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Evolutionary dynamics on sequential temporal networks
Population structure is a well-known catalyst for the evolution of cooperation and has traditionally been considered to be static in the course of evolution. Conversely, real-world populations, such as microbiome communities and online social networks, frequently show a progression from tiny, active...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10434888/ https://www.ncbi.nlm.nih.gov/pubmed/37549167 http://dx.doi.org/10.1371/journal.pcbi.1011333 |
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author | Sheng, Anzhi Li, Aming Wang, Long |
author_facet | Sheng, Anzhi Li, Aming Wang, Long |
author_sort | Sheng, Anzhi |
collection | PubMed |
description | Population structure is a well-known catalyst for the evolution of cooperation and has traditionally been considered to be static in the course of evolution. Conversely, real-world populations, such as microbiome communities and online social networks, frequently show a progression from tiny, active groups to huge, stable communities, which is insufficient to be captured by constant structures. Here, we propose sequential temporal networks to characterize growing networked populations, and we extend the theory of evolutionary games to these temporal networks with arbitrary structures and growth rules. We derive analytical rules under which a sequential temporal network has a higher fixation probability for cooperation than its static counterpart. Under neutral drift, the rule is simply a function of the increment of nodes and edges in each time step. But if the selection is weak, the rule is related to coalescence times on networks. In this case, we propose a mean-field approximation to calculate fixation probabilities and critical benefit-to-cost ratios with lower calculation complexity. Numerical simulations in empirical datasets also prove the cooperation-promoting effect of population growth. Our research stresses the significance of population growth in the real world and provides a high-accuracy approximation approach for analyzing the evolution in real-life systems. |
format | Online Article Text |
id | pubmed-10434888 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-104348882023-08-18 Evolutionary dynamics on sequential temporal networks Sheng, Anzhi Li, Aming Wang, Long PLoS Comput Biol Research Article Population structure is a well-known catalyst for the evolution of cooperation and has traditionally been considered to be static in the course of evolution. Conversely, real-world populations, such as microbiome communities and online social networks, frequently show a progression from tiny, active groups to huge, stable communities, which is insufficient to be captured by constant structures. Here, we propose sequential temporal networks to characterize growing networked populations, and we extend the theory of evolutionary games to these temporal networks with arbitrary structures and growth rules. We derive analytical rules under which a sequential temporal network has a higher fixation probability for cooperation than its static counterpart. Under neutral drift, the rule is simply a function of the increment of nodes and edges in each time step. But if the selection is weak, the rule is related to coalescence times on networks. In this case, we propose a mean-field approximation to calculate fixation probabilities and critical benefit-to-cost ratios with lower calculation complexity. Numerical simulations in empirical datasets also prove the cooperation-promoting effect of population growth. Our research stresses the significance of population growth in the real world and provides a high-accuracy approximation approach for analyzing the evolution in real-life systems. Public Library of Science 2023-08-07 /pmc/articles/PMC10434888/ /pubmed/37549167 http://dx.doi.org/10.1371/journal.pcbi.1011333 Text en © 2023 Sheng et al 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 Sheng, Anzhi Li, Aming Wang, Long Evolutionary dynamics on sequential temporal networks |
title | Evolutionary dynamics on sequential temporal networks |
title_full | Evolutionary dynamics on sequential temporal networks |
title_fullStr | Evolutionary dynamics on sequential temporal networks |
title_full_unstemmed | Evolutionary dynamics on sequential temporal networks |
title_short | Evolutionary dynamics on sequential temporal networks |
title_sort | evolutionary dynamics on sequential temporal networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10434888/ https://www.ncbi.nlm.nih.gov/pubmed/37549167 http://dx.doi.org/10.1371/journal.pcbi.1011333 |
work_keys_str_mv | AT shenganzhi evolutionarydynamicsonsequentialtemporalnetworks AT liaming evolutionarydynamicsonsequentialtemporalnetworks AT wanglong evolutionarydynamicsonsequentialtemporalnetworks |