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Econometrica Vol. 90 No. 6 2022

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

Daron Acemoglu1; Ali Makhdoumi2; Azarakhsh Malekian3; Asuman Ozdaglar4

1 Department of Economics, Massachusetts Institute of Technology · 2 Fuqua School of Business, Duke University , · 3 Rotman School of Management, University of Toronto · 4 Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

open access

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.

DOI
10.3982/ecta15847
Volume
90
Issue
6
Pages
2857-2899
Language
en
Sources
bibtex:phds-export.bib openalex crossref

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