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Social influence and interaction bias can drive emergent behavioural specialization and modular social networks across systems
In social systems ranging from ant colonies to human society, behavioural specialization—consistent individual differences in behaviour—is commonplace: individuals can specialize in the tasks they perform (division of labour (DOL)), the political behaviour they exhibit (political polarization) or th...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7014790/ https://www.ncbi.nlm.nih.gov/pubmed/31910771 http://dx.doi.org/10.1098/rsif.2019.0564 |
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author | Tokita, Christopher K. Tarnita, Corina E. |
author_facet | Tokita, Christopher K. Tarnita, Corina E. |
author_sort | Tokita, Christopher K. |
collection | PubMed |
description | In social systems ranging from ant colonies to human society, behavioural specialization—consistent individual differences in behaviour—is commonplace: individuals can specialize in the tasks they perform (division of labour (DOL)), the political behaviour they exhibit (political polarization) or the non-task behaviours they exhibit (personalities). Across these contexts, behavioural specialization often co-occurs with modular and assortative social networks, such that individuals tend to associate with others that have the same behavioural specialization. This raises the question of whether a common mechanism could drive co-emergent behavioural specialization and social network structure across contexts. To investigate this question, here we extend a model of self-organized DOL to account for social influence and interaction bias among individuals—social dynamics that have been shown to drive political polarization. We find that these same social dynamics can also drive emergent DOL by forming a feedback loop that reinforces behavioural differences between individuals, a feedback loop that is impacted by group size. Moreover, this feedback loop also results in modular and assortative social network structure, whereby individuals associate strongly with those performing the same task. Our findings suggest that DOL and political polarization—two social phenomena not typically considered together—may actually share a common social mechanism. This mechanism may result in social organization in many contexts beyond task performance and political behaviour. |
format | Online Article Text |
id | pubmed-7014790 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-70147902020-02-15 Social influence and interaction bias can drive emergent behavioural specialization and modular social networks across systems Tokita, Christopher K. Tarnita, Corina E. J R Soc Interface Life Sciences–Mathematics interface In social systems ranging from ant colonies to human society, behavioural specialization—consistent individual differences in behaviour—is commonplace: individuals can specialize in the tasks they perform (division of labour (DOL)), the political behaviour they exhibit (political polarization) or the non-task behaviours they exhibit (personalities). Across these contexts, behavioural specialization often co-occurs with modular and assortative social networks, such that individuals tend to associate with others that have the same behavioural specialization. This raises the question of whether a common mechanism could drive co-emergent behavioural specialization and social network structure across contexts. To investigate this question, here we extend a model of self-organized DOL to account for social influence and interaction bias among individuals—social dynamics that have been shown to drive political polarization. We find that these same social dynamics can also drive emergent DOL by forming a feedback loop that reinforces behavioural differences between individuals, a feedback loop that is impacted by group size. Moreover, this feedback loop also results in modular and assortative social network structure, whereby individuals associate strongly with those performing the same task. Our findings suggest that DOL and political polarization—two social phenomena not typically considered together—may actually share a common social mechanism. This mechanism may result in social organization in many contexts beyond task performance and political behaviour. The Royal Society 2020-01 2020-01-08 /pmc/articles/PMC7014790/ /pubmed/31910771 http://dx.doi.org/10.1098/rsif.2019.0564 Text en © 2020 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Life Sciences–Mathematics interface Tokita, Christopher K. Tarnita, Corina E. Social influence and interaction bias can drive emergent behavioural specialization and modular social networks across systems |
title | Social influence and interaction bias can drive emergent behavioural specialization and modular social networks across systems |
title_full | Social influence and interaction bias can drive emergent behavioural specialization and modular social networks across systems |
title_fullStr | Social influence and interaction bias can drive emergent behavioural specialization and modular social networks across systems |
title_full_unstemmed | Social influence and interaction bias can drive emergent behavioural specialization and modular social networks across systems |
title_short | Social influence and interaction bias can drive emergent behavioural specialization and modular social networks across systems |
title_sort | social influence and interaction bias can drive emergent behavioural specialization and modular social networks across systems |
topic | Life Sciences–Mathematics interface |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7014790/ https://www.ncbi.nlm.nih.gov/pubmed/31910771 http://dx.doi.org/10.1098/rsif.2019.0564 |
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