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Evolutionary dynamics in spatial threshold public goods game with the asymmetric return rate mechanism

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  • Wang, Xianjia
  • Chen, Wenman

Abstract

In reality, cooperators often are provided a higher return rate for their contributions. Inspired by the reality, this paper introduces the asymmetric return rate mechanism, where the return rate is asymmetric between cooperators and defectors. This paper mainly studies how the asymmetric return rate mechanism influences the evolutionary dynamics in spatial threshold public goods game on two different complex networks, the namely square lattice and Barabási-Albert scale-free network. The simulation results show that increasing the sensitivity for the spread of cooperation is more effective than increasing that for the spread of defection not only to promote cooperation, but also to elevate the provision of the public goods. In addition, a moderate value of threshold is the best to elevate both the promotion of cooperation and the provision of the public goods.

Suggested Citation

  • Wang, Xianjia & Chen, Wenman, 2020. "Evolutionary dynamics in spatial threshold public goods game with the asymmetric return rate mechanism," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
  • Handle: RePEc:eee:chsofr:v:136:y:2020:i:c:s0960077920302198
    DOI: 10.1016/j.chaos.2020.109819
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    1. Robin Cubitt & Michalis Drouvelis & Simon Gächter, 2011. "Framing and free riding: emotional responses and punishment in social dilemma games," Experimental Economics, Springer;Economic Science Association, vol. 14(2), pages 254-272, May.
    2. Wang, Xianjia & Lv, Shaojie & Quan, Ji, 2017. "The evolution of cooperation in the Prisoner’s Dilemma and the Snowdrift game based on Particle Swarm Optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 286-295.
    3. Chen Liu & Wen-Bo Du & Wen-Xu Wang, 2014. "Particle Swarm Optimization with Scale-Free Interactions," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-8, May.
    4. He, Nanrong & Chen, Xiaojie & Szolnoki, Attila, 2019. "Central governance based on monitoring and reporting solves the collective-risk social dilemma," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 334-341.
    5. Andrew M. Colman, 2006. "The puzzle of cooperation," Nature, Nature, vol. 440(7085), pages 744-745, April.
    6. Wes Maciejewski & Feng Fu & Christoph Hauert, 2014. "Evolutionary Game Dynamics in Populations with Heterogenous Structures," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-16, April.
    7. Li, Y.S. & Xu, C. & Hui, P.M., 2018. "An effective intervention algorithm for promoting cooperation in the prisoner’s dilemma game with multiple stable states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 400-407.
    8. Wang, Xianjia & Lv, Shaojie, 2019. "The roles of particle swarm intelligence in the prisoner’s dilemma based on continuous and mixed strategy systems on scale-free networks," Applied Mathematics and Computation, Elsevier, vol. 355(C), pages 213-220.
    9. Fernando P. Santos & Francisco C. Santos & Jorge M. Pacheco, 2018. "Social norm complexity and past reputations in the evolution of cooperation," Nature, Nature, vol. 555(7695), pages 242-245, March.
    10. Fang, Yinhai & Xu, Haiyan & Perc, Matjaž & Tan, Qingmei, 2019. "Dynamic evolution of economic networks under the influence of mergers and divestitures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 89-99.
    11. Quan, Ji & Yang, Xiukang & Wang, Xianjia, 2018. "Spatial public goods game with continuous contributions based on Particle Swarm Optimization learning and the evolution of cooperation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 973-983.
    12. 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.
    13. L. H. Shang & X. Li & X. F. Wang, 2006. "Cooperative dynamics of snowdrift game on spatial distance-dependent small-world networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 54(3), pages 369-373, December.
    14. Yang, Han-Xin & Chen, Xiaojie, 2018. "Promoting cooperation by punishing minority," Applied Mathematics and Computation, Elsevier, vol. 316(C), pages 460-466.
    15. 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.
    16. Wang, Qiuling & Meng, Haoran & Gao, Bo, 2019. "Spontaneous punishment promotes cooperation in public good game," Chaos, Solitons & Fractals, Elsevier, vol. 120(C), pages 183-187.
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    1. Wenman Chen & Ji Quan & Xianjia Wang, 2024. "The emergence and maintenance of cooperation in the public goods game under stochastic strategy updating rule with preference," Dynamic Games and Applications, Springer, vol. 14(5), pages 1225-1237, November.
    2. Wang, Jianwei & Dai, Wenhui & Zheng, Yanfeng & Yu, Fengyuan & Chen, Wei & Xu, Wenshu, 2024. "Partial intervention promotes cooperation and social welfare in regional public goods game," Chaos, Solitons & Fractals, Elsevier, vol. 184(C).
    3. Chen, Wei & Wang, Jianwei & Yu, Fengyuan & He, Jialu & Xu, Wenshu & Wang, Rong, 2021. "Effects of emotion on the evolution of cooperation in a spatial prisoner’s dilemma game," Applied Mathematics and Computation, Elsevier, vol. 411(C).

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