Effects of trust-driven updating rule based on reputation in spatial prisoner’s dilemma games

Abstract

In real life, people tend to trust those with higher reputations, but if they have a lower payoff, people will not learn from them. Therefore, individuals are more inclined to learn strategies from individuals with higher reputations and payoffs. Here, we introduce a trust-driven updating rule based on reputation into prisoner’s dilemma games on ER random networks and BA scale-free networks and investigate the effects of this model on cooperation. To control the influence of reputation in the system, we introduce a heterogeneity parameter to describe the effect of reputation on trust. The results show that when individuals update their policy through this trust-driven updating rule, the level of cooperation is significantly promoted. The increase in the heterogeneity parameter will promote the level of cooperation. Besides, we study the effect of the correlation between the node degree and reputation heterogeneity on cooperation due to the different influences of individuals. Additionally, we investigate the robustness of the model in real social networks and conduct experiments on the dolphin and karate networks, respectively, finding that the qualitative features of the results are unchanged.