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The effect of asymmetric payoff mechanism on evolutionary networked prisoner’s dilemma game

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  • Du, Wen-Bo
  • Cao, Xian-Bin
  • Hu, Mao-Bin

Abstract

In social and biological systems, there are obvious individual divergence and asymmetric payoff phenomenon due to the strength, power and influence differences. In this paper, we introduce an asymmetric payoff mechanism to evolutionary Prisoner’s Dilemma Game (PDG) on scale-free networks. The co-effects of individual diversity and asymmetric payoff mechanism on the evolution of cooperation and the wealth distribution under different updating rules are investigated. Numerical results show that the cooperation is highly promoted when the hub nodes are favored in the payoff matrix, which seems to harm the interest of the majority. But the inequality of social wealth distribution grows with the unbalanced payoff rule. However, when the node difference is eliminated in the learning strategy, the asymmetric payoff rule will not affect the cooperation level. Our work may sharpen the understanding of the cooperative behavior and wealth inequality in the society.

Suggested Citation

  • Du, Wen-Bo & Cao, Xian-Bin & Hu, Mao-Bin, 2009. "The effect of asymmetric payoff mechanism on evolutionary networked prisoner’s dilemma game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(24), pages 5005-5012.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:24:p:5005-5012
    DOI: 10.1016/j.physa.2009.08.026
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    Citations

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    Cited by:

    1. Wu, Yu’e & Zhang, Zhipeng & Chang, Shuhua, 2017. "Effect of self-interaction on the evolution of cooperation in complex topologies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 191-197.
    2. Liu, Chen & Guo, Hao & Li, Zhibin & Gao, Xiaoyuan & Li, Shudong, 2019. "Coevolution of multi-game resolves social dilemma in network population," Applied Mathematics and Computation, Elsevier, vol. 341(C), pages 402-407.
    3. Yu, Fengyuan & Wang, Jianwei & He, Jialu, 2022. "Inequal dependence on members stabilizes cooperation in spatial public goods game," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    4. Feng, Minyu & Han, Songlin & Li, Qin & Wu, Juan & Kurths, Jürgen, 2023. "Harmful strong agents and asymmetric interaction can promote the frequency of cooperation in the snowdrift game," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).
    5. Keizo Shigaki & Zhen Wang & Jun Tanimoto & Eriko Fukuda, 2013. "Effect of Initial Fraction of Cooperators on Cooperative Behavior in Evolutionary Prisoner's Dilemma Game," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-7, November.
    6. Ping Zhu & Guiyi Wei, 2014. "Stochastic Heterogeneous Interaction Promotes Cooperation in Spatial Prisoner's Dilemma Game," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-10, April.
    7. Yang, Xu-Hua & Lou, Shun-Li & Chen, Guang & Chen, Sheng-Yong & Huang, Wei, 2013. "Scale-free networks via attaching to random neighbors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3531-3536.
    8. Huang, Keke & Zheng, Xiaoping & Su, Yunpeng, 2015. "Effect of heterogeneous sub-populations on the evolution of cooperation," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 681-687.
    9. Chen Chu & Jinzhuo Liu & Chen Shen & Jiahua Jin & Lei Shi, 2017. "Win-stay-lose-learn promotes cooperation in the prisoner’s dilemma game with voluntary participation," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-8, February.
    10. Fengjie Xie & Jing Shi & Jun Lin, 2017. "Impact of interaction style and degree on the evolution of cooperation on Barabási–Albert scale-free network," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-15, August.
    11. Griffin, Christopher & Semonsen, Justin & Belmonte, Andrew, 2022. "Generalized Hamiltonian dynamics and chaos in evolutionary games on networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).

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