Co-evolution of strategies for an n-player dilemma

O’Riordan, Colm; Griffith, Josephine; Newell, John A.; Sorensen, Humphrey

doi:10.1109/cec.2004.1331090

Cited by 5 publications

(2 citation statements)

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“…In this paper, we consider the effects of neighbourhood structure on the evolution of cooperative behaviour in the NIPD game. Previous works have studied the impact of the number of players [6], payoff function [7], neighbourhood size [7], history length [8], localisation issue [8], population structure [9], generalisation ability [6,10], forgiveness [11], trust [12], cultural learning [13], noise [14], etc but none has investigated the influence of the neighbourhood structure on the evolution of cooperation among players in the context of NIPD. We simulate the NIPD as a multiagent bidding game using a two dimensional grid-world, where each agent is required to bid either high or low against its neighbours based on a chosen game strategy.…”

Section: Introductionmentioning

confidence: 99%

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

Chiong

Dhakal

Jankovic

2007

Intelligent Data Engineering and Automated Learning - IDEAL 2007

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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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Section: Introductionmentioning

confidence: 99%

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

Chiong

Dhakal

Jankovic

2007

Intelligent Data Engineering and Automated Learning - IDEAL 2007

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“…The effects of genetic drift in evolutionary simulations with finite populations has been studied in [1]. It was shown in [11] that with small populations of strategies playing the N-player iterated prisoner's dilemma, that cooperation can emerge for periods of time due to genetic drift whereas it does not emerge for larger populations.…”

Section: Evolutionary Models Of the N-player Dilemmamentioning

confidence: 99%

Norms and cultural learning in the N-player prisoner’s dilemma

O’Riordan

Griffith

Curran

et al.

2006 IEEE International Conference on Evolutionary Computation

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Social dilemmas are characterised by a choice between actions which are individually rational but collectively sub-optimal and actions which are better for the collective but leave individuals open to exploitation. Evolutionary game theory has been adopted to model the evolution of successive generations of agents playing a social dilemma game. In evolutionary simulations of N-player social dilemmas, cooperation rarely emerges. This paper investigates cultural evolution (via norms that are recorded as artefacts) as a means to increase the fitness of the society by allowing individual strategies to base their actions, not just on their genetic material, but also to take into consideration (by learning) evidence recorded as artefacts. In the first set of experiments, these norms are propagated vertically and we show that allowing cultural learning for a set of strategies in a small population can result in a stable and cooperative society. In the second set of preliminary experiments, agents are organised spatially according to a random graph and norms are spread horizontally.

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Co-evolutionary Learning in the N-player Iterated Prisoner’s Dilemma with a Structured Environment

Chiong

Kirley

2009

Artificial Life: Borrowing From Biology

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Co-evolution of strategies for an n-player dilemma

Cited by 5 publications

References 6 publications

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

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

Norms and cultural learning in the N-player prisoner’s dilemma

Co-evolutionary Learning in the N-player Iterated Prisoner’s Dilemma with a Structured Environment

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