TY - JOUR
T1 - Reinforcement learning account of network reciprocity
AU - Ezaki, Takahiro
AU - Masuda, Naoki
N1 - Funding Information:
TE acknowledges the support provided through PRESTO, Japan Science and Technology Agency (No. JPMJPR16D2); and Kawarabayashi Large Graph Project, ERATO, Japan Science and Technology Agency (No. JPMJER1201, URL: http://www.jst.go.jp/erato/kawarabayashi/english/ index.html). NM acknowledges the support provided through CREST, Japan Science and Technology Agency (No. JPMJCR1304); and Kawarabayashi Large Graph Project, ERATO, Japan Science and Technology Agency (No. JPMJER1201, URL: http://www.jst.go.jp/erato/ kawarabayashi/english/index.html). We acknowledge Hisashi Ohtsuki for valuable comments on the manuscript. TE acknowledges the support provided through PRESTO, JST (No. JPMJPR16D2) and Kawarabayashi Large Graph Project, ERATO, JST (No. JPMJER1201). NM acknowledges the support provided through, CREST, JST (No. JPMJCR1304) and Kawarabayashi Large Graph Project, ERATO, JST (No. JPMJER1201).
Publisher Copyright:
© 2017 Ezaki, Masuda. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2017/12
Y1 - 2017/12
N2 - 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.
AB - 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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U2 - 10.1371/journal.pone.0189220
DO - 10.1371/journal.pone.0189220
M3 - Article
C2 - 29220413
AN - SCOPUS:85037522869
SN - 1932-6203
VL - 12
JO - PloS one
JF - PloS one
IS - 12
M1 - e0189220
ER -