Dynamic pricing with Bayesian updates from online reviews

As a key part of modern online platforms, online decision-making plays a crucial role in a variety of settings, particularly related to the Internet. Two landmark examples that have been widely studied are dynamic pricing and online reviews. Online review systems constitute powerful platforms for users to get informed about the product and for the firm to understand how a given market is receiving the product. The study of these systems has been vast for the last two decades [6, 10], and more recently, modeling simple like/dislike reviews as bandits problems have become standard [1, 2, 3, 13, 16, 18]. Dynamic pricing, on the other hand, is an active area of research in economics, computer science, and operations research [12, 14], and has become a common practice in several industries such as transportation and retail. There has been a growing interest in combining the two areas as a way to design more effective pricing mechanisms that gather information from current reviews to update prices and make the product more attractive [5, 11, 17]. In particular, [5] considers social learning with non-Bayesian agents in a market with like & dislike reviews, and the resulting pricing decision of a monopolist.