Changing intensity of interaction can resolve prisoner's dilemmas

We put forward a computational model which mainly focuses on the effect of changing the intensity of interaction between individuals to study the evolutionary prisoner's dilemma game in social networks. In this model, an individual will unilaterally increase the intensity of interaction from it to some of its neighbors in case it is satisfied with the current income which it obtains from the neighbor; conversely, the individual will unilaterally reduce the intensity of interaction from it to its neighbor. We show that this simple evolutionary rule can effectively shift the survival barrier of cooperators and drastically facilitate the emergence of cooperation. Interestingly, for a fixed temptation to defect, there exists the smallest increment of intensity of interaction, resulting in a plateau of high cooperation level due to the positive feedback mechanism. Furthermore, we find good agreement between simulation results and theoretical predictions obtained from an extended pair-approximation method. Meanwhile, we illustrate the dynamical evolution of cooperators on the network, and investigate the impact of noise during the strategy updates