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Multi-strategy evolutionary games: A Markov chain approach

Interacting strategies in evolutionary games is studied analytically in a well-mixed population using a Markov chain method. By establishing a correspondence between an evolutionary game and Markov chain dynamics, we show that results obtained from the fundamental matrix method in Markov chain dynam...

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Detalles Bibliográficos
Autores principales: Hajihashemi, Mahdi, Aghababaei Samani, Keivan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8853582/
https://www.ncbi.nlm.nih.gov/pubmed/35176094
http://dx.doi.org/10.1371/journal.pone.0263979
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author Hajihashemi, Mahdi
Aghababaei Samani, Keivan
author_facet Hajihashemi, Mahdi
Aghababaei Samani, Keivan
author_sort Hajihashemi, Mahdi
collection PubMed
description Interacting strategies in evolutionary games is studied analytically in a well-mixed population using a Markov chain method. By establishing a correspondence between an evolutionary game and Markov chain dynamics, we show that results obtained from the fundamental matrix method in Markov chain dynamics are equivalent to corresponding ones in the evolutionary game. In the conventional fundamental matrix method, quantities like fixation probability and fixation time are calculable. Using a theorem in the fundamental matrix method, conditional fixation time in the absorbing Markov chain is calculable. Also, in the ergodic Markov chain, the stationary probability distribution that describes the Markov chain’s stationary state is calculable analytically. Finally, the Rock, scissor, paper evolutionary game are evaluated as an example, and the results of the analytical method and simulations are compared. Using this analytical method saves time and computational facility compared to prevalent simulation methods.
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spelling pubmed-88535822022-02-18 Multi-strategy evolutionary games: A Markov chain approach Hajihashemi, Mahdi Aghababaei Samani, Keivan PLoS One Research Article Interacting strategies in evolutionary games is studied analytically in a well-mixed population using a Markov chain method. By establishing a correspondence between an evolutionary game and Markov chain dynamics, we show that results obtained from the fundamental matrix method in Markov chain dynamics are equivalent to corresponding ones in the evolutionary game. In the conventional fundamental matrix method, quantities like fixation probability and fixation time are calculable. Using a theorem in the fundamental matrix method, conditional fixation time in the absorbing Markov chain is calculable. Also, in the ergodic Markov chain, the stationary probability distribution that describes the Markov chain’s stationary state is calculable analytically. Finally, the Rock, scissor, paper evolutionary game are evaluated as an example, and the results of the analytical method and simulations are compared. Using this analytical method saves time and computational facility compared to prevalent simulation methods. Public Library of Science 2022-02-17 /pmc/articles/PMC8853582/ /pubmed/35176094 http://dx.doi.org/10.1371/journal.pone.0263979 Text en © 2022 Hajihashemi, Aghababaei Samani https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Hajihashemi, Mahdi
Aghababaei Samani, Keivan
Multi-strategy evolutionary games: A Markov chain approach
title Multi-strategy evolutionary games: A Markov chain approach
title_full Multi-strategy evolutionary games: A Markov chain approach
title_fullStr Multi-strategy evolutionary games: A Markov chain approach
title_full_unstemmed Multi-strategy evolutionary games: A Markov chain approach
title_short Multi-strategy evolutionary games: A Markov chain approach
title_sort multi-strategy evolutionary games: a markov chain approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8853582/
https://www.ncbi.nlm.nih.gov/pubmed/35176094
http://dx.doi.org/10.1371/journal.pone.0263979
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