Effects of Punishment in a Structure Population Playing the Social Dilemma Game by Using Discrete PSO Algorithm

The evolution of strategies in n-choice social dilemma game with punishment is studied on spatial environment. This paper presents and investigates the application of co-evolutionary training techniques based on discrete particle swarm optimization (PSO) to evolve cooperation, and exploring different parameter configurations via numerical simulations. Key model parameters include the number of players, the interaction topology, the punishment and the cost-to-benefit ratio. The simulation results reveal that the punishment can promote the levels of cooperative behaviors to some extent, the cost-to-benefit ratio and the number of players is important factors in determining the strategy evolution.