Evolutionary Game Model with Group Decision-Making in Signed Social Networks

When facing choices such as getting vaccinated, traveling, and going to the cinema, individuals can be classified into two groups based on their decision-making strategy: 1) selfish agents, who make decisions to maximize their own payoff; and 2) collectivist agents, who try to maximize the group’s benefits. We model both strategies in the evolutionary dynamics of signed networks to examine how both types of agents choose to behave in society. In particular, we run simulations and find that when agents’ strategies are fixed, collectivist agents or group decision-making are conducive to cooperation. Some network properties such as having structural balance and a clear modular organization promote cooperation. However, when strategic learning is considered, the dynamics become highly uncertain. When collectivist agents are initially located in a closely related group, the cooperation ratio and proportion of collectivist agents both rise. These observations reveal that besides network properties, the topology distribution of agents with different decision-making tendencies plays a significant role in social evolution.