报告内容摘要：

Dynamic pricing with demand learning refers to the profit maximization problem where one optimizes the profit by choosing a price and learns the demand at the same time. Traditionally, the cost is fixed and the problem may be formulated as a multi armed bandit problem, which is known to have an O(logT) lower bound on the expected regret, where T is the number of periods. In this paper, we consider the case where the cost changes over periods. Then, the optimal pricing decision becomes a function of the cost. We propose an upper-confidence-bound type of algorithm to solve the problem. When the cost is a continuous random variable, we prove that the expected regret of our proposed algorithm is O((logT)2). When the cost is discrete, surprisingly, we find that the expected regret may be bounded by a constant. This is a joint work with Ying Zhong and Guangwu Liu.
 
报告人简介：

Prof. Jeff Hong is an Endowed Chair Professor of Management Sciences with a joint appointment with Department of Management Sciences and Department of Economics and Finance in College of Business at City University of Hong Kong. Before joining City University, he was a Professor in Department of Industrial Engineering and Logistics Management at the Hong Kong University of Science and Technology (HKUST). Prof. Hong’s research interests include stochastic simulation and optimization, financial engineering, and business analytics.

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