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Emergence and resilience of social networks : a general theoretical framework

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  • Georges Erhardt
  • Matteo Marsili
  • Fernando Vega-Redondo

Abstract

The paper first introduces a general dynamic setup that embodies abstract formulations of the following two forces: (a) homophily, i.e. the idea that networking is favored by similarity in behavior; (b) conformity, i.e. the pressure towards similar behavior induced by interaction. This general framework is then specialized into three alternative directions, corresponding to different specific manifestations of those two forces. For each such particularization of the general model, we find that (i) sharp transitions, (ii) hysteresis, and (iii) equilibrium multiplicity are salient characteristics of the long-run social dynamics. Since features (i)-(iii) are often reported for a variety of social (network) phenomena where (a)-(b) play an important role, we sugget that the former may indeed be the result of some common mechanism at work that relies on the interplay of the latter.

Suggested Citation

  • Georges Erhardt & Matteo Marsili & Fernando Vega-Redondo, 2007. "Emergence and resilience of social networks : a general theoretical framework," Annals of Economics and Statistics, GENES, issue 86, pages 1-13.
    • Handle: RePEc:adr:anecst:y:2007:i:86:p:1-13
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    File URL: http://www.jstor.org/stable/20079189
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    Cited by:

    1. Di Guilmi, C. & Gallegati, M. & Landini, S. & Stiglitz, J.E., 2020. "An analytical solution for network models with heterogeneous and interacting agents," Journal of Economic Behavior & Organization, Elsevier, vol. 171(C), pages 189-220.
    2. Zakaria Babutsidze, 2012. "Consumer Learning through Interaction: Effects on Aggregate Outcomes," Chapters, in: Guido Buenstorf (ed.), Evolution, Organization and Economic Behavior, chapter 4, Edward Elgar Publishing.
    3. Rainer Andergassen & Franco Nardini & Massimo Ricottilli, 2015. "Emergence and Resilience in a Model of Innovation and Network Formation," Networks and Spatial Economics, Springer, vol. 15(2), pages 293-311, June.
    4. Jianye Liu & Roderic Beaujot & Zenaida Ravanera, 2018. "Measuring the Effects of Stress and Social Networks on the Health of Canadians," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 13(4), pages 891-908, December.
    5. Alex Centeno, 2022. "A Structural Model for Detecting Communities in Networks," Papers 2209.08380, arXiv.org, revised Oct 2022.

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