title: Natural selection of cooperation and degree hierarchy in heterogeneous populations.
abstract: One of the current theoretical challenges to the explanatory powers of Evolutionary Theory is the understanding of the observed evolutionary survival of cooperative behavior when selfish actions provide higher fitness (reproductive success). In unstructured populations natural selection drives cooperation to extinction. However, when individuals are allowed to interact only with their neighbors, specified by a graph of social contacts, cooperation-promoting mechanisms (known as lattice reciprocity) offer to cooperation the opportunity of evolutionary survival. Recent numerical works on the evolution of Prisoner's Dilemma in complex network settings have revealed that graph heterogeneity dramatically enhances the lattice reciprocity. Here we show that in highly heterogeneous populations, under the graph analog of replicator dynamics, the fixation of a strategy in the whole population is in general an impossible event, for there is an asymptotic partition of the population in three subsets, two in which fixation of cooperation or defection has been reached and a third one which experiences cycles of invasion by the competing strategies. We show how the dynamical partition correlates with connectivity classes and characterize the temporal fluctuations of the fluctuating set, unveiling the mechanisms stabilizing cooperation in macroscopic scale-free structures.
authors.affiliation: Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50009 Zaragoza, Spain.
authors.forename: Jesús
authors.initials: J
authors.lastname: Gómez-Gardeñes
authors.forename: Julia
authors.initials: J
authors.lastname: Poncela
authors.forename: Luis
authors.initials: L
authors.lastname: Mario Floría
authors.forename: Yamir
authors.initials: Y
authors.lastname: Moreno
mesh_terms: D000818:Animals; D005075:Biological Evolution; D003299:Cooperative Behavior; D005716:Game Theory; D008957:Models, Genetic; D012641:Selection, Genetic