title: Aspiration dynamics in structured population acts as if in a well-mixed one.
abstract: Understanding the evolution of human interactive behaviors is important. Recent experimental results suggest that human cooperation in spatial structured population is not enhanced as predicted in previous works, when payoff-dependent imitation updating rules are used. This constraint opens up an avenue to shed light on how humans update their strategies in real life. Studies via simulations show that, instead of comparison rules, self-evaluation driven updating rules may explain why spatial structure does not alter the evolutionary outcome. Though inspiring, there is a lack of theoretical result to show the existence of such evolutionary updating rule. Here we study the aspiration dynamics, and show that it does not alter the evolutionary outcome in various population structures. Under weak selection, by analytical approximation, we find that the favored strategy in regular graphs is invariant. Further, we show that this is because the criterion under which a strategy is favored is the same as that of a well-mixed population. By simulation, we show that this holds for random networks. Although how humans update their strategies is an open question to be studied, our results provide a theoretical foundation of the updating rules that may capture the real human updating rules.
authors.affiliation: Center for Systems and Control, State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing 100871, China.
authors.forename: Jinming
authors.initials: J
authors.lastname: Du
authors.affiliation: Department of Evolutionary Theory, Max-Planck-Institute for Evolutionary Biology, August-Thienemann-Straβe 2, 24306 Plön, Germany.
authors.forename: Bin
authors.initials: B
authors.lastname: Wu
authors.affiliation: Center for Systems and Control, State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing 100871, China.
authors.forename: Long
authors.initials: L
authors.lastname: Wang
mesh_terms: D005075:Biological Evolution; D003198:Computer Simulation; D003299:Cooperative Behavior; D005716:Game Theory; D006801:Humans; D008962:Models, Theoretical; D011153:Population