The Impact of Payoff Function and Local Interaction on the N-Player Iterated Prisoner's Dilemma |
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Authors: | Yeon-Gyu Seo Sung-Bae Cho Xin Yao |
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Affiliation: | (1) Department of Computer Science, Yonsei University, Seoul, S. Korea, KR;(2) School of Computer Science, University of Birmingham, Birmingham, UK, GB |
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Abstract: | The N-player iterated prisoner's dilemma (NIPD) game has been widely used to study the evolution of cooperation in social, economic
and biological systems. This paper studies the impact of different payoff functions and local interactions on the NIPD game.
The evolutionary approach is used to evolve game-playing strategies starting from a population of random strategies. The different
payoff functions used in our study describe different behaviors of cooperation and defection among a group of players. Local
interaction introduces neighborhoods into the NIPD game. A player does not play against every other player in a group any
more. He only interacts with his neighbors. We investigate the impact of neighborhood size on the evolution of cooperation
in the NIPD game and the generalization ability of evolved strategies.
Received 18 August 1999 / Revised 27 February 2000 / Accepted 15 May 2000 |
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Keywords: | : Co-evolutionary learning Iterated prisoner's dilemma Generalization Local interaction |
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