The prisoner's dilemma game model with reconnection-cost in adaptive network

Abstract

Individuals could adaptively change their neighbors to maximize their own profits, however, this may bring some potential losses for the changers. This paper explores the prisoner's dilemma game (PDG) model in the adaptive networks. For each player involved in the game, we set 1 − p as his reconnection probability, and h as the reconnection-cost, and then perform our model in the artificially synthesized network using Monte Carlo method. Simulation results show that when the temptation benefit T is smaller, the larger the decision variable p, the greater the final density of cooperators. However, when T is relatively larger, the larger the decision variable p, the smaller the final density of cooperators. Since individuals may autonomously disconnect from those "malicious" relations, the system is eventually dominated by cooperators even if the temptation benefit T is relatively large. However, as the temptation benefit T continues to increase, the defectors' density could exceed that of cooperators. We also observe that the reconnection-cost h has almost no effect on the number of one's neighbors, but the value of p has a greater influence on it. Finally, the degree in the steady state obeys the normal distribution, and the density of cooperators in the entire population is negatively related to the density of changers.

How the individuals' memory affects the evolution of prisoners' dilemma game in the two-layer network

“…We can not only improve human understanding of their own behavior, but also improve their understanding of the evolution of social systems through the study of human behavior [1][2][3][4]. Although it is still a huge challenge to understand the cooperative phenomenon of complex systems in different disciplines for individuals [5,6], the game theory and complex network methods provide some effective ways to effectively study the phenomenon of individual cooperation [7][8][9][10][11][12][13][14].…”