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Reinforcement learning account of network reciprocity

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  • Takahiro Ezaki
  • Naoki Masuda

Abstract

Evolutionary game theory predicts that cooperation in social dilemma games is promoted when agents are connected as a network. However, when networks are fixed over time, humans do not necessarily show enhanced mutual cooperation. Here we show that reinforcement learning (specifically, the so-called Bush-Mosteller model) approximately explains the experimentally observed network reciprocity and the lack thereof in a parameter region spanned by the benefit-to-cost ratio and the node’s degree. Thus, we significantly extend previously obtained numerical results.

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  • Takahiro Ezaki & Naoki Masuda, 2017. "Reinforcement learning account of network reciprocity," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-8, December.
    • Handle: RePEc:plo:pone00:0189220
    DOI: 10.1371/journal.pone.0189220
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    References listed on IDEAS

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    1. repec:cup:cbooks:9780511771576 is not listed on IDEAS
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    Cited by:

    1. Correia, A.D. & Leestmaker, L.L. & Stoof, H.T.C. & Broere, J.J., 2022. "Asymmetric games on networks: Towards an Ising-model representation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    2. Han, Xu & Zhao, Xiaowei & Xia, Haoxiang, 2022. "Hybrid learning promotes cooperation in the spatial prisoner’s dilemma game," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    3. Zhang, Liming & Huang, Changwei & Li, Haihong & Dai, Qionglin & Yang, Junzhong, 2021. "Cooperation guided by imitation, aspiration and conformity-driven dynamics in evolutionary games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).

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