Proceedings of the National Academy of Sciences · 2006 · 1K citations · 27 references
Life SciencesBiologyEvolutionary BiologyEvolutionary TheoryGame TheoryMacroevolutionBehavioral Game TheoryEvolution Of CooperationEvolutionary DynamicsEvolutionary Game TheoryNash EquilibriumSocial CooperationCooperation In Social DilemmasEvolutionary Population DynamicsGame-theoretic Evolution
Real populations are heterogeneous, with some individuals having many more contacts than others, contrasting with the homogeneous settings traditionally used in evolutionary game dynamics. The study investigates how cooperation evolves in the most popular social dilemmas. Heterogeneity is modeled by placing players on graphs ranging from single‑scale networks with Gaussian degree distributions to scale‑free networks with power‑law degree distributions. Greater heterogeneity promotes cooperation across all dilemmas, enabling long‑term cooperative behavior to resist short‑term defection and revealing that cooperation depends on the complex ties in scale‑free populations.
Real populations have been shown to be heterogeneous, in which some individuals have many more contacts than others. This fact contrasts with the traditional homogeneous setting used in studies of evolutionary game dynamics. We incorporate heterogeneity in the population by studying games on graphs, in which the variability in connectivity ranges from single-scale graphs, for which heterogeneity is small and associated degree distributions exhibit a Gaussian tale, to scale-free graphs, for which heterogeneity is large with degree distributions exhibiting a power-law behavior. We study the evolution of cooperation, modeled in terms of the most popular dilemmas of cooperation. We show that, for all dilemmas, increasing heterogeneity favors the emergence of cooperation, such that long-term cooperative behavior easily resists short-term noncooperative behavior. Moreover, we show how cooperation depends on the intricate ties between individuals in scale-free populations.
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Statistical mechanics of complex networks
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