Speaker: Yining Wang University of Texas at Dallas
Time: 2024-05-29 10:45-2024-05-29 12:00
Venue: Tencent Meeting ID: 314-483-507, code: 2905

Abstract:

This paper introduces a novel contextual bandit algorithm for personalized pricing under utility fairness constraints in scenarios with uncertain demand, achieving an optimal regret upper bound. Our approach, which incorporates dynamic pricing and demand learning, addresses the critical challenge of fairness in pricing strategies. We first delve into the static full-information setting to formulate an optimal pricing policy as a constrained optimization problem. Here, we propose an approximation algorithm for efficiently and approximately computing the ideal policy.
We also use mathematical analysis and computational studies to characterize the structures of optimal contextual pricing policies subject to fairness constraints, deriving simplified policies which lays the foundations of more in-depth research and extensions.
Further, we extend our study to dynamic pricing problems with demand learning, establishing a non-standard regret lower bound that highlights the complexity added by fairness constraints. Our research offers a comprehensive analysis of the cost of fairness and its impact on the balance between utility and revenue maximization. This work represents a step towards integrating ethical considerations into algorithmic efficiency in data-driven dynamic pricing.

Short Bio:

Dr.Yining Wang is an associate professor of operations management at the Naveen Jindal School of Management of University of Texas at Dallas. Before joining UTD, he was an assistant professor of information systems and operations management at the Warrington College of Business of University of Florida. He obtained his PhD in Machine Learning at Carnegie Mellon University, advised by Aarti Singh. Before coming to CMU, he was an undergraduate student at the Yao Class in Tsinghua University.
He is generally interested in machine learning and its applications in revenue management and information systems research. His main research focus is on the development and analysis of sequential decision making methods under uncertainty, with emphasis to revenue management applications such as assortment optimization and dynamic pricing. His research is also connected with bandit online learning and reinforcement learning in machine learning research.