Empty nodes affect conditional cooperation under reinforcement learning

In social dilemmas, individual behavior generally follows the characteristics of conditional cooperation and emotional conditional cooperation. However, it is hard to adequately explain the behavior patterns of conditional cooperation with the evolutionary game theory. This paper introduces expectation-based reinforcement learning methods in the public goods game to investigate and account for the behavior patterns. Instead of letting individuals occupy the entire network as previous studies have done, we focus on studying individual behavior patterns on a network with empty nodes. The results under total population density show the effectivity of our model as they are consistent with those of the previous studies, that is, individuals' behavior exhibits conditional cooperation and its variant moody conditional cooperation. However, in the network with empty nodes, conditional cooperation shows opposite trends. We finally demonstrate that an appropriate population density can facilitate the maintenance and development of cooperation. (C) 2021 Published by Elsevier Inc.

Automated summary

This paper investigates individual behavior patterns in public goods games using expectation-based reinforcement learning methods. The study finds that conditional cooperation shows opposite trends in networks with empty nodes, while an appropriate population density can facilitate the maintenance and development of cooperation.