Exit for Cooperation --A Simulation Study-- Yuhsuke Koyama (Tokyo Tech) Hirokuni Ooura (Teikyo) Jun Kobayashi (Chicago) August 15, 2004 ASA, San Francisco."— Presentation transcript:

1 Exit for Cooperation --A Simulation Study-- Yuhsuke Koyama (Tokyo Tech) Hirokuni Ooura (Teikyo) Jun Kobayashi (Chicago) August 15, 2004 ASA, San Francisco

2 OVERVIEW Free-riding and Mobility? Simulation, Agent-Based EVOLUTION of Cooperation MATCHING among Strategies

3 3 Modern Societies... MOBILITY Turnover, Divorce, Moving, Immigration Globalization, Internet Effects of MOBILITY on FREE-RIDER PROBLEM? EVOLUTION of Cooperation??? QUESTION

4 4 When Goods have Externality Promise, Donation Teamwork, Social Movement RATIONAL to FREE-RIDE but EFFICIENT to COOPERATE FREE-RIDER PROBLEM

5 OUT-FOR-TAT (Hayashi) Simulation, 2-person game “EXITING” is Effective as Revenge if Mobility Cost is HIGH MULTIPLE-PERSON Games? Free-rider Cooperator

6 Introduction Method Result

7 SIMULATION AGENT-BASED, JAVA SHARE Change of 4 Strategies  MOVING TFT (MT)  FIXED TFT (FT)  MOVING ALL D (MD)  FIXED ALL D (FD) (Move to Most Profitable, Largest) Cooperators can REJECT??? Cooperator

8 GAME 100 Agents Each with 1 of 4 Strategies Randomly Assigned to 10 Groups Free-rider Problem x 5 rounds -> Exit Option Free-rider Problem x 5 rounds -> Exit Option (repeated till 20 Exit Options) 100

9 A B... J Exit Option Free-rider Problem

10 FREE-RIDER PROBLEM Resource $4 PROVIDE or NOT Pooled Resources DOUBLED EQUALLY Distributed in Group u (Provide) = 8 u (Not) = 8 + 4 # Providers Group Size # Providers -1 Group Size

11 EVOLUTION SHARE CHANGE after Game Proportional to PAYOFF x SHARE Mobility Cost = $1 Repeat 100 Games 3 Possible OUTCOMES  ALL Cooperators  ALL Defectors  Draw (Otherwise)

12 WINS and LOSSES # Strategies = Multiples of 5 All Initial Distributions = 1,771 30 Iterations for Each Distribution “WIN” if Cooperators Dominate 21 “LOSS” if 10 Iterations (H 0 : Even, p<.05, Two-sided Test)

13 FOCUS Defectors Reject Cooperators Many Cooperators+Few Defectors Initial Distributions -> WINS??? Moving TFT Moving Defector Fixed TFT Fixed Defector

14 Introduction Method Result

15 0 and 5 FIXED DEFECTORS MT FT LOSS WIN Moving Defector Fixed Defector

16 10, 15, 20 FIXED DEFECTORS MT FT MD024252421242723202426251915 10 4 Moving Defector Fixed Defector

17 1. EVOLUTION of COOPERAITON Cooperators can REJECT (Blue) If FEW Defectors Even with MOBILITY Up to about 15 Defectors b/c 10 Groups

18 2. MATCHING Cooperators Refuse TOGETHER Moving TFT Moving Defector Fixed TFT Fixed Defector

19 SUMMARY 1. Evolution of Cooperation Even with Mobility 2. MATCHING matters More STRATEGIES? More MOBILITY COSTS? Compare with Experiment, Survey


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