Deterministic and stochastic cooperation transitions in evolutionary games on networks
The environment has a strong influence on a population's evolutionary dynamics. Driven by both intrinsic and external factors, the environment is subject to continuous change in nature. To model an ever-changing environment, we develop a framework of evolutionary dynamics with stochastic game transitions, where individuals' behaviors together with the games they play in one time step decide the games to be played next time step. Within this framework, we study the evolution of cooperation in structured populations and find a simple rule: natural selection favors cooperation over defection if the ratio of the benefit provided by an altruistic behavior, $b$, to the corresponding cost, $c$, exceeds $k-k'$, which means $b/c>k-k'$, where $k$ is the average number of neighbors and $k'$ captures the effects from game transitions. We show that even if each individual game opposes cooperation, allowing for a transition between them can result in a favorable outcome for cooperation. Even small variations in different games being played can promote cooperation markedly. Our work suggests that interdependence between the environment and the individuals' behaviors may explain the large-scale cooperation in realistic systems even when cooperation is expensive relative to its benefit.
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