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Threshold Learning Dynamics in Social Networks

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  • Juan Carlos González-Avella
  • Victor M Eguíluz
  • Matteo Marsili
  • Fernado Vega-Redondo
  • Maxi San Miguel

Abstract

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.

Suggested Citation

  • Juan Carlos González-Avella & Victor M Eguíluz & Matteo Marsili & Fernado Vega-Redondo & Maxi San Miguel, 2011. "Threshold Learning Dynamics in Social Networks," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-9, May.
  • Handle: RePEc:plo:pone00:0020207
    DOI: 10.1371/journal.pone.0020207
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    References listed on IDEAS

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    1. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
    2. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, December.
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    Cited by:

    1. Sun, Ruoyan, 2013. "Kinetics of jobs in multi-link cities with migration-driven aggregation process," Economic Modelling, Elsevier, vol. 30(C), pages 36-41.
    2. Haydée Lugo & Maxi San Miguel, 2014. "Learning and coordinating in a multilayer network," Documentos de Trabajo del ICAE 2014-30, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    3. Stefanny Ramirez & Dario Bauso, 2023. "Dynamic Games with Strategic Complements and Large Number of Players," Journal of Optimization Theory and Applications, Springer, vol. 197(1), pages 1-21, April.

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