Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
成果类型：
Review; Early Access
署名作者：
Guo, Wenshuo; Haghtalab, Nika; Kandasamy, Kirthevasan; Vitercik, Ellen
署名单位：
University of California System; University of California Berkeley; University of Wisconsin System; University of Wisconsin Madison; Stanford University
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.2023.0447
发表日期：
2026
关键词：
摘要：
In online marketplaces, buyers often use reviews from other customers that share their type-such as height for clothing and skin type for skincare products-to estimate their values. Customers with few relevant reviews may hesitate to purchase except at a low price, so for the seller, there is a tension between setting high prices and ensuring there are enough reviews so buyers can confidently estimate their values. Simultaneously, sellers may use reviews to gauge the demand for items they wish to sell. We study this pricing problem in an online setting in which the seller interacts with a set of buyers of finitely many types, one by one. At each round, the seller sets a price. A buyer arrives and examines the reviews of previous buyers of the same type. Based on the reviews, the buyer decides to purchase if the buyer believes the buyer's ex ante utility is positive. The seller does not know the buyer's type when setting the price or the distribution over types. We provide a no-regret algorithm that the seller can use to obtain high revenue with matching lower bounds.