Fixation of cooperation in evolutionary games with environmental feedbacks

IF 3.5 2区 数学 Q1 MATHEMATICS, APPLIED Applied Mathematics and Computation Pub Date : 2024-07-31 DOI:10.1016/j.amc.2024.128957
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Abstract

The interaction between strategy and environment widely exists in nature and society. Traditionally, evolutionary dynamics in finite populations are described by the Moran process, where the environment is constant. Therefore, we model the Moran process with environmental feedbacks. Our results show that the selection intensity, which is closely related to the population size, exerts varying influences on evolutionary dynamics. In the case of the specific payoff matrix, cooperation cannot be favored by selection in extremely small-sized populations. The medium-sized populations are beneficial for the evolution of cooperation under intermediate selection intensities. For weak or strong selection intensities, the larger the population size, the more favorable it is for the evolution of cooperation. In the case of the generalized payoff matrix, the low incentives for the defector to cooperate in the degraded state cannot promote the emergence of cooperation. As the incentive for the defector to cooperate in the degraded state increases, selection favors cooperation or defection depending on the population size and selection intensity. For large values of the incentive for the defector facing the cooperative opponent to cooperate in the degraded state, selection always favors cooperation. We further investigate the impact of the time-scale on the fixation probability of cooperation.

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有环境反馈的进化博弈中的固定合作
战略与环境之间的相互作用广泛存在于自然界和社会中。传统上,有限种群的进化动态是由莫兰过程(Moran process)描述的,其中环境是恒定的。因此,我们建立了带有环境反馈的莫兰过程模型。我们的研究结果表明,与种群规模密切相关的选择强度对进化动力学产生了不同的影响。在特定报酬矩阵的情况下,合作在极小种群中无法得到选择的青睐。在中等选择强度下,中等规模的种群有利于合作的进化。对于弱选择强度或强选择强度,种群规模越大,越有利于合作的进化。在广义报酬矩阵的情况下,退化状态下叛逃者的合作动机较低,无法促进合作的出现。随着叛逃者在退化状态下合作的激励增加,选择有利于合作还是叛逃取决于种群规模和选择强度。当叛逃者面对合作对手在退化状态下合作的激励值很大时,选择总是有利于合作。我们进一步研究了时间尺度对合作固定概率的影响。
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来源期刊
CiteScore
7.90
自引率
10.00%
发文量
755
审稿时长
36 days
期刊介绍: Applied Mathematics and Computation addresses work at the interface between applied mathematics, numerical computation, and applications of systems – oriented ideas to the physical, biological, social, and behavioral sciences, and emphasizes papers of a computational nature focusing on new algorithms, their analysis and numerical results. In addition to presenting research papers, Applied Mathematics and Computation publishes review articles and single–topics issues.
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