Learning From Reviews: The Selection Effect and the Speed of Learning
Name
Econometrica - 2022 - Acemoglu - Learning From Reviews The Selection Effect and the Speed of Learning.pdf
Size
475.81 KB
Format
Adobe PDF
Checksum (MD5)
4f08bf0f995b2f3ae656185fca193d3e
Author(s)
Acemoglu, Daron
Makhdoumi, Ali
Malekian, Azarakhsh
Ozdaglar, Asuman
Date Issued
2022
Publisher
The Econometric Society
Citation
Acemoglu, Daron, Makhdoumi, Ali, Malekian, Azarakhsh and Ozdaglar, Asuman. 2022. "Learning From Reviews: The Selection Effect and the Speed of Learning." 90 (6).
Version
Final published version
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.
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.
Subjects
Economics and Econometrics
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Economics
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3982/ecta15847
