Cooperation in Public Goods Games: Leveraging Other-Regarding Reinforcement Learning on Hypergraphs
Bo-Ying Li, Zhen-Na Zhang, Guo-Zhong Zheng, Chao-Ran Cai, Ji-Qiang, Zhang, and Chen Li

TL;DR
This paper explores how cooperation emerges in public goods games when players use other-regarding reinforcement learning on hypergraphs, revealing phase transitions and spatial patterns that influence cooperation levels.
Contribution
It introduces a novel model combining other-regarding RL with hypergraph interactions, analyzing cooperation transitions and spatial structures in group settings.
Findings
Three cooperation regimes identified: AC, MC, HC.
Abrupt transitions occur near critical synergy factors.
Long-sighted players with low exploration foster cooperation.
Abstract
Cooperation as a self-organized collective behavior plays a significant role in the evolution of ecosystems and human society. Reinforcement learning (RL) offers a new perspective, distinct from imitation learning in evolutionary games, for exploring the mechanisms underlying its emergence. However, most existing studies with the public good game (PGG) employ a self-regarding setup or are on pairwise interaction networks. Players in the real world, however, optimize their policies based not only on their histories but also on the histories of their co-players, and the game is played in a group manner. In the work, we investigate the evolution of cooperation in the PGG under the other-regarding reinforcement learning evolutionary game (OR-RLEG) on hypergraph by combining the Q-learning algorithm and evolutionary game framework, where other players' action history is incorporated and the…
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Taxonomy
TopicsExperimental Behavioral Economics Studies
