Selling Quality-Differentiated Products in a Markovian Market with Unknown Transition Probabilities
研究当顾客对质量的偏好随时间变化时，如何对垂直差异化产品线进行动态定价，并开发了一种有界学习策略，在马尔可夫需求环境中实现接近最优的数据驱动学习。
How to Price a Product Line When Customer Preferences Change over Time Quality-differentiated products can help sellers increase their profits through market segmentation. However, in many business applications, such as online search and consumer lending, customer preferences evolve over time, making it difficult for sellers to use market segmentation. In their study “Selling Quality-Differentiated Products in a Markovian Market with Unknown Transition Probabilities,” Keskin and Li analyze dynamic pricing of a vertically differentiated product line when customer preferences for quality can shift over time. Keskin and Li show that data-driven learning is essential when operating in a changing market with unknown customer heterogeneity. Keskin and Li also develop a bounded learning policy that implements near-optimal data-driven learning in a Markov-modulated demand environment.