Evolution of Cooperation in Signed Networks Under a Cheating Strategy

Abstract

Evolutionary game theory tries to analyze the underlying stochastic and nonlinear decision-making processes of individuals, which provides a comprehensive understanding of the emergence of individual cooperative behaviors. The existing works mainly focus on the evolutionary game of players with undirected relationships, while neglecting conflicting relationships between players. In this paper, we study the evolution of cooperation in signed networks with conflicting relationships. Moreover, we propose a cheating strategy to promote the cooperation of individuals in the evolutionary game. In this strategy, to gain a maximum payoff, individuals will provide reliable payoff information to his friends, whereas they give unreliable payoff information to his opponents. Experiments on both simulated and real-world signed networks show that this cheating strategy can effectively promote the cooperation of players in signed networks.