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Observational learning with position uncertainty

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  • Monzón, Ignacio
  • Rapp, Michael

Abstract

Observational learning is typically examined when agents have precise information about their position in the sequence of play. We present a model in which agents are uncertain about their positions. Agents sample the decisions of past individuals and receive a private signal about the state of the world. We show that social learning is robust to position uncertainty. Under any sampling rule satisfying a stationarity assumption, learning is complete if signal strength is unbounded. In cases with bounded signal strength, we provide a lower bound on information aggregation: individuals do at least as well as an agent with the strongest signal realizations would do in isolation. Finally, we show in a simple environment that position uncertainty slows down learning but not to a great extent.

Suggested Citation

  • Monzón, Ignacio & Rapp, Michael, 2014. "Observational learning with position uncertainty," Journal of Economic Theory, Elsevier, vol. 154(C), pages 375-402.
  • Handle: RePEc:eee:jetheo:v:154:y:2014:i:c:p:375-402
    DOI: 10.1016/j.jet.2014.09.012
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    Citations

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    Cited by:

    1. Ignacio Monzón, 2017. "Aggregate Uncertainty Can Lead to Incorrect Herds," American Economic Journal: Microeconomics, American Economic Association, vol. 9(2), pages 295-314, May.
    2. 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 Mar 2018.
    3. repec:bla:randje:v:49:y:2018:i:1:p:224-253 is not listed on IDEAS
    4. Antonio Guarino & Philippe Jehiel, 2013. "Social Learning with Coarse Inference," American Economic Journal: Microeconomics, American Economic Association, vol. 5(1), pages 147-174, February.
    5. Andrea Gallice & Ignacio Monzon, 2016. "Cooperation in Social Dilemmas through Position Uncertainty," Carlo Alberto Notebooks 493, Collegio Carlo Alberto.
    6. Alexei Parakhonyak & Nick Vikander, 2016. "Inducing Herding with Capacity Constraints," Economics Series Working Papers 808, University of Oxford, Department of Economics.
    7. Daniel Garcia & Sandro Shelegia, 2018. "Consumer search with observational learning," RAND Journal of Economics, RAND Corporation, vol. 49(1), pages 224-253, March.

    More about this item

    Keywords

    Social learning; Complete learning; Information aggregation; Herds; Position uncertainty; Observational learning;

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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