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Observational Learning with Position Uncertainty

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  • Ignacio Monzon
  • Michael Rapp

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 are allowed to have arbitrary ex-ante beliefs about their positions: they may observe their position perfectly, imperfectly, or not at all. 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 show that agents achieve what we define as constrained efficient learning: individuals do at least as well as the most informed agent would do in isolation.

Suggested Citation

  • Ignacio Monzon & Michael Rapp, 2011. "Observational Learning with Position Uncertainty," Carlo Alberto Notebooks 206, Collegio Carlo Alberto.
  • Handle: RePEc:cca:wpaper:206
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    References listed on IDEAS

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    1. Herrera, Helios & Hörner, Johannes, 2013. "Biased social learning," Games and Economic Behavior, Elsevier, vol. 80(C), pages 131-146.
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    8. Antonio Guarino & Philippe Jehiel, 2013. "Social Learning with Coarse Inference," American Economic Journal: Microeconomics, American Economic Association, vol. 5(1), pages 147-174, February.
    9. Celen, Bogachan & Kariv, Shachar, 2004. "Observational learning under imperfect information," Games and Economic Behavior, Elsevier, vol. 47(1), pages 72-86, April.
    10. Costain James S, 2007. "A Herding Perspective on Global Games and Multiplicity," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 7(1), pages 1-55, June.
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    Citations

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    Cited by:

    1. Antonio Guarino & Philippe Jehiel, 2013. "Social Learning with Coarse Inference," American Economic Journal: Microeconomics, American Economic Association, vol. 5(1), pages 147-174, February.
    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. Ignacio Monzón, 2012. "Aggregate Uncertainty Can Lead to Herds," Carlo Alberto Notebooks 245, Collegio Carlo Alberto.
    4. repec:aea:aejmic:v:9:y:2017:i:2:p:295-314 is not listed on IDEAS
    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; 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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