Ezaki Takahiro, Masuda Naoki
PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, Japan.
Department of Engineering Mathematics, University of Bristol, Clifton, Bristol, United Kingdom.
PLoS One. 2017 Dec 8;12(12):e0189220. doi: 10.1371/journal.pone.0189220. eCollection 2017.
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.
进化博弈论预测，当参与者以网络形式相连时，社会困境博弈中的合作会得到促进。然而，当网络随时间固定不变时，人类不一定会表现出更强的相互合作。在此我们表明，强化学习（具体而言，即所谓的布什-莫斯特勒模型）在由收益成本比和节点度所跨越的参数区域内，近似地解释了实验观察到的网络互惠现象及其缺失情况。因此，我们显著扩展了先前获得的数值结果。
