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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. OVERVIEW. Free-riding and Mobility? Simulation, Agent-Based EVOLUTION of Cooperation MATCHING among Strategies. QUESTION.
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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
OVERVIEW • Free-riding and Mobility? • Simulation, Agent-Based • EVOLUTION of Cooperation • MATCHING among Strategies
QUESTION • Modern Societies... MOBILITY • Turnover, Divorce, Moving, Immigration • Globalization, Internet • Effects of MOBILITY on FREE-RIDER PROBLEM? • EVOLUTION of Cooperation???
FREE-RIDER PROBLEM • When Goods have Externality • Promise, Donation • Teamwork, Social Movement • RATIONAL to FREE-RIDE • but EFFICIENT to COOPERATE
OUT-FOR-TAT (Hayashi) • Simulation, 2-person game • “EXITING” is Effective as Revenge if Mobility Cost is HIGH • MULTIPLE-PERSON Games? Free-rider Cooperator
Introduction Method Result
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
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
A B ... J Free-rider Problem Exit Option
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
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)
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 (H0: Even, p<.05, Two-sided Test)
FOCUS • Defectors Reject Cooperators • Many Cooperators+Few Defectors • Initial Distributions -> WINS??? Fixed Defector Moving TFT Fixed TFT Moving Defector
Introduction Method Result
0 and 5 FIXED DEFECTORS Fixed Defector MT FT Moving Defector WIN LOSS
10, 15, 20 FIXED DEFECTORS FT 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 0 30 30 28 30 29 30 28 29 30 28 29 25 28 22 27 26 18 10 1 22 21 16 11 16 16 11 8 14 11 6 11 12 11 12 8 8 2 14 9 10 6 13 11 6 7 4 3 6 5 3 7 3 4 5 MD 3 7 7 8 6 6 3 5 4 4 1 2 3 5 1 3 2 4 4 2 4 4 1 4 1 2 2 1 0 2 3 2 4 2 5 2 5 4 0 1 4 2 1 1 1 0 3 0 7 3 1 1 3 0 MD 0 24 25 24 21 24 27 23 20 24 26 25 19 15 15 10 10 4 Fixed Defector MT FT Moving Defector
1. EVOLUTION of COOPERAITON • Cooperators can REJECT (Blue) • If FEW Defectors • Even with MOBILITY • Up to about 15 Defectors • b/c 10 Groups
2. MATCHING • Cooperators Refuse TOGETHER Fixed Defector Moving TFT Fixed TFT Moving Defector
SUMMARY • 1. Evolution of Cooperation Even with Mobility • 2. MATCHING matters • More STRATEGIES? • More MOBILITY COSTS? • Compare with Experiment, Survey