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Behavioural patterns in social networks


  • Anna Contea

    (Max Planck Institute of Economics, Jena)

  • Daniela T. Di Cagno

    (LUISS University, Rome)

  • Emanuela Sciubbad

    (Birkbeck College, University of London)


In this paper, we focus on the analysis of individual decision making for the formation of social networks, using experimentally generated data. We first analyse the determinants of the individual demand for links under the assumption of agents' static expectations. The results of this exercise subsequently allow us to identify patterns of behaviour that can be subsumed in three strategies of link formation: 1) reciprocator strategy - players propose links to those from whom they have received link proposals in the previous round; 2) myopic best response strategy - players aim to profit from maximisation; 3) opportunistic strategy - players reciprocate link proposals to those who have the largest number of connections. We find that these strategies explain approximately 76% of the observed choices. We finally estimate a mixture model to highlight the proportion of the population who adopt each of these strategies.

Suggested Citation

  • Anna Contea & Daniela T. Di Cagno & Emanuela Sciubbad, 2011. "Behavioural patterns in social networks," Jena Economic Research Papers 2011-060, Friedrich-Schiller-University Jena.
  • Handle: RePEc:jrp:jrpwrp:2011-060

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    References listed on IDEAS

    1. Falk Armin & Kosfeld Michael, 2012. "It's all about Connections: Evidence on Network Formation," Review of Network Economics, De Gruyter, vol. 11(3), pages 1-36, September.
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    3. Dutta, Bhaskar & Ghosal, Sayantan & Ray, Debraj, 2005. "Farsighted network formation," Journal of Economic Theory, Elsevier, vol. 122(2), pages 143-164, June.
    4. Jackson, Matthew O. & Wolinsky, Asher, 1996. "A Strategic Model of Social and Economic Networks," Journal of Economic Theory, Elsevier, vol. 71(1), pages 44-74, October.
    5. Berninghaus, Siegfried K. & Ehrhart, Karl-Martin & Ott, Marion, 2008. "Myopically Forward-Looking Agents in a Network Formation Game: Theory and Experimental Evidence," Sonderforschungsbereich 504 Publications 08-02, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    6. Anna Conte & M. Levati, 2014. "Use of data on planned contributions and stated beliefs in the measurement of social preferences," Theory and Decision, Springer, vol. 76(2), pages 201-223, February.
    7. Goeree, Jacob K. & Riedl, Arno & Ule, Aljaz, 2009. "In search of stars: Network formation among heterogeneous agents," Games and Economic Behavior, Elsevier, vol. 67(2), pages 445-466, November.
    8. Herings, P. Jean-Jacques & Mauleon, Ana & Vannetelbosch, Vincent, 2009. "Farsightedly stable networks," Games and Economic Behavior, Elsevier, vol. 67(2), pages 526-541, November.
    9. Cary Deck & Cathleen Johnson, 2004. "Link bidding in laboratory networks," Review of Economic Design, Springer;Society for Economic Design, vol. 8(4), pages 359-372, April.
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    12. Sanjeev Goyal & Sumit Joshi, 2006. "Unequal connections," International Journal of Game Theory, Springer;Game Theory Society, vol. 34(3), pages 319-349, October.
    13. Matteo Galizzi & Michele Bernasconi, 2005. "Coordination in Networks Formation: Experimental Evidence on Learning and Salience," Working Papers 2005.107, Fondazione Eni Enrico Mattei.
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    Cited by:

    1. Wirth, Herbert & Kulczycka, Joanna & Hausner, Jerzy & Koński, Maciej, 2016. "Corporate Social Responsibility: Communication about social and environmental disclosure by large and small copper mining companies," Resources Policy, Elsevier, vol. 49(C), pages 53-60.
    2. Daniela Di Cagno & Arianna Galliera & Werner Güth & Noemi Pace, 2017. "Behavioral Patterns and Reduction of Sub-Optimality: An Experimental Choice Analysis," Working Papers 2017:23, Department of Economics, University of Venice "Ca' Foscari".

    More about this item


    Network formation; Experiments; Multivariate probit models; Mixture models;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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