AI

Learning Prices for Repeated Auctions with Strategic Buyers

Abstract

Inspired by real-time ad exchanges for online display advertising, we consider the problem of inferring a buyer’s value for a good when the buyer is repeatedly interacting with the seller through a posted-price mechanism. We model the buyer as a strategic agent, interested in maximizing her long-term surplus, and are interested in optimizing seller revenue. We show conditions under which the seller cannot hope to gain an advantage by learning the buyer’s value – i.e. the buyer can always manipulate the exchange to hide her value. This result is accompanied by a seller algorithm that is able to achieve no-regret when the buyer is unable to incur the short-term costs of such manipulation.