Evolving cooperation in spatial population with punishment by using PSO algorithm
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- 作者：Xiaoyang Wang ; Lei Zhang ; Xiaorong Du ; Yunlin Sun
- 关键词：PSO ; IPD (iterated prisoner’s dilemma) ; ISD (iterated snowdrift dilemma) ; Punishment ; Spatial structure
- 刊名：Natural Computing
- 出版年：2017
- 出版时间：March 2017
- 年：2017
- 卷：16
- 期：1
- 页码：99-117
- 全文大小：
- 刊物类别：Computer Science
- 刊物主题：Theory of Computation; Evolutionary Biology; Processor Architectures; Artificial Intelligence (incl. Robotics); Complex Systems;
- 出版者：Springer Netherlands
- ISSN：1572-9796
- 卷排序：16
文摘
Understanding the effects of punishment 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 with punishment. Two kinds of spatial structure are used, regular and random connected network. Key model parameters include the number of players, the interaction topology, the punishment and the cost-to-benefit ratio. The simulation results reveal that the punishment can promote the levels of cooperative behaviors to some extent, the cost-to-benefit ratio and the number of players is important factors in determining the strategy evolution. While punishment improves cooperation in these new environments, the impact of punishment is different in two social dilemma games. The spatial structures add the complex of the evolution and the random connected network can better isolate the defectors.