Structured heterogeneity of network in evolutionary social dilemmas under assortative linking and learning

We use assortativity in linking and learning by studying social dilemmas on a dynamic network, where assortativity in linking runs faster than assortativity in learning, so that both can work together. The emergence of cooperation is based on the structured heterogeneity of a network, which includes heterogeneous interactions and a heterogeneous population structure made by assortativity. In an infinite population, cooperation is more likely to happen in the Snowdrift game when there is heterogeneous interaction, in the Stag-hunt game when there is heterogeneous population structure, and in the Prisoner's Dilemma game when there are two different kinds of settings. In a finite population size, the heterogeneous interaction plays an important role in emergence of cooperation in all dilemmas. & COPY; 2023 Elsevier B.V. All rights reserved.

Automated summary

We utilize assortativity in linking and learning to facilitate cooperation in social dilemmas on a dynamic network. The structured heterogeneity of the network, including heterogeneous interactions and a heterogeneous population structure, contributes to the emergence of cooperation. In both infinite and finite population sizes, heterogeneous interactions play a crucial role in promoting cooperation across different social dilemma games.