The DOI matches its Crossref record; 128 of the 128 references it cites were found in a registry or an index.
Strategy selection in structured populations
Open accessAbstract
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(abcd). We study a mutation and selection process. For weak selection strategy A is favored over B if and only if sigma a+b>c+sigma d. This means the effect of population structure on strategy selection can be described by a single parameter, sigma. We present the values of sigma 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 sigma, 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 sigma and the critical benefit to cost ratio for the evolution of cooperation. The single parameter, sigma, allows us to quantify the ability of a population structure to promote the evolution of cooperation or to choose efficient equilibria in coordination games.
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“Broadly speaking, our work highlights the significance of integrating multiple communities into one system, since treating communities individually and independently may lead to erroneous conclusions about behavioral dynamics in the population as a whole [53].”
From Strategy evolution on dynamic networks (2023) · cites this paper as [53]
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Self-citationEvolutionary Games and Population Dynamics
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Nowak, M. A., Tarnita, C. E., & Wilson, E. O. (2010) · Nature · doi:10.1038/nature09205 · Cited by 1,530
Self-citationRand, D. G., & Nowak, M. A. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.06.003 · Cited by 1,308
Self-citationEvolutionary dynamics on any population structure
Allen, B., Lippner, G., Chen, Y.-T., Fotouhi, B., Momeni, N., Yau, S.-T., & Nowak, M. A. (2017) · Nature · doi:10.1038/nature21723 · Cited by 534
Self-citationEvolutionary dynamics in structured populations
Nowak, M. A., Tarnita, C. E., & Tibor Antal, A. (2009) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2009.0215 · Cited by 505
Self-citationImitation dynamics of vaccination behaviour on social networks
Fu, F., Rosenbloom, D. I. S., Wang, L., & Nowak, M. A. (2010) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2010.1107 · Cited by 456
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Metadata
- Authors
- Tarnita, C. E., Ohtsuki, H., Antal, T., Fu, F., & Nowak, M. A.
- Affiliations
- Harvard University (US)
- Tokyo Institute of Technology (JP)
- Corresponding author
- Tarnita, Corina E.
- Venue
- Journal of Theoretical Biology
- Publisher
- Elsevier BV
- Published
- 2009-08
- Volume
- 259
- Issue
- 3
- Pages
- 570-581
- ISSN
- 0022-5193 (ISSN-L)
- PMC
- PMC2710410
- OpenAlex
- W2103207495
- Cited by
- 281
- References
- 128
- Citations by year
- 2026: 21 · 2025: 12 · 2024: 16 · 2023: 19 · 2022: 12 · 2021: 16 · 2020: 7 · 2019: 17 · 2018: 19 · 2017: 13 · 2016: 21 · 2015: 17 · 2014: 29 · 2013: 20 · 2012: 16
- FWCI
- 45.29
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Theoretical Biology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- National Institute of General Medical Sciences (R01GM078986)
- Topics
- Social Sciences › Sociology and Political Science › Evolutionary Game Theory and Cooperation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Game Theory and Applications
- Keywords
- structured populations, evolutionary game theory, frequency-dependent selection, weak selection, evolution of cooperation, coordination games, graph games, phenotype space, sets, mutation and selection, well-mixed populations
- MeSH terms
- Animals, Computer Simulation, Game Theory, Models, Genetic, Mutation, Population Density, Population Dynamics, Selection, Genetic
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