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A general framework for rational learning in social networks


  • Mueller-Frank, Manuel

    () (Department of Economics, University of Oxford)


This paper provides a formal characterization of the process of rational learning in social networks. Agents receive initial private information and select an action out of a choice set under uncertainty in each of infinitely many periods, observing the history of choices of their neighbors. Choices are made based on a common behavioral rule. Conditions under which rational learning leads to global consensus, local indifference and local disagreement are characterized. In the general setting considered, rational learning can lead to pairs of neighbors selecting different actions once learning ends, while not being indifferent among their choices. The effect of the network structure on the degree of information aggregation and speed of convergence is also considered and an answer to the question of optimal information aggregation in networks provided. The results highlight distinguishing features between properties of Bayesian and non-Bayesian learning in social networks.

Suggested Citation

  • Mueller-Frank, Manuel, 2013. "A general framework for rational learning in social networks," Theoretical Economics, Econometric Society, vol. 8(1), January.
  • Handle: RePEc:the:publsh:1015

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    References listed on IDEAS

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    Cited by:

    1. Marco Pelliccia, 2013. "Ambiguous Networks," Birkbeck Working Papers in Economics and Finance 1303, Birkbeck, Department of Economics, Mathematics & Statistics.
    2. Buechel, Berno & Hellmann, Tim & Klößner, Stefan, 2015. "Opinion dynamics and wisdom under conformity," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 240-257.
    3. Mueller-Frank, Manuel, 2014. "Does one Bayesian make a difference?," Journal of Economic Theory, Elsevier, vol. 154(C), pages 423-452.
    4. Fabrizio Germano & Francesco Sobbrio, 2016. "Opinion dynamics via search engines (and other algorithmic gatekeepers)," Economics Working Papers 1552, Department of Economics and Business, Universitat Pompeu Fabra, revised Apr 2017.
    5. John Barrdear, 2014. "Peering into the mist: social learning over an opaque observation network," Discussion Papers 1409, Centre for Macroeconomics (CFM).
    6. Vivi Alatas & Abhijit Banerjee & Arun G. Chandrasekhar & Rema Hanna & Benjamin A. Olken, 2012. "Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia," NBER Working Papers 18351, National Bureau of Economic Research, Inc.
    7. Christos Mavridis & Nikolas Tsakas, 2017. "Social Capital, Communication Channels and Opinion Formation," University of Cyprus Working Papers in Economics 08-2017, University of Cyprus Department of Economics.
    8. Azomahou, T. & Opolot, D., 2014. "Beliefs dynamics in communication networks," MERIT Working Papers 034, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    9. James C. D. Fisher & John Wooders, 2017. "Interacting information cascades: on the movement of conventions between groups," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 63(1), pages 211-231, January.
    10. Cabrales, Antonio; Gale, Douglas; Gottardi, Piero, 2015. "Financial Contagion in Networks," Economics Working Papers ECO2015/01, European University Institute.
    11. Sanjeev Goyal, 2015. "Networks in Economics: A Perspective on the Literature," Cambridge Working Papers in Economics 1548, Faculty of Economics, University of Cambridge.
    12. Syngjoo Choi & Edoardo Gallo & Shachar Kariv, 2015. "Networks in the laboratory," Cambridge Working Papers in Economics 1551, Faculty of Economics, University of Cambridge.
    13. Mueller-Frank, Manuel & Arieliy, Itai, 2015. "A General Model of Boundedly Rational Observational Learning: Theory and Experiment," IESE Research Papers D/1120, IESE Business School.

    More about this item


    Learning; social networks; common knowledge; consensus; speed of convergence; optimal information aggregation;

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation


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