Adaptive interaction driven by learning effect in spatial prisoner’s dilemma

We propose a computing model in which the individual can automatically adjust the interaction intensity with his mentor according to the learning effect to investigate the cooperative dynamics of spatial prisoner’s dilemma. More specifically, when the cumulative payoff of a learner is more than his reference earning, he will strengthen the interaction with his mentor; otherwise, he will reduce that. The experimental results indicate that this mechanism can improve the emergence of cooperation in networked population, and the driving coefficient of interaction intensity plays an important role in promoting cooperation. Interestingly, under a certain social dilemma condition, there exists the smallest driving coefficient, resulting in optimal cooperation due to the positive feedback effect between the individual’s satisfaction frequency and the number of the effective neighbor. Moreover, we find the experimental results in accord with the ones of theoretical prediction obtained from an extended of the classical pair-approximation. These conclusions obtained by considering the relationship with mentor can provide a new perspective for further study on the dynamics of evolutionary game in structured population.