Using Discrete PSO Algorithm to Evolve Multi-player Games on Spatial Structure Environment

Mechanisms promoting the evolution of cooperation in two-player, two-strategy evolutionary games have been discussed in great detail over the past decades. Understanding the effects of repeated interactions in multi-player social dilemma game is a formidable challenge. This paper presents and investigates the application of co-evolutionary training techniques based on discrete particle swarm optimization (PSO) to evolve cooperation for the n-player iterated prisoner’s dilemma (IPD) game and n-player iterated snowdrift game (ISD) in spatial environment. Our simulation experiments reveal that, the length of history record, the cost-to-benefit ratio and group size are important factors in determining the cooperation ratio in repeated interactions.

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