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Relating size and functionality in human social networks through complexity

Extensive empirical evidence suggests that there is a maximal number of people with whom an individual can maintain stable social relationships (the Dunbar number). We argue that this arises as a consequence of a natural phase transition in the dynamic self-organization among N individuals within a...

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Autores principales: West, B. J., Massari, G. F., Culbreth, G., Failla, R., Bologna, M., Dunbar, R. I. M., Grigolini, P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7414177/
https://www.ncbi.nlm.nih.gov/pubmed/32690712
http://dx.doi.org/10.1073/pnas.2006875117
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author West, B. J.
Massari, G. F.
Culbreth, G.
Failla, R.
Bologna, M.
Dunbar, R. I. M.
Grigolini, P.
author_facet West, B. J.
Massari, G. F.
Culbreth, G.
Failla, R.
Bologna, M.
Dunbar, R. I. M.
Grigolini, P.
author_sort West, B. J.
collection PubMed
description Extensive empirical evidence suggests that there is a maximal number of people with whom an individual can maintain stable social relationships (the Dunbar number). We argue that this arises as a consequence of a natural phase transition in the dynamic self-organization among N individuals within a social system. We present the calculated size dependence of the scaling properties of complex social network models to argue that this collective behavior is an enhanced form of collective intelligence. Direct calculation establishes that the complexity of social networks as measured by their scaling behavior is nonmonotonic, peaking around 150, thereby providing a theoretical basis for the value of the Dunbar number. Thus, we establish a theory-based bridge spanning the gap between sociology and psychology.
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spelling pubmed-74141772020-08-21 Relating size and functionality in human social networks through complexity West, B. J. Massari, G. F. Culbreth, G. Failla, R. Bologna, M. Dunbar, R. I. M. Grigolini, P. Proc Natl Acad Sci U S A Social Sciences Extensive empirical evidence suggests that there is a maximal number of people with whom an individual can maintain stable social relationships (the Dunbar number). We argue that this arises as a consequence of a natural phase transition in the dynamic self-organization among N individuals within a social system. We present the calculated size dependence of the scaling properties of complex social network models to argue that this collective behavior is an enhanced form of collective intelligence. Direct calculation establishes that the complexity of social networks as measured by their scaling behavior is nonmonotonic, peaking around 150, thereby providing a theoretical basis for the value of the Dunbar number. Thus, we establish a theory-based bridge spanning the gap between sociology and psychology. National Academy of Sciences 2020-08-04 2020-07-20 /pmc/articles/PMC7414177/ /pubmed/32690712 http://dx.doi.org/10.1073/pnas.2006875117 Text en Copyright © 2020 the Author(s). Published by PNAS. http://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (http://creativecommons.org/licenses/by/4.0/) .
spellingShingle Social Sciences
West, B. J.
Massari, G. F.
Culbreth, G.
Failla, R.
Bologna, M.
Dunbar, R. I. M.
Grigolini, P.
Relating size and functionality in human social networks through complexity
title Relating size and functionality in human social networks through complexity
title_full Relating size and functionality in human social networks through complexity
title_fullStr Relating size and functionality in human social networks through complexity
title_full_unstemmed Relating size and functionality in human social networks through complexity
title_short Relating size and functionality in human social networks through complexity
title_sort relating size and functionality in human social networks through complexity
topic Social Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7414177/
https://www.ncbi.nlm.nih.gov/pubmed/32690712
http://dx.doi.org/10.1073/pnas.2006875117
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