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Rigorous Punishment Promotes Cooperation in Prisoners’ Dilemma Game

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Abstract

In this paper, we introduce a rigorous punishment mechanism into the prisoners’ dilemma game. In our model, the punisher punishes the defector with fine \(\beta \) at the cost of \(\gamma \). Monte-Carlo simulations show the evolution of system is jointly affected by \(\beta \), \(\gamma \) and system’s initial state. We find that when \(\gamma \) is small, the system can evolve into two steady states, i.e., coexisting of cooperators and defectors, and pure punishers. When \(\gamma \) is large, the system can evolve into the only steady state, i.e., coexisting of cooperators and defectors. However, in the middle value of \(\gamma \), the system can evolve into three steady states, i.e., coexisting of cooperators and defectors, a rock-paper-scissors type of cyclic dominance, and pure cooperators. These results are explained by average total payoff, transition possibility and evolutionary snapshot. We also find the heterogeneity of population distribution can affect cooperation as well.

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Acknowledgements

This work was supported in part by the National Basic Research Program of China (Grant No. 2012CB315804), the Natural Science Foundation of Zhejiang Province of China under Grants No. Y1110766, the Key Project of Chinese Ministry of Education under Grant No. 210085, the National Development and Reform Commission, China under Special Grants “The Operation System of Multimedia Cloud Based on the Integration of Telecommunications Networks, Cable TV Networks and the Internet”, and the Science and Technology Planning Projects of Zhejiang Province, China under Grants No. 2010C13005 and 2011C13006-1. We also thank Runran Liu and Wen-Bo Du for the useful discussions.

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© 2014 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Ling, Y., Liu, J., Zhu, P., Wei, G. (2014). Rigorous Punishment Promotes Cooperation in Prisoners’ Dilemma Game. In: Di Caro, G., Theraulaz, G. (eds) Bio-Inspired Models of Network, Information, and Computing Systems. BIONETICS 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 134. Springer, Cham. https://doi.org/10.1007/978-3-319-06944-9_22

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  • DOI: https://doi.org/10.1007/978-3-319-06944-9_22

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  • Online ISBN: 978-3-319-06944-9

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