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Network adaption based on environment feedback promotes cooperation in co-evolutionary games

Author

Listed:
  • Guo, Yujie
  • Zhang, Liming
  • Li, Haihong
  • Dai, Qionglin
  • Yang, Junzhong

Abstract

In evolutionary games, the choices of individuals form the cooperation environments, and the environments in turn affect the behaviors of individuals. Such environment feedback can be utilized to facilitate the evolution of cooperation. On the other hand, adaptive social structures have been thought to promote cooperation, in which the individuals can switch their interacting neighbors. Here, we propose a co-evolutionary game model, in which individuals can adjust connections based on environment feedback during the evolution. In particular, the individuals determine whether to adjust their connections by comparing the local cooperation environment with the global one. Meanwhile, we use the parameter ω to adjust the time scale between the strategy evolution and the network adaptation in the co-evolutionary dynamics. Our results show that, the network adaption based on the environment feedback can significantly promote cooperation. We find that, relatively fast network adaptation (large ω) can better facilitate the evolution of cooperation. We further investigate how the rationality of individuals in network adaptation process affects the cooperation, and find that a high level of rationality is beneficial to improve cooperation. Through monitoring the local cooperation environments of the individuals, the fractions of the reconnecting individuals and the average degrees of cooperators and defectors, respectively, we provide some intuitive explanations for the promotion of cooperation by the co-evolutionary scheme. Our results may provide some references on the study about how to improve cooperation by adapting the social structures based on environment feedback in the real world.

Suggested Citation

  • Guo, Yujie & Zhang, Liming & Li, Haihong & Dai, Qionglin & Yang, Junzhong, 2023. "Network adaption based on environment feedback promotes cooperation in co-evolutionary games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
  • Handle: RePEc:eee:phsmap:v:617:y:2023:i:c:s0378437123002443
    DOI: 10.1016/j.physa.2023.128689
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    References listed on IDEAS

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    1. Rui Cong & Bin Wu & Yuanying Qiu & Long Wang, 2012. "Evolution of Cooperation Driven by Reputation-Based Migration," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-7, May.
    2. Bin Wu & Da Zhou & Feng Fu & Qingjun Luo & Long Wang & Arne Traulsen, 2010. "Evolution of Cooperation on Stochastic Dynamical Networks," PLOS ONE, Public Library of Science, vol. 5(6), pages 1-7, June.
    3. Hisashi Ohtsuki & Christoph Hauert & Erez Lieberman & Martin A. Nowak, 2006. "A simple rule for the evolution of cooperation on graphs and social networks," Nature, Nature, vol. 441(7092), pages 502-505, May.
    4. Francisco C Santos & Jorge M Pacheco & Tom Lenaerts, 2006. "Cooperation Prevails When Individuals Adjust Their Social Ties," PLOS Computational Biology, Public Library of Science, vol. 2(10), pages 1-8, October.
    5. Takahiro Ezaki & Yutaka Horita & Masanori Takezawa & Naoki Masuda, 2016. "Reinforcement Learning Explains Conditional Cooperation and Its Moody Cousin," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-13, July.
    6. Du, Wen-Bo & Cao, Xian-Bin & Zhao, Lin & Hu, Mao-Bin, 2009. "Evolutionary games on scale-free networks with a preferential selection mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(20), pages 4509-4514.
    7. Zhang, Liming & Li, Haihong & Dai, Qionglin & Yang, Junzhong, 2022. "Migration based on environment comparison promotes cooperation in evolutionary games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    8. F. Fu & L.-H. Liu & L. Wang, 2007. "Evolutionary Prisoner's Dilemma on heterogeneous Newman-Watts small-world network," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 56(4), pages 367-372, April.
    9. A. Szolnoki & M. Perc, 2009. "Promoting cooperation in social dilemmas via simple coevolutionary rules," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 67(3), pages 337-344, February.
    10. Song, Zhao & Guo, Hao & Jia, Danyang & Perc, Matjaž & Li, Xuelong & Wang, Zhen, 2021. "Third party interventions mitigate conflicts on interdependent networks," Applied Mathematics and Computation, Elsevier, vol. 403(C).
    11. Miyaji, Kohei & Tanimoto, Jun, 2021. "A co-evolutionary model combined mixed-strategy and network adaptation by severing disassortative neighbors promotes cooperation in prisoner’s dilemma games," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    12. David G. Rand & Martin A. Nowak, 2011. "The evolution of antisocial punishment in optional public goods games," Nature Communications, Nature, vol. 2(1), pages 1-7, September.
    13. Xiaojie Chen & Attila Szolnoki, 2018. "Punishment and inspection for governing the commons in a feedback-evolving game," PLOS Computational Biology, Public Library of Science, vol. 14(7), pages 1-15, July.
    14. C. Y. Zhang & J. L. Zhang & G. M. Xie & L. Wang, 2011. "Coevolving agent strategies and network topology for the public goods games," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 80(2), pages 217-222, March.
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