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The influence of own historical information and environmental historical information on the evolution of cooperation

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  • Ji, Jiezhou
  • Pan, Qiuhui
  • Zhu, Wenqiang
  • He, Mingfeng

Abstract

This article has studied the influence of own historical information and environmental historical information on the evolution of cooperation in the three common social dilemma games. The results show that only the past two-step information can effectively promote cooperation. In the prisoner's dilemma game, cooperation can be improved by using only the individual own information. In the stag hunt game, using only environmental information can promote cooperation. In the snowdrift game, when the parameter values are different, the results are different. The cooperation ratio can be maximized by using either its own information or environmental information only. This article provides a new way to promote and understand cooperation.

Suggested Citation

  • Ji, Jiezhou & Pan, Qiuhui & Zhu, Wenqiang & He, Mingfeng, 2023. "The influence of own historical information and environmental historical information on the evolution of cooperation," Applied Mathematics and Computation, Elsevier, vol. 446(C).
    • Handle: RePEc:eee:apmaco:v:446:y:2023:i:c:s0096300323000711
    DOI: 10.1016/j.amc.2023.127902
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    References listed on IDEAS

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    1. A. Szolnoki & M. Perc & G. Szabó, 2008. "Diversity of reproduction rate supports cooperation in the prisoner's dilemma game on complex networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 61(4), pages 505-509, February.
    2. Fu, Mingjian & Wang, Jingbin & Cheng, Linlin & Chen, Lijuan, 2021. "Promotion of cooperation with loyalty-based reward in the spatial prisoner’s dilemma game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    3. Shi, Zhenyu & Wei, Wei & Perc, Matjaž & Li, Baifeng & Zheng, Zhiming, 2022. "Coupling group selection and network reciprocity in social dilemmas through multilayer networks," Applied Mathematics and Computation, Elsevier, vol. 418(C).
    4. Ramón Alonso-Sanz & Margarita Martín, 2006. "Memory Boosts Cooperation," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 841-852.
    5. Neil Johnson & Thomas Lux, 2011. "Ecology and economics," Nature, Nature, vol. 469(7330), pages 302-303, January.
    6. Ye, Wenxing & Feng, Weiying & Lü, Chen & Fan, Suohai, 2017. "Memory-based prisoner’s dilemma game with conditional selection on networks," Applied Mathematics and Computation, Elsevier, vol. 307(C), pages 31-37.
    7. Marco Alberto Javarone, 2016. "Statistical physics of the spatial Prisoner’s Dilemma with memory-aware agents," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(2), pages 1-6, February.
    8. Marco Alberto Javarone, 2016. "Statistical physics of the spatial Prisoner’s Dilemma with memory-aware agents," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(2), pages 1-6, February.
    9. Zhu, Wenqiang & Pan, Qiuhui & He, Mingfeng, 2022. "Exposure-based reputation mechanism promotes the evolution of cooperation," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    10. Oliver P. Hauser & David G. Rand & Alexander Peysakhovich & Martin A. Nowak, 2014. "Cooperating with the future," Nature, Nature, vol. 511(7508), pages 220-223, July.
    11. Pu, Jia & Jia, Tao & Li, Ya, 2019. "Effects of time cost on the evolution of cooperation in snowdrift game," Chaos, Solitons & Fractals, Elsevier, vol. 125(C), pages 146-151.
    12. Li, Dandan & Sun, Xiaoxiao & He, Youxin & Han, Dun, 2022. "On prisoner’s dilemma game with psychological bias and memory learning," Applied Mathematics and Computation, Elsevier, vol. 433(C).
    13. Pan, Qiuhui & Wang, Yue & He, Mingfeng, 2022. "Impacts of special cooperation strategy with reward and punishment mechanism on cooperation evolution," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    14. Deng, Xiao-Heng & Liu, Yi & Chen, Zhi-Gang, 2010. "Memory-based evolutionary game on small-world network with tunable heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(22), pages 5173-5181.
    15. Dong, Yukun & Xu, Hedong & Fan, Suohai, 2019. "Memory-based stag hunt game on regular lattices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 247-255.
    16. Luo, Chao & Zhang, Xiaolin & Liu, Hong & Shao, Rui, 2016. "Cooperation in memory-based prisoner’s dilemma game on interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 560-569.
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