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.
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ABSTRACT: This paper explores the evolution of strategies in a n-player dilemma game. These n-player dilemmas provide a formal representation of many real world social dilemmas. Those social dilemmas include littering, voting and sharing com-mon resources such as sharing computer processing time. This paper explores the evolution of altruism using an n-player dilemma. Our results show the importance of so-ciability in these games. For the first time we will use a tag-mediated interaction model to examine the n-player dilemma and demonstrate the significance of sociability in these games.
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ABSTRACT: Mechanisms promoting the evolution of cooperation in two players and two strategies (22) evolutionary games have been investigated in great detail over the past decades. Understanding the effects of repeated interactions in multiplayer spatial games, however, is a formidable challenge. In this paper, we present a multiplayer evolutionary game model in which agents play iterative games in spatial populations. -player versions of the well-known Prisoner's Dilemma and the Snowdrift games are used as the basis of the investigation. These games were chosen as they have emerged as the most promising mathematical metaphors for studying cooperative phenomena. Here, we have adopted an experimental approach to study the emergent behavior, exploring different parameter configurations via numerical simulations. Key model parameters include the cost-to-benefit ratio, the size of groups, the number of repeated encounters, and the interaction topology. Our simulation results reveal that, while the introduction of iterated interactions does promote higher levels of cooperative behavior across a wide range of parameter settings, the cost-to-benefit ratio and group size are important factors in determining the appropriate length of beneficial repeated interactions. In particular, increasing the number of iterated interactions may have a detrimental effect when the cost-to-benefit ratio and group size are small.IEEE Transactions on Evolutionary Computation 08/2012; 16(4):537-555. DOI:10.1109/TEVC.2011.2167682 · 5.55 Impact Factor
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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