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Observational Learning with Position Uncertainty

Author

Listed:
  • 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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    Cited by:

    1. 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.
    2. Simon Board & Moritz Meyer‐ter‐Vehn, 2021. "Learning Dynamics in Social Networks," Econometrica, Econometric Society, vol. 89(6), pages 2601-2635, November.
    3. Parakhonyak, Alexei & Vikander, Nick, 2023. "Information design through scarcity and social learning," Journal of Economic Theory, Elsevier, vol. 207(C).
    4. Germano, Fabrizio & Sobbrio, Francesco, 2020. "Opinion dynamics via search engines (and other algorithmic gatekeepers)," Journal of Public Economics, Elsevier, vol. 187(C).
    5. Monzón, Ignacio, 2019. "Observational learning in large anonymous games," Theoretical Economics, Econometric Society, vol. 14(2), May.
    6. Ignacio Monzón, 2017. "Aggregate Uncertainty Can Lead to Incorrect Herds," American Economic Journal: Microeconomics, American Economic Association, vol. 9(2), pages 295-314, May.
    7. Antonio Guarino & Philippe Jehiel, 2013. "Social Learning with Coarse Inference," American Economic Journal: Microeconomics, American Economic Association, vol. 5(1), pages 147-174, February.
    8. Alexei Parakhonyak & Nick Vikander, 2016. "Inducing Herding with Capacity Constraints," Economics Series Working Papers 808, University of Oxford, Department of Economics.
    9. Daniel Garcia & Sandro Shelegia, 2018. "Consumer search with observational learning," RAND Journal of Economics, RAND Corporation, vol. 49(1), pages 224-253, March.
    10. Cavatorta, Elisa & Guarino, Antonio & Huck, Steffen, 2024. "Social learning with partial and aggregate information: Experimental evidence," Games and Economic Behavior, Elsevier, vol. 146(C), pages 292-307.
    11. 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.
    12. Bahar, Gal & Arieli, Itai & Smorodinsky, Rann & Tennenholtz, Moshe, 2020. "Multi-issue social learning," Mathematical Social Sciences, Elsevier, vol. 104(C), pages 29-39.
    13. 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.
    14. 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

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    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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