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Evolution of cooperation in lattice population with adaptive interaction intensity

Author

Listed:
  • Li, Zhi
  • Gao, Jia
  • Suh, Il Hong
  • Wang, Long

Abstract

We study the evolutionary Prisoner’s Dilemma game among individuals endowed with adaptively interaction intensity. Individuals adjust their interaction intensity according to the rules “payoff increase-high intensity, payoff decrease-low intensity”: if an individual’s payoff increases compared with that in the previous generation, he raises his interaction intensity; otherwise, he reduces the probability of interaction. We find that if individuals can adjust their interaction intensity with a proper scale, cooperation can be promoted. Interestingly, individuals with low interaction intensity usually hold the boundary of cooperator cluster. Such spatial distribution can alleviate the exploitation from defectors to cooperators since the interaction between cooperators and defectors is weakened. We hope our work can yield some insight into investigation of the evolution of cooperation in structured population.

Suggested Citation

  • Li, Zhi & Gao, Jia & Suh, Il Hong & Wang, Long, 2013. "Evolution of cooperation in lattice population with adaptive interaction intensity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2046-2051.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:9:p:2046-2051
    DOI: 10.1016/j.physa.2012.12.031
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    Citations

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    Cited by:

    1. Lu, Shounan & Zhu, Ge & Dai, Jianhua, 2023. "Promoting effect of adaptive interaction based on random neighbors to cooperation in the spatial prisoner's dilemma game," Applied Mathematics and Computation, Elsevier, vol. 450(C).
    2. Wang, Zhen & Zhang, Geng-shun & Ding, Hong & Cui, Guang-Hai & Yao, Ye, 2019. "Strategy imitation behavior driven influence adjustment promotes cooperation in spatial prisoner’s dilemma game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    3. Zhang, Hong, 2015. "Moderate tolerance promotes tag-mediated cooperation in spatial Prisoner’s dilemma game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 52-61.
    4. Cui, Guang-Hai & Wang, Zhen & Yang, Yan-Cun & Tian, Sheng-Wen & Yue, Jun, 2018. "Heterogeneous game resource distributions promote cooperation in spatial prisoner’s dilemma game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1191-1200.
    5. Cui, Guang-Hai & Li, Ming-Chu & Fan, Xin-Xin & Deonauth, Nakema & Wang, Zhen, 2014. "Optimism when winning and cautiousness when losing promote cooperation in the spatial prisoner’s dilemma game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 181-189.
    6. Liu, Yandi & Wang, Hexin & Ding, Yi & Yang, Xuan & Dai, Yu, 2022. "Can weak diversity help in propagating cooperation? Invasion of cooperators at the conformity-conflict boundary," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    7. Lu, Shounan & Dai, Jianhua & Zhu, Ge & Guo, Li, 2023. "Investigating the effectiveness of interaction-efficiency-driven strategy updating under progressive-interaction for the evolution of the prisoner's dilemma game," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    8. Han, Xu & Zhao, Xiaowei & Xia, Haoxiang, 2018. "Promotion of cooperation by adaptive interaction: The role of heterogeneity in neighborhoods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 483-491.
    9. Sun, Chengbin & Luo, Chao & Li, Junqiu, 2020. "Aspiration-based co-evolution of cooperation with resource allocation on interdependent networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).

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