title: Coveting thy neighbors fitness as a means to resolve social dilemmas.
abstract: In spatial evolutionary games the fitness of each individual is traditionally determined by the payoffs it obtains upon playing the game with its neighbors. Since defection yields the highest individual benefits, the outlook for cooperators is gloomy. While network reciprocity promotes collaborative efforts, chances of averting the impending social decline are slim if the temptation to defect is strong. It is, therefore, of interest to identify viable mechanisms that provide additional support for the evolution of cooperation. Inspired by the fact that the environment may be just as important as inheritance for individual development, we introduce a simple switch that allows a player to either keep its original payoff or use the average payoff of all its neighbors. Depending on which payoff is higher, the influence of either option can be tuned by means of a single parameter. We show that, in general, taking into account the environment promotes cooperation. Yet coveting the fitness of one's neighbors too strongly is not optimal. In fact, cooperation thrives best only if the influence of payoffs obtained in the traditional way is equal to that of the average payoff of the neighborhood. We present results for the prisoner's dilemma and the snowdrift game, for different levels of uncertainty governing the strategy adoption process, and for different neighborhood sizes. Our approach outlines a viable route to increased levels of cooperative behavior in structured populations, but one that requires a thoughtful implementation.
authors.affiliation: School of Physics, Nankai University, Tianjin 300071, China.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Wang
authors.forename: Aleksandra
authors.initials: A
authors.lastname: Murks
authors.forename: Wen-Bo
authors.initials: WB
authors.lastname: Du
authors.forename: Zhi-Hai
authors.initials: ZH
authors.lastname: Rong
authors.forename: Matjaž
authors.initials: M
authors.lastname: Perc
mesh_terms: D003198:Computer Simulation; D003299:Cooperative Behavior; D005716:Game Theory; D056084:Genetic Fitness; D009010:Monte Carlo Method; D012919:Social Behavior; D013997:Time Factors