Suppr超能文献

网络互惠性的强化学习解释

Reinforcement learning account of network reciprocity.

作者信息

Ezaki Takahiro, Masuda Naoki

机构信息

PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, Japan.

Department of Engineering Mathematics, University of Bristol, Clifton, Bristol, United Kingdom.

出版信息

PLoS One. 2017 Dec 8;12(12):e0189220. doi: 10.1371/journal.pone.0189220. eCollection 2017.

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf04/5722284/c43b1a4e7a54/pone.0189220.g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验