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Evolving cooperation in spatial population with punishment by using PSO algorithm

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Abstract

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.

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Acknowledgments

This work is supported by three research program of China. One is the Guangdong province philosophy social sciences program Under Grant No. GD15XGL03, the Zhongshan Science and Technology Development Funds under Grant No. 2014A2FC385, and also supported by Dr Startup project Under Grant No. 414YKQ04.

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Authors and Affiliations

  1. University of Electronic Science and Technology of China, Zhongshan Insitute, Zhongshan, Guangdong, China

    Xiaoyang Wang

  2. School of Physics and Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China

    Lei Zhang, Xiaorong Du & Yunlin Sun

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  1. Xiaoyang Wang
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  2. Lei Zhang
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  3. Xiaorong Du
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  4. Yunlin Sun
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Correspondence to Xiaoyang Wang.

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Wang, X., Zhang, L., Du, X. et al. Evolving cooperation in spatial population with punishment by using PSO algorithm. Nat Comput 16, 99–117 (2017). https://doi.org/10.1007/s11047-016-9546-5

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