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The impact of initial cooperation fraction on the evolutionary fate in a spatial prisoner's dilemma game

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  • Tanimoto, Jun

Abstract

Although the initial cooperation fraction and its arrangement obviously impacts the equilibrium in spatial prisoner's dilemma (SPD) games, most of the previous studies relying on a simulation approach have by conformity assumed a value of 0.5, thus implying an equal distribution of cooperators and defectors as the established premise. This study first points out that depending on target applications, i.e., biological or general evolutionary dynamical systems, we must select the initial cooperation fraction and its spatial distribution carefully to avoid a misleading conclusion. On the basis of comprehensive numerical experiments and a deductive discussion, wherein both the rule of strategy update and the underlying network are varied, we present a panoramic view of the effects of initial cooperation settings on evolutionary dynamics. Several non-trivial findings are revealed concerning the different impacts of deterministic or stochastic updating, or of homogeneous or heterogeneous underlying topology on evolutionary dynamics.

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  • Tanimoto, Jun, 2015. "The impact of initial cooperation fraction on the evolutionary fate in a spatial prisoner's dilemma game," Applied Mathematics and Computation, Elsevier, vol. 263(C), pages 171-188.
    • Handle: RePEc:eee:apmaco:v:263:y:2015:i:c:p:171-188
    DOI: 10.1016/j.amc.2015.04.043
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    References listed on IDEAS

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    1. Xia, Cheng-yi & Ma, Zhi-qin & Wang, Zhen & Wang, Juan, 2012. "Evaluating fitness by integrating the highest payoff within the neighborhood promotes cooperation in social dilemmas," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6440-6447.
    2. Zhen Wang & Lin Wang & Zi-Yu Yin & Cheng-Yi Xia, 2012. "Inferring Reputation Promotes the Evolution of Cooperation in Spatial Social Dilemma Games," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
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    Cited by:

    1. Tanimoto, Jun, 2016. "A multi-community homogeneous small-world network and its fundamental characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 88-97.
    2. Gao, Liyan & Pan, Qiuhui & He, Mingfeng, 2022. "Advanced defensive cooperators promote cooperation in the prisoner’s dilemma game," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    3. Zhang, Hong & Ye, Hang, 2016. "Role of perception cost in tag-mediated cooperation," Applied Mathematics and Computation, Elsevier, vol. 279(C), pages 76-89.
    4. Tanimoto, Jun, 2017. "Coevolution of discrete, mixed, and continuous strategy systems boosts in the spatial prisoner's dilemma and chicken games," Applied Mathematics and Computation, Elsevier, vol. 304(C), pages 20-27.
    5. 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).
    6. Deng, Xinyang & Jiang, Wen & Wang, Zhen, 2019. "Zero-sum polymatrix games with link uncertainty: A Dempster-Shafer theory solution," Applied Mathematics and Computation, Elsevier, vol. 340(C), pages 101-112.
    7. Geng, Yini & Liu, Yifan & Lu, Yikang & Shen, Chen & Shi, Lei, 2022. "Reinforcement learning explains various conditional cooperation," Applied Mathematics and Computation, Elsevier, vol. 427(C).

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