Privacy, Personalization, and Price Discrimination
研究买方对多产品卖家有私人估值时的双边贸易，提出激励相容的市场细分概念，刻画买方最优细分，并用于分析零售平台搜索界面的粗糙度设计，关联消费者隐私与在线零售中的产品引导和价格歧视争论。
Abstract We study a bilateral trade setting in which a buyer has private valuations over a multi-product seller’s inventory. We introduce the notion of an incentive-compatible market segmentation (IC-MS)—a market segmentation compatible with the buyer’s incentives to voluntarily reveal their preferences. Our main result is a characterization of the buyer-optimal IC-MS. It is partially revealing, comprised primarily of pooling segments wide enough to keep prices low but narrow enough to ensure trade over relevant products. We use our results to study a novel design problem in which a retail platform seeks to attract consumers by calibrating the coarseness of its search interface. Our analysis speaks directly to consumer privacy and the debate regarding product steering versus price discrimination in online retail.