Reinforcement learning account of network reciprocity

Naoki Masuda*, Takahiro Ezaki

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

7 Citations (Scopus)
260 Downloads (Pure)

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.

Original languageEnglish
Article numbere0189220
Number of pages8
JournalPLoS ONE
Volume12
Issue number12
DOIs
Publication statusPublished - 8 Dec 2017

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