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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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    Cited by:

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    2. Antonio Guarino & Philippe Jehiel, 2013. "Social Learning with Coarse Inference," American Economic Journal: Microeconomics, American Economic Association, vol. 5(1), pages 147-174, February.
    3. Germano, Fabrizio & Sobbrio, Francesco, 2020. "Opinion dynamics via search engines (and other algorithmic gatekeepers)," Journal of Public Economics, Elsevier, vol. 187(C).
    4. Ignacio Monzón, 2017. "Aggregate Uncertainty Can Lead to Incorrect Herds," American Economic Journal: Microeconomics, American Economic Association, vol. 9(2), pages 295-314, May.
    5. Monzón, Ignacio, 2019. "Observational learning in large anonymous games," Theoretical Economics, Econometric Society, vol. 14(2), May.
    6. Sushil Bikhchandani & David Hirshleifer & Omer Tamuz & Ivo Welch, 2024. "Information Cascades and Social Learning," Journal of Economic Literature, American Economic Association, vol. 62(3), pages 1040-1093, September.
    7. Hu, Ju, 2020. "On the existence of the ex post symmetric random entry model," Journal of Mathematical Economics, Elsevier, vol. 90(C), pages 42-47.
    8. Bahar, Gal & Arieli, Itai & Smorodinsky, Rann & Tennenholtz, Moshe, 2020. "Multi-issue social learning," Mathematical Social Sciences, Elsevier, vol. 104(C), pages 29-39.
    9. Andrea Gallice & Ignacio Monzón, 2019. "Co-operation in Social Dilemmas Through Position Uncertainty," The Economic Journal, Royal Economic Society, vol. 129(621), pages 2137-2154.
    10. Alexei Parakhonyak & Nick Vikander, 2016. "Inducing Herding with Capacity Constraints," Economics Series Working Papers 808, University of Oxford, Department of Economics.
    11. Simon Board & Moritz Meyer‐ter‐Vehn, 2021. "Learning Dynamics in Social Networks," Econometrica, Econometric Society, vol. 89(6), pages 2601-2635, November.
    12. Daniel Garcia & Sandro Shelegia, 2018. "Consumer search with observational learning," RAND Journal of Economics, RAND Corporation, vol. 49(1), pages 224-253, March.
    13. Parakhonyak, Alexei & Vikander, Nick, 2023. "Information design through scarcity and social learning," Journal of Economic Theory, Elsevier, vol. 207(C).

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    More about this item

    Keywords

    social learning; information aggregation; herds; position uncertainty; observational learning;
    All these keywords.

    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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