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Non-Bayesian Social Learning, Third Version

  • Ali Jadbabaie

    ()

    (Department of Electrical and Systems Engineering, University of Pennsylvania)

  • Pooya Molavi

    ()

    (Department of Electrical and Systems Engineering, University of Pennsylvania)

  • Alvaro Sandroni

    ()

    (MEDS, Kellogg School of Management, Northwestern University)

  • Alireza Tahbaz-Salehi

    ()

    (Decisions, Risk and Operations Divsion, Columbia University)

We develop a dynamic model of opinion formation in social networks when the information required for learning a payoff-relevant parameter may not be at the disposal of any single agent. Individuals engage in communication with their neighbors in order to learn from their experiences. However, instead of incorporating the views of their neighbors in a fully Bayesian manner, agents use a simple updating rule which linearly combines their personal experience and the views of their neighbors (even though the neighbors’ views may be quite inaccurate). This non-Bayesian learning rule is motivated by the formidable complexity required to fully implement Bayesian updating in networks. We show that, as long as individuals take their personal signals into account in a Bayesian way, repeated interactions lead them to successfully aggregate information and learn the true underlying state of the world. This result holds in spite of the apparent na¨ıvet´e of agents’ updating rule, the agents’ need for information from sources the existence of which they may not be aware of, the possibility that the most persuasive agents in the network are precisely those least informed and with worst prior views, and the assumption that no agent can tell whether her own views or those of her neighbors are more accurate.

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File URL: http://economics.sas.upenn.edu/system/files/11-025.pdf
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Paper provided by Penn Institute for Economic Research, Department of Economics, University of Pennsylvania in its series PIER Working Paper Archive with number 11-025.

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Length: 25 pages
Date of creation: 01 Jun 2009
Date of revision: 05 Aug 2011
Handle: RePEc:pen:papers:11-025
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  1. Larry G. Epstein & Jawwad Noor & Alvaro Sandroni, 2005. "Non-Bayesian Updating: A Theoretical Framework," Boston University - Department of Economics - Working Papers Series WP2005-025, Boston University - Department of Economics.
  2. Allison, G. & Fudenberg, D., 1992. "Rules of Thumb for Social Learning," Working papers 92-12, Massachusetts Institute of Technology (MIT), Department of Economics.
  3. Smith, L. & Sorensen, P., 1996. "Pathological Outcomes of Observational Learning," Working papers 96-19, Massachusetts Institute of Technology (MIT), Department of Economics.
  4. Kalai, Ehud & Lehrer, Ehud, 1994. "Weak and strong merging of opinions," Journal of Mathematical Economics, Elsevier, vol. 23(1), pages 73-86, January.
  5. Epstein Larry G & Noor Jawwad & Sandroni Alvaro, 2010. "Non-Bayesian Learning," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 10(1), pages 1-20, January.
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