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Emergence of complex structures from nonlinear interactions and noise in coevolving networks
We study the joint effect of the non-linearity of interactions and noise on coevolutionary dynamics. We choose the coevolving voter model as a prototype framework for this problem. By numerical simulations and analytical approximations we find three main phases that differ in the absolute magnetisat...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7519106/ https://www.ncbi.nlm.nih.gov/pubmed/32973287 http://dx.doi.org/10.1038/s41598-020-72662-8 |
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author | Raducha, Tomasz San Miguel, Maxi |
author_facet | Raducha, Tomasz San Miguel, Maxi |
author_sort | Raducha, Tomasz |
collection | PubMed |
description | We study the joint effect of the non-linearity of interactions and noise on coevolutionary dynamics. We choose the coevolving voter model as a prototype framework for this problem. By numerical simulations and analytical approximations we find three main phases that differ in the absolute magnetisation and the size of the largest component: a consensus phase, a coexistence phase, and a dynamical fragmentation phase. More detailed analysis reveals inner differences in these phases, allowing us to divide two of them further. In the consensus phase we can distinguish between a weak or alternating consensus and a strong consensus, in which the system remains in the same state for the whole realisation of the stochastic dynamics. In the coexistence phase we distinguish a fully-mixing phase and a structured coexistence phase, where the number of active links drops significantly due to the formation of two homogeneous communities. Our numerical observations are supported by an analytical description using a pair approximation approach and an ad-hoc calculation for the transition between the coexistence and dynamical fragmentation phases. Our work shows how simple interaction rules including the joint effect of non-linearity, noise, and coevolution lead to complex structures relevant in the description of social systems. |
format | Online Article Text |
id | pubmed-7519106 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75191062020-09-29 Emergence of complex structures from nonlinear interactions and noise in coevolving networks Raducha, Tomasz San Miguel, Maxi Sci Rep Article We study the joint effect of the non-linearity of interactions and noise on coevolutionary dynamics. We choose the coevolving voter model as a prototype framework for this problem. By numerical simulations and analytical approximations we find three main phases that differ in the absolute magnetisation and the size of the largest component: a consensus phase, a coexistence phase, and a dynamical fragmentation phase. More detailed analysis reveals inner differences in these phases, allowing us to divide two of them further. In the consensus phase we can distinguish between a weak or alternating consensus and a strong consensus, in which the system remains in the same state for the whole realisation of the stochastic dynamics. In the coexistence phase we distinguish a fully-mixing phase and a structured coexistence phase, where the number of active links drops significantly due to the formation of two homogeneous communities. Our numerical observations are supported by an analytical description using a pair approximation approach and an ad-hoc calculation for the transition between the coexistence and dynamical fragmentation phases. Our work shows how simple interaction rules including the joint effect of non-linearity, noise, and coevolution lead to complex structures relevant in the description of social systems. Nature Publishing Group UK 2020-09-24 /pmc/articles/PMC7519106/ /pubmed/32973287 http://dx.doi.org/10.1038/s41598-020-72662-8 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Raducha, Tomasz San Miguel, Maxi Emergence of complex structures from nonlinear interactions and noise in coevolving networks |
title | Emergence of complex structures from nonlinear interactions and noise in coevolving networks |
title_full | Emergence of complex structures from nonlinear interactions and noise in coevolving networks |
title_fullStr | Emergence of complex structures from nonlinear interactions and noise in coevolving networks |
title_full_unstemmed | Emergence of complex structures from nonlinear interactions and noise in coevolving networks |
title_short | Emergence of complex structures from nonlinear interactions and noise in coevolving networks |
title_sort | emergence of complex structures from nonlinear interactions and noise in coevolving networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7519106/ https://www.ncbi.nlm.nih.gov/pubmed/32973287 http://dx.doi.org/10.1038/s41598-020-72662-8 |
work_keys_str_mv | AT raduchatomasz emergenceofcomplexstructuresfromnonlinearinteractionsandnoiseincoevolvingnetworks AT sanmiguelmaxi emergenceofcomplexstructuresfromnonlinearinteractionsandnoiseincoevolvingnetworks |