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Learning From Reviews: The Selection Effect and the Speed of Learning

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  • Daron Acemoglu
  • Ali Makhdoumi
  • Azarakhsh Malekian
  • Asuman Ozdaglar

Abstract

This paper develops a model of Bayesian learning from online reviews and investigates the conditions for learning the quality of a product and the speed of learning under different rating systems. A rating system provides information about reviews left by previous customers. observe the ratings of a product and decide whether to purchase and review it. We study learning dynamics under two classes of rating systems: full history, where customers see the full history of reviews, and summary statistics, where the platform reports some summary statistics of past reviews. In both cases, learning dynamics are complicated by a selection effect—the types of users who purchase the good, and thus their overall satisfaction and reviews depend on the information available at the time of purchase. We provide conditions for complete learning and characterize and compare its speed under full history and summary statistics. We also show that providing more information does not always lead to faster learning, but strictly finer rating systems do.

Suggested Citation

  • Daron Acemoglu & Ali Makhdoumi & Azarakhsh Malekian & Asuman Ozdaglar, 2022. "Learning From Reviews: The Selection Effect and the Speed of Learning," Econometrica, Econometric Society, vol. 90(6), pages 2857-2899, November.
    • Handle: RePEc:wly:emetrp:v:90:y:2022:i:6:p:2857-2899
    DOI: 10.3982/ECTA15847
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    References listed on IDEAS

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    1. Alexander Wolitzky, 2018. "Learning from Others' Outcomes," American Economic Review, American Economic Association, vol. 108(10), pages 2763-2801, October.
    2. Yeon-Koo Che & Johannes Hörner, 2018. "Recommender Systems as Mechanisms for Social Learning," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 871-925.
    3. Xavier Vives, 1993. "How Fast do Rational Agents Learn?," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(2), pages 329-347.
    4. Daron Acemoglu & Munther A. Dahleh & Ilan Lobel & Asuman Ozdaglar, 2011. "Bayesian Learning in Social Networks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(4), pages 1201-1236.
    5. Glosten, Lawrence R. & Milgrom, Paul R., 1985. "Bid, ask and transaction prices in a specialist market with heterogeneously informed traders," Journal of Financial Economics, Elsevier, vol. 14(1), pages 71-100, March.
    6. Welch, Ivo, 1992. "Sequential Sales, Learning, and Cascades," Journal of Finance, American Finance Association, vol. 47(2), pages 695-732, June.
    7. Nikhil Vellodi, 2018. "Ratings Design and Barriers to Entry," Working Papers 18-13, NET Institute.
    8. Vives Xavier, 1995. "The Speed of Information Revelation in a Financial Market Mechanism," Journal of Economic Theory, Elsevier, vol. 67(1), pages 178-204, October.
    9. Bohren, Aislinn & Hauser, Daniel, 2017. "Learning with Heterogeneous Misspecified Models: Characterization and Robustness," CEPR Discussion Papers 12036, C.E.P.R. Discussion Papers.
    10. Amador, Manuel & Weill, Pierre-Olivier, 2012. "Learning from private and public observations of othersʼ actions," Journal of Economic Theory, Elsevier, vol. 147(3), pages 910-940.
    11. McLennan, Andrew, 1984. "Price dispersion and incomplete learning in the long run," Journal of Economic Dynamics and Control, Elsevier, vol. 7(3), pages 331-347, September.
    12. Nikhil Vellodi, 2018. "Ratings Design and Barriers to Entry," Working Papers 18-13, NET Institute.
    13. Hongbin Cai & Yuyu Chen & Hanming Fang, 2009. "Observational Learning: Evidence from a Randomized Natural Field Experiment," American Economic Review, American Economic Association, vol. 99(3), pages 864-882, June.
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    Cited by:

    1. Hui, Xiang & Klein, Tobias & Stahl, Konrad, 2022. "Learning from Online Ratings," CEPR Discussion Papers 17006, C.E.P.R. Discussion Papers.
    2. Daron Acemoglu & Asuman Ozdaglar & Sarath Pattathil, 2023. "Learning, Diversity and Adaptation in Changing Environments: The Role of Weak Links," Papers 2305.00474, arXiv.org.
    3. Jose Apesteguia & Miguel A. Ballester, 2023. "The rationalizability of survey responses," Economics Working Papers 1863, Department of Economics and Business, Universitat Pompeu Fabra.
    4. Nicolas Lagios & Pierre-Guillaume Méon, 2023. "A Matter of Taste: The Negative Welfare Effect of Expert Judgments," Working Papers CEB 23-009, ULB -- Universite Libre de Bruxelles.
    5. Jose Apesteguia & Miguel Ángel Ballester, 2023. "The Rationalizability of Survey Responses," Working Papers 1393, Barcelona School of Economics.
    6. David Boto-Garc a & Veronica Leoni, 2023. "Noisy signals: do ratings volatility depend on the length of the consumption span?," Working Papers wp1183, Dipartimento Scienze Economiche, Universita' di Bologna.
    7. Xingjun, Huang & Mao, Zhouhui & Lin, Yun & Shi, Qiuju & Liu, Feng & Zhou, Fuli, 2024. "Sharing or privacy for private electric vehicle charging piles? Evidence from Chongqing," Technological Forecasting and Social Change, Elsevier, vol. 203(C).

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