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A proportional-neighborhood-diversity evolution in snowdrift game on square lattice

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  • Hu, Xiang
  • Liu, Xingwen
  • Zhou, Xiaobing

Abstract

The subject of how to facilitate cooperation of evolution through different mechanisms has been intensively investigated. For snowdrift game, researches have shown that a moderate dilution of the number of neighbors of individuals will benefit the evolution of cooperation, and have not shown how different dilution degrees effect evolution of cooperation. This paper proposes a proportional-neighborhood-diversity (PND) mechanism which takes into account some relatively typical situations. The core lies in: We provide a proportional vector to indicate the proportion of individuals with different number of interactive neighbors. Each player has fixed spatial neighbors and only plays with interactive neighbors selected from its spatial neighbors. This study is performed by means of the Monte Carlo method and an extended pair-approximation method. The Monte Carlo simulation results show that, compared with the traditional case, introducing different dilution degrees in evolution promotes the emergence of cooperative behavior. An interesting phenomenon is that when most individuals have a smaller number of interactive neighbors, the cooperation level is relatively higher. When applied to the evolutionary game, the PND mechanism can reduce game cost since the number of interactive neighbors is less than that of fixed neighbors in whole evolutionary process. Moreover, it has potential role in social management since the proposed mechanism is more realistic.

Suggested Citation

  • Hu, Xiang & Liu, Xingwen & Zhou, Xiaobing, 2022. "A proportional-neighborhood-diversity evolution in snowdrift game on square lattice," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    • Handle: RePEc:eee:phsmap:v:607:y:2022:i:c:s0378437122007166
    DOI: 10.1016/j.physa.2022.128158
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    References listed on IDEAS

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    1. Yang, Zhihu & Li, Zhi & Wu, Te & Wang, Long, 2014. "Effects of payoff-related velocity in the co-evolutionary snowdrift game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 304-311.
    2. Wang, Lei & Xia, Chengyi & Wang, Li & Zhang, Ying, 2013. "An evolving Stag-Hunt game with elimination and reproduction on regular lattices," Chaos, Solitons & Fractals, Elsevier, vol. 56(C), pages 69-76.
    3. repec:hhs:iuiwop:487 is not listed on IDEAS
    4. Scatà, Marialisa & Di Stefano, Alessandro & La Corte, Aurelio & Liò, Pietro & Catania, Emanuele & Guardo, Ermanno & Pagano, Salvatore, 2016. "Combining evolutionary game theory and network theory to analyze human cooperation patterns," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 17-24.
    5. Hu, Xiang & Liu, Xingwen, 2021. "Unfixed-neighbor-mechanism promotes cooperation in evolutionary snowdrift game on lattice," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    6. Shu, Feng & Liu, Xingwen & Fang, Kai & Chen, Hao, 2018. "Memory-based snowdrift game on a square lattice," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 15-26.
    7. Jorgen W. Weibull, 1997. "Evolutionary Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262731215, December.
    8. Ren, Guangming & Wang, Xingyuan, 2014. "Robustness of cooperation in memory-based prisoner’s dilemma game on a square lattice," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 40-46.
    9. Matjaž Perc & Zhen Wang, 2010. "Heterogeneous Aspirations Promote Cooperation in the Prisoner's Dilemma Game," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-8, December.
    10. Deng, Xinyang & Zhang, Zhipeng & Deng, Yong & Liu, Qi & Chang, Shuhua, 2016. "Self-adaptive win-stay-lose-shift reference selection mechanism promotes cooperation on a square lattice," Applied Mathematics and Computation, Elsevier, vol. 284(C), pages 322-331.
    11. Christoph Hauert & Michael Doebeli, 2004. "Spatial structure often inhibits the evolution of cooperation in the snowdrift game," Nature, Nature, vol. 428(6983), pages 643-646, April.
    12. Kokubo, Satoshi & Wang, Zhen & Tanimoto, Jun, 2015. "Spatial reciprocity for discrete, continuous and mixed strategy setups," Applied Mathematics and Computation, Elsevier, vol. 259(C), pages 552-568.
    13. Ming-Jian Fu & Han-Xin Yang, 2018. "Stochastic win-stay-lose-learn promotes cooperation in the spatial public goods game," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 29(04), pages 1-8, April.
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