The evolutionary public goods game model with punishment mechanism in an activity-driven network

Abstract

Abstract Considering the ‘antisocial punishment’ and ‘second-order free-riding’ mechanisms, we propose a public goods game model with a punishment mechanism in an activity-driven network. Simulation results show that the maximal fine of punishment has a greater impact on defectors’ strategies than it does on cooperators’ strategies. That is, as the maximal fine of punishment increases, the final density of cooperators fluctuates within a small interval. Interestingly, when the initial density of cooperators is large, leading to a low density of final cooperators. In contrast, when the initial density of cooperators is small, the final cooperative density is relative large. In addition, when the maximal cost of punishment is sufficiently small, the difference between the final density of cooperators, defectors, punishing cooperators, and punishing defectors is not obvious. However, if the maximal cost of punishment exceeds a certain threshold, the density of punishing cooperators is less than that of cooperators. Meanwhile, the density of punishing defectors is greater than that of defectors.