Asymmetric evaluation of fitness enhances spatial reciprocity in social dilemmas

Evolutionary game theory has been a powerful tool to understand the ubiquity of cooperation in many real-world systems which ranges from biological to economical and social sciences. In this paper, we propose a novel game model, which considers different fitness evaluation means for focal agent and his randomly chosen neighbor, to characterize the asymmetry of information during the game playing. When playing the game, the focal agent obtains its own fitness which includes its own payoff and the average payoff of his neighbors. However, due to the limited information for the chosen neighbor, his fitness can not be evaluated as the focal agent but just the traditional way, namely his fitness equals to his payoff. Large-scale numerical simulations indicate that this kind of asymmetric fitness computation can have substantial effects on the cooperative behaviors on the regular lattice. Interestingly, the larger the asymmetric factor α, the higher the cooperation level ρC. Meanwhile, introducing the asymmetric fitness evaluation can induce the cooperative clusters to become larger and larger as α increases. The phase space between b and α can be enlarged, and thereby the ranges for cooperators to survive in the sea of defectors can be widely extended. Our findings can greatly be conducive to interpret the emergence of cooperation within the population.