ArticlePLoS computational biology2016
Evolutionary Games of Multiplayer Cooperation on Graphs.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Hyperedge approximation for stochastic processes on higher-order networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
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- Higher-order interactions shape collective human behaviour.Nature human behaviour · 2025Review
- Density amplifiers of cooperation for spatial games.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- The evolution of environmentally mediated social interactions and posthumous spite under isolation by distance.PLoS computational biology · 2024Article
- Nonlinear social evolution and the emergence of collective action.PNAS nexus · 2024Article
- Evolutionary dynamics on sequential temporal networks.PLoS computational biology · 2023Article
- A game-based approach for designing a collaborative evolution mechanism for unmanned swarms on community networks.Scientific reports · 2022Article
- The role of evolutionary game theory in spatial and non-spatial models of the survival of cooperation in cancer: a review.Journal of the Royal Society, Interface · 2022Review
- Dynamics on higher-order networks: a review.Journal of the Royal Society, Interface · 2022Review
- Cost efficiency of institutional incentives for promoting cooperation in finite populations.Proceedings. Mathematical, physical, and engineering sciences · 2021Article
- Social goods dilemmas in heterogeneous societies.Nature human behaviour · 2020Article
- Transient amplifiers of selection and reducers of fixation for death-Birth updating on graphs.PLoS computational biology · 2020Article
- Evolutionary games on isothermal graphs.Nature communications · 2019Article
- Diffusion-driven enhancement of the antibiotic resistance selection window.Journal of the Royal Society, Interface · 2019Article
- Close spatial arrangement of mutants favors and disfavors fixation.PLoS computational biology · 2019Article
- Motion, fixation probability and the choice of an evolutionary process.PLoS computational biology · 2019Article
- Spatial reciprocity in the evolution of cooperation.Proceedings. Biological sciences · 2019Article
- Evolutionary multiplayer games on graphs with edge diversity.PLoS computational biology · 2019Article
- A mathematical formalism for natural selection with arbitrary spatial and genetic structure.Journal of mathematical biology · 2019Article
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Authors and funding
4 authors.
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Abstract
There has been much interest in studying evolutionary games in structured populations, often modeled as graphs. However, most analytical results so far have only been obtained for two-player or linear games, while the study of more complex multiplayer games has been usually tackled by computer simulations. Here we investigate evolutionary multiplayer games on graphs updated with a Moran death-Birth process. For cycles, we obtain an exact analytical condition for cooperation to be favored by natural selection, given in terms of the payoffs of the game and a set of structure coefficients. For regular graphs of degree three and larger, we estimate this condition using a combination of pair approximation and diffusion approximation. For a large class of cooperation games, our approximations suggest that graph-structured populations are stronger promoters of cooperation than populations lacking spatial structure. Computer simulations validate our analytical approximations for random regular graphs and cycles, but show systematic differences for graphs with many loops such as lattices. In particular, our simulation results show that these kinds of graphs can even lead to more stringent conditions for the evolution of cooperation than well-mixed populations. Overall, we provide evidence suggesting that the complexity arising from many-player interactions and spatial structure can be captured by pair approximation in the case of random graphs, but that it need to be handled with care for graphs with high clustering.
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