Fast and Slow Learning from Reviews
Fast and Slow Learning from Reviews
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从评论中快速和缓慢地学习
DOI：
10.2139/ssrn.3072495
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
A. Ozdaglar
中科院分区：
文献类型：
--
作者：
D. Acemoglu;A. Makhdoumi;Azarakhsh Malekian;A. Ozdaglar
本文建立了一个基于在线评论的贝叶斯学习模型，并研究了在不同评分系统下，产品质量的渐近学习条件和学习速度。评级系统提供有关以前客户留下的评论的信息。一系列潜在客户决定是否加入该平台。在加入并观察产品的评级后，并以她事先的估价为条件，客户决定是否购买。如果她购买，产品的真实质量、她的事前估价、事后特殊偏好项和产品的价格决定了她的总体满意度。考虑到平台的评级系统，她决定根据她的总体满意度留下评论。我们在两类评级系统下研究学习动态：完整历史，客户可以看到完整的评论历史，以及汇总统计，平台报告过去评论的一些汇总统计。在这两种情况下，学习动态都因选择效应而变得复杂--购买商品的用户类型以及他们的总体满意度和评论取决于他们在购买时所拥有的信息。我们提供了条件下的完整历史和汇总统计量的渐近学习，并显示如何选择效果变得更加难以纠正汇总统计。在渐近学习的条件下，学习的速度（速率）总是指数级的，并且在两种类型的评级系统下受到类似的力量的支配，尽管确切的速率不同。使用这种特性，我们提供了几种不同类型的评级系统下的学习率。我们表明，提供更多的信息并不总是导致更快的学习，但严格更精细的评级系统总是这样做。我们还说明了不同的评级系统，具有相同的偏好分布，可以导致非常快或非常慢的学习速度。
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.
DOI：
10.1007/978-3-319-04268-8
发表时间：
2013
期刊：
Seg Technical Program Expanded Abstracts
影响因子：
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作者：
Carles Padró
通讯作者：
Carles Padró