Conference Paper

Effects of Neighbourhood Structure on Evolution of Cooperation in N-Player Iterated Prisoner's Dilemma.

DOI: 10.1007/978-3-540-77226-2_95 Conference: Intelligent Data Engineering and Automated Learning - IDEAL 2007, 8th International Conference, Birmingham, UK, December 16-19, 2007, Proceedings
Source: DBLP


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.

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    • "In this paper, we study the co-evolution of agent strategies in a spatial environment by modelling the strategic interactions between agents using the N-player Iterated Prisoner's Dilemma (NIPD) game based on the formalism of Boyd and Richerson [2]. Previous studies [3] [4] have revealed that spatial structure is beneficial for cooperation, which is consistent with findings in the PD game. Here, we examine the influence of alternative strategy update mechanisms in detail. "
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    ABSTRACT: Understanding how cooperative behaviour emerges within a population of individuals has been the focus of a great deal of research in the multi-agent systems community. In this paper, we examine the effectiveness of two different learning mechanisms -- an evolutionary-based technique and a social imitation technique -- in promoting and maintaining cooperation in the spatial N-player Iterated Prisoner's Dilemma (NIPD) game. Comprehensive Monte Carlo simulation experiments show that both mechanisms are able to evolve high levels of cooperation in the NIPD despite the diminished impact of direct reciprocation. However, the performance of evolutionary learning is significantly better than social learning, especially for larger population sizes. Our conclusion implies that when designing autonomous agents situated in complex environments, the use of evolutionary-based adaptation mechanisms will help realising efficient collective actions.
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    • "Yao and Darwin demonstrated the effects of limiting group size, which was shown to benefit cooperation. Increasingly complex aspects of agent interactions have been examined by a number of authors, these include the effects of community structure on the evolution of cooperation [12] [1]. These have shown that neighbourhood structures benefit cooperation. "
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    ABSTRACT: Co-evolutionary learning is a process where a set of agents mutually adapt via strategic interactions. In this paper, we consider the ability of co-evolutionary learning to evolve cooperative strategies in structured populations using the N-player Iterated Prisoner's Dilemma (NIPD). To do so, we examine the effects of both fixed and random neighbourhood structures on the evolution of cooperative behaviour in a lattice-based NIPD model. Our main focus is to gain a deeper understanding on how co-evolutionary learning could work well in a spatially structured environment. The numerical experiments demonstrate that, while some recent studies have shown that neighbourhood structures encourage cooperation to emerge, the topological arrangement of the neighbourhood structures is an important factor that determines the level of cooperation.
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