Evolutionary snowdrift game with rational selection based on radical evaluation

Considering some phenomena observed in the real world, we introduce a rational selecting mechanism based on radical evaluation into evolutionary snowdrift game. In the proposed model, players are endowed with a sense of rationality, which helps evaluate behaviors radically and select neighbors with different attractiveness. Those neighbors who made a preferable strategy and got more payoffs compared with the anti-strategy will have more attractiveness as references. It is found that the selection based on radical evaluation significantly enhances the level of cooperation on regular networks with large neighborhood size K and scale-free networks over a wide range of cost-to-benefit ratio r. Discussions for the transition of spatial patterns and strategy degree distribution at some critical values of the payoff parameter show the effects of the proposed selecting mechanism. The findings may be helpful in understanding cooperative behavior in natural and social systems consisting of rational selection with radical evaluation.