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

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.

  • [Show abstract] [Hide abstract]
    ABSTRACT: The problem of evolving and maintaining cooperation in both ecological and artificial multi-agent systems has intrigued scientists for decades. In this paper, we present an evolutionary game model that combines direct and spatial reciprocity to investigate the effectiveness of two different learning mechanisms used to promote cooperative behaviour in a social dilemma game - the N-player Iterated Prisoner's Dilemma (NIPD). Unlike the two-player game, in the NIPD the action of a player typically results in a non Pareto-optimal outcome for all other players within a social group given the relative costs and benefits associated with particular actions. Consequently, promoting system-wide cooperation is extremely difficult. We use comprehensive Monte Carlo simulation experiments to show that evolutionary-based strategy adaptation and update leads to significantly higher levels of cooperation in the NIPD when compared to social learning via cultural imitation. This finding suggests that when designing decentralised multi-agent systems, evolutionary adaptation mechanisms should be incorporated into the model where efficient collective actions are required.
    Evolutionary Computation (CEC), 2010 IEEE Congress on; 08/2010
  • [Show abstract] [Hide abstract]
    ABSTRACT: The evolution of strategies in iterated multi-player social dilemma games is studied on small-world networks. Two different games with varying reward values - the N-player Iterated Prisoner's Dilemma (N-IPD) and the N-player Iterated Snowdrift game (N-ISD) - form the basis of this study. Here, the agents playing the game are mapped to the nodes of different network architectures, ranging from regular lattices to small-world networks and random graphs. In a given game instance, the focal agent participates in an iterative game with N-1 other agents drawn from its local neighbourhood. We use a genetic algorithm with synchronous updating to evolve agent strategies. Extensive Monte Carlo simulation experiments show that for smaller cost-to-benefit ratios, the extent of cooperation in both games decreases as the probability of re-wiring increases. For higher cost-to-benefit ratios, when the re-wiring probability is small we observe an increase in the level of cooperation in the N-IPD population, but not the N-ISD population. This suggests that the small-world network structure with small re-wiring probabilities can both promote and maintain higher levels of cooperation when the game becomes more challenging.
    13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011, Proceedings, Dublin, Ireland, July 12-16, 2011; 01/2011
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: For decades, attempts to understand cooperation between non-kin have generated substantial theoretical and empirical interest in the evolutionary mechanisms of reciprocal altruism. There is growing evidence that the cognitive limitations of animals can hinder direct and indirect reciprocity because the necessary mental capacity is costly. Here, we show that cooperation can evolve by generalized reciprocity (help anyone, if helped by someone) even in large groups, if individuals base their decision to cooperate on a state variable updated by the outcome of the last interaction with an anonymous partner. We demonstrate that this alternative mechanism emerges through small evolutionary steps under a wide range of conditions. Since this state-based generalized reciprocity works without advanced cognitive abilities it may help to understand the evolution of complex social behaviour in a wide range of organisms.
    Proceedings of the Royal Society B: Biological Sciences 03/2011; 278(1707):843-8. · 5.68 Impact Factor