Pricing and Positioning of Horizontally Differentiated Products with Incomplete Demand Information
研究了在顾客偏好和需求分布未知时，如何通过数据驱动算法学习最优定价和产品配置，并证明该算法在长期内接近最优。
How to Manage Horizontally Differentiated Products When Customer Preferences and Demand Distributions Are Unknown In the paper “Pricing and Positioning of Horizontally Differentiated Products with Incomplete Demand Information,” we consider the problem of determining the optimal prices and product configurations of horizontally differentiated products when customers purchase according to a locational choice model and where the problem parameters are initially unknown to the decision maker. We propose a data-driven algorithm that learns the optimal prices and product configurations from accumulating sales data, and we show that their regret—the expected cumulative loss caused by not using optimal decisions—after T time periods is [Formula: see text]. We accompany this result by proving an almost-matching lower bound of regret, implying that our algorithms are asymptotically near optimal. In an extension, we show how our algorithm can be adapted for the case of fixed locations.