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Length of information-based bidirectional choice in spatial prisoner’s dilemma

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  • Li, Jiaqi
  • Dang, Jianwu
  • Zhang, Jianlei

Abstract

We present a computational model of bidirectional choice based on the length of information of previous payoff to study the cooperative dynamics of iterated prisoner’s dilemma game on directed weighted lattices. For paired two individuals in network, they can attend game under the condition that they both successfully choose the other party with the intensity of unidirectional interaction, and the changing of the intensity of unidirectional interaction according to the relation between individual’s current payoff and its previous payoff which are both gained from the other party. The simulation results show that this evolutionary rule can availably remove the stumbling blocks of cooperators for existence and hugely boost the cooperation in population. Interestingly, for a smaller or moderate cost-to-benefit ratio (0 < r < 0.235), the cooperation level ascends monotonically as the length of information increases; however, for a bigger cost-to-benefit ratio (0.235 ≤ r < 0.249), there are some moderate values of length of information, resulting in the status of full cooperation. Surprisingly, we find that there is an optimal length of information which not just leads to the ultimately full cooperation in population but benefits the learning of cooperation strategy for individual, regardless of the value of cost-to-benefit ratio (0 < r < 0.249). Our results may contribute to the understanding and the analysis of cooperative dynamics of bidirectional choice in network.

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  • Li, Jiaqi & Dang, Jianwu & Zhang, Jianlei, 2020. "Length of information-based bidirectional choice in spatial prisoner’s dilemma," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    • Handle: RePEc:eee:apmaco:v:369:y:2020:i:c:s009630031930829x
    DOI: 10.1016/j.amc.2019.124837
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    References listed on IDEAS

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    1. Jianlei Zhang & Zimin Xu & Zengqiang Chen, 2018. "Effects of strategy switching and network topology on decision-making in multi-agent systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(9), pages 1934-1949, July.
    2. EL-Seidy, Essam, 2015. "The effect of noise and average relatedness between players in iterated games," Applied Mathematics and Computation, Elsevier, vol. 269(C), pages 343-350.
    3. 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.
    4. Yongkui Liu & Xiaojie Chen & Lin Zhang & Long Wang & Matjaž Perc, 2012. "Win-Stay-Lose-Learn Promotes Cooperation in the Spatial Prisoner's Dilemma Game," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-8, February.
    5. K. M. Ariful Kabir & Jun Tanimoto & Zhen Wang, 2018. "Influence of bolstering network reciprocity in the evolutionary spatial Prisoner’s Dilemma game: a perspective," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 91(12), pages 1-10, December.
    6. Li, Jiaqi & Zhang, Chunyan & Sun, Qinglin & Chen, Zengqiang, 2015. "Coevolution between strategy and social networks structure promotes cooperation," Chaos, Solitons & Fractals, Elsevier, vol. 77(C), pages 253-263.
    7. Li, Yan & Ye, Hang & Zhang, Hong, 2016. "Evolution of cooperation driven by social-welfare-based migration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 48-56.
    8. Ye, Wenxing & Fan, Suohai, 2017. "Evolutionary snowdrift game with rational selection based on radical evaluation," Applied Mathematics and Computation, Elsevier, vol. 294(C), pages 310-317.
    9. 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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    Cited by:

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