Effects of Neighbourhood Structure on Evolution of Cooperation in N-Player Iterated Prisoner's Dilemma.
ABSTRACT In multi-agent systems, complex and dynamic interactions often emerge among individual agents. The ability of each agent to
learn adaptively is therefore important for them to survive in such changing environment. In this paper, we consider the effects
of neighbourhood structure on the evolution of cooperative behaviour in the N-Player Iterated Prisoner’s Dilemma (NIPD). We
simulate the NIPD as a bidding game on a two dimensional grid-world, where each agent has to bid against its neighbours based
on a chosen game strategy. We conduct experiments with three different types of neighbourhood structures, namely the triangular
neighbourhood structure, the rectangular neighbourhood structure and the random pairing structure. Our results show that cooperation
does emerge under the triangular neighbourhood structure, but defection prevails under the rectangular neighbourhood structure
as well as the random pairing structure.
Conference Proceeding: Co-evolution of strategies for an n-player dilemma[show abstract] [hide abstract]
ABSTRACT: This work presents results on co-evolving classes of strategies for the n-player iterated prisoner's dilemma (NIPD). We incorporate the notion of forgiveness in strategies and present experimental results which show that higher levels of cooperation and fitness are attainable when strategies are forgiving.Evolutionary Computation, 2004. CEC2004. Congress on; 07/2004
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ABSTRACT: The N-player iterated prisoner's dilemma (NIPD) game has been used widely to study the evolution of cooperation in social, economic and biological systems. Previous work on the NIPD game studied the impact of the number of players and the payoff function on the evolution of cooperation. This paper studies the localization issue in the NIPD game and investigates the impact of local interaction on genetically evolved strategies for the NIPD game. Our experimental results show that localization of interaction has a major impact on the evolution of cooperative coalitions, while localized learning makes the population oscillate. This paper also investigates the effect of the history length in the NIPD game. It is found experimentally that a longer history makes a population more stable, but it takes longer time to reach this stable stateEvolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on; 02/1999
Article: The evolution of cooperationBioScience 01/1997; 47(6):355-362. · 4.74 Impact Factor