Markdown Policies for Demand Learning with Forward-Looking Customers
研究在需求不确定且顾客会策略性等待降价时，如何设计降价策略来平衡需求学习和顾客行为，提出接近最优的延迟降价方案。
Markdown Policies for Demand Learning with Forward-Looking Customers Demand uncertainty and forward-looking customer behavior pose substantial challenges for sellers. In “Markdown policies for demand learning with forward-looking customers,” Birge, Chen, and Keskin analyze a markdown pricing problem involving demand model uncertainty and strategic customers. The authors identify that strategic customer behavior creates a strong intertemporal dependence, where early markdowns influence later demand outcomes. They characterize the impact of this intertemporal dependence on demand learning and develop near-optimal policies that judiciously delay markdowns to manage forward-looking customer behavior while ensuring efficient learning.