title: Consolidating birth-death and death-birth processes in structured populations.
abstract: Network models extend evolutionary game theory to settings with spatial or social structure and have provided key insights on the mechanisms underlying the evolution of cooperation. However, network models have also proven sensitive to seemingly small details of the model architecture. Here we investigate two popular biologically motivated models of evolution in finite populations: Death-Birth (DB) and Birth-Death (BD) processes. In both cases reproduction is proportional to fitness and death is random; the only difference is the order of the two events at each time step. Although superficially similar, under DB cooperation may be favoured in structured populations, while under BD it never is. This is especially troubling as natural populations do not follow a strict one birth then one death regimen (or vice versa); such constraints are introduced to make models more tractable. Whether structure can promote the evolution of cooperation should not hinge on a simplifying assumption. Here, we propose a mixed rule where in each time step DB is used with probability δ and BD is used with probability 1-δ. We derive the conditions for selection favouring cooperation under the mixed rule for all social dilemmas. We find that the only qualitatively different outcome occurs when using just BD (δ = 0). This case admits a natural interpretation in terms of kin competition counterbalancing the effect of kin selection. Finally we show that, for any mixed BD-DB update and under weak selection, cooperation is never inhibited by population structure for any social dilemma, including the Snowdrift Game.
authors: Department of Mathematics, University of British Columbia, Vancouver, British Columbia, Canada. jzuke@math.ubc.ca
authors: Joshua
authors: J
authors: Zukewich
authors: Venu
authors: V
authors: Kurella
authors: Michael
authors: M
authors: Doebeli
authors: Christoph
authors: C
authors: Hauert
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D003198:Computer Simulation; D005716:Game Theory; D008954:Models, Biological; D011157:Population Dynamics; D012098:Reproduction
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