Diversity-optimized cooperation on complex networks

Han-Xin Yang, Wen-Xu Wang, Zhi-Xi Wu, Ying-Cheng Lai, Bing-Hong Wang
2009 Physical Review E  
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
more » ... ding 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.
doi:10.1103/physreve.79.056107 pmid:19518521 fatcat:gvgf3l2mhjhvpjthvwgws7hqye