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Threshold Learning Dynamics in Social Networks

Social learning is defined as the ability of a population to aggregate information, a process which must crucially depend on the mechanisms of social interaction. Consumers choosing which product to buy, or voters deciding which option to take with respect to an important issue, typically confront e...

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Autores principales: González-Avella, Juan Carlos, Eguíluz, Victor M., Marsili, Matteo, Vega-Redondo, Fernado, San Miguel, Maxi
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3103531/
https://www.ncbi.nlm.nih.gov/pubmed/21637714
http://dx.doi.org/10.1371/journal.pone.0020207
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author González-Avella, Juan Carlos
Eguíluz, Victor M.
Marsili, Matteo
Vega-Redondo, Fernado
San Miguel, Maxi
author_facet González-Avella, Juan Carlos
Eguíluz, Victor M.
Marsili, Matteo
Vega-Redondo, Fernado
San Miguel, Maxi
author_sort González-Avella, Juan Carlos
collection PubMed
description Social learning is defined as the ability of a population to aggregate information, a process which must crucially depend on the mechanisms of social interaction. Consumers choosing which product to buy, or voters deciding which option to take with respect to an important issue, typically confront external signals to the information gathered from their contacts. Economic models typically predict that correct social learning occurs in large populations unless some individuals display unbounded influence. We challenge this conclusion by showing that an intuitive threshold process of individual adjustment does not always lead to such social learning. We find, specifically, that three generic regimes exist separated by sharp discontinuous transitions. And only in one of them, where the threshold is within a suitable intermediate range, the population learns the correct information. In the other two, where the threshold is either too high or too low, the system either freezes or enters into persistent flux, respectively. These regimes are generally observed in different social networks (both complex or regular), but limited interaction is found to promote correct learning by enlarging the parameter region where it occurs.
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spelling pubmed-31035312011-06-02 Threshold Learning Dynamics in Social Networks González-Avella, Juan Carlos Eguíluz, Victor M. Marsili, Matteo Vega-Redondo, Fernado San Miguel, Maxi PLoS One Research Article Social learning is defined as the ability of a population to aggregate information, a process which must crucially depend on the mechanisms of social interaction. Consumers choosing which product to buy, or voters deciding which option to take with respect to an important issue, typically confront external signals to the information gathered from their contacts. Economic models typically predict that correct social learning occurs in large populations unless some individuals display unbounded influence. We challenge this conclusion by showing that an intuitive threshold process of individual adjustment does not always lead to such social learning. We find, specifically, that three generic regimes exist separated by sharp discontinuous transitions. And only in one of them, where the threshold is within a suitable intermediate range, the population learns the correct information. In the other two, where the threshold is either too high or too low, the system either freezes or enters into persistent flux, respectively. These regimes are generally observed in different social networks (both complex or regular), but limited interaction is found to promote correct learning by enlarging the parameter region where it occurs. Public Library of Science 2011-05-27 /pmc/articles/PMC3103531/ /pubmed/21637714 http://dx.doi.org/10.1371/journal.pone.0020207 Text en Gonzalez et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
González-Avella, Juan Carlos
Eguíluz, Victor M.
Marsili, Matteo
Vega-Redondo, Fernado
San Miguel, Maxi
Threshold Learning Dynamics in Social Networks
title Threshold Learning Dynamics in Social Networks
title_full Threshold Learning Dynamics in Social Networks
title_fullStr Threshold Learning Dynamics in Social Networks
title_full_unstemmed Threshold Learning Dynamics in Social Networks
title_short Threshold Learning Dynamics in Social Networks
title_sort threshold learning dynamics in social networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3103531/
https://www.ncbi.nlm.nih.gov/pubmed/21637714
http://dx.doi.org/10.1371/journal.pone.0020207
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