Strategy selection in structured populations

Corina E. Tarnita, Hisashi Ohtsuki, Tibor Antal, Feng Fu, Martin A. Nowak

Research output: Contribution to journalArticlepeer-review

167 Scopus citations

Abstract

Evolutionary game theory studies frequency dependent selection. The fitness of a strategy is not constant, but depends on the relative frequencies of strategies in the population. This type of evolutionary dynamics occurs in many settings of ecology, infectious disease dynamics, animal behavior and social interactions of humans. Traditionally evolutionary game dynamics are studied in well-mixed populations, where the interaction between any two individuals is equally likely. There have also been several approaches to study evolutionary games in structured populations. In this paper we present a simple result that holds for a large variety of population structures. We consider the game between two strategies, A and B, described by the payoff matrix (frac(a, c) frac(b, d)). We study a mutation and selection process. For weak selection strategy A is favored over B if and only if σ a + b > c + σ d. This means the effect of population structure on strategy selection can be described by a single parameter, σ. We present the values of σ for various examples including the well-mixed population, games on graphs, games in phenotype space and games on sets. We give a proof for the existence of such a σ, which holds for all population structures and update rules that have certain (natural) properties. We assume weak selection, but allow any mutation rate. We discuss the relationship between σ and the critical benefit to cost ratio for the evolution of cooperation. The single parameter, σ, allows us to quantify the ability of a population structure to promote the evolution of cooperation or to choose efficient equilibria in coordination games.

Original languageEnglish (US)
Pages (from-to)570-581
Number of pages12
JournalJournal of Theoretical Biology
Volume259
Issue number3
DOIs
StatePublished - Aug 7 2009

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Keywords

  • Evolutionary dynamics
  • Finite populations
  • Stochastic effects

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