Recent work has revealed that success-driven migration can promote cooperation among selfish individuals in evolutionary games. This migration mechanism relies, however, on nonlocal information about the states of the individuals and their computational capabilities for prediction. We investigate the role of adaptive migration in cooperative behavior in the framework of spatial game by proposing an alternative migration strategy that requires only local information obtainable through game interactions. Our results demonstrate that adaptive migration can be effective in promoting cooperation in two ways. First, there exists an optimal degree of migration associated with the density of empty sites and migration speed, which leads to the highest level of cooperation. Second, adaptive migration can induce an outbreak of cooperation from an environment dominated by defectors. These findings hold for common types of evolutionary games that involve pairwise interactions.

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We propose a strategy for achieving maximum cooperation in evolutionary games on complex networks. Each individual is assigned a weight that is proportional to the power of its degree, where the exponent ␣ is an adjustable parameter that controls the level of diversity among individuals in the network. During the evolution, every individual chooses one of its neighbors as a reference with a probability proportional to the weight of the neighbor, and updates its strategy depending on their payoff difference. It is found that there exists an optimal value of ␣, for which the level of cooperation reaches maximum. This phenomenon indicates that, although high-degree individuals play a prominent role in maintaining the cooperation, too strong influences from the hubs may counterintuitively inhibit the diffusion of cooperation. Other pertinent quantities such as the payoff, the cooperator density as a function of the degree, and the payoff distribution are also investigated computationally and theoretically. Our results suggest that in order to achieve strong cooperation on a complex network, individuals should learn more frequently from neighbors with higher degrees, but only to a certain extent.