Working Paper: NBER ID: w24046
Authors: 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 asymptotic learning of 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. A sequence of potential customers decide whether to join the platform. After joining and observing the ratings of the product, and conditional on her ex ante valuation, a customer decides whether to purchase or not. If she purchases, the true quality of the product, her ex ante valuation, an ex post idiosyncratic preference term and the price of the product determine her overall satisfaction. Given the rating system of the platform, she decides to leave a review as a function of her overall satisfaction. 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 that they have available at the time of their purchase. We provide conditions for asymptotic learning under both full history and summary statistics, and show how the selection effect becomes more difficult to correct for with summary statistics. Conditional on asymptotic learning, the speed (rate) of learning is always exponential and is governed by similar forces under both types of rating systems, though the exact rates differ. Using this characterization, we provide the rate of learning under several different types of rating systems. We show that providing more information does not always lead to faster learning, but strictly finer rating systems always do. We also illustrate how different rating systems, with the same distribution of preferences, can lead to very fast or very slow speeds of learning.
Keywords: Bayesian Learning; Online Reviews; Rating Systems; Consumer Behavior
JEL Codes: C72; D83; L15
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| full history rating systems (N20) | asymptotic learning (C51) |
| customers can accurately compute public beliefs based on entire history of reviews (D83) | correct for selection effect (C34) |
| speed of learning under full history rating systems (C41) | higher revenues for platforms (D26) |
| summary statistics rating systems (C46) | restrictive conditions for asymptotic learning (C62) |
| speed of learning remains exponential under summary statistics (C51) | differs from full history systems (N20) |
| providing more information (Y50) | does not always lead to faster learning (C92) |
| design of rating systems (C52) | affects speed and accuracy of learning about product quality (L15) |