Title: Collaborative Learning and Decision-Making on Pricing and Recommendation: A Simple Framework for Planning.
Abstract:
We formulate a collaborative learning and decision-making problem involving contextual information. In current business practices, pricing and recommendation decisions often are made jointly by multiple teams in sequence. The decision-making processes for different teams can be controlled by either a centralized or decentralized planner. We propose a simple collaboration framework that integrates the learning about decision making in an unknown environment. The main challenge in a decentralized framework is that the decision-making process in other teams is unknown, but the subsequent decisions are mutually dependent. From a practical concern about high exploration costs and implementation complexity, we propose a simple greedy algorithm for centralized planners and a "greedy" + "weighted sampling" (GWS) algorithm for both centralized and decentralized planners to balance the learning and earning. We show that the exploration-free greedy algorithm can achieve the optimal rate when context diversity holds. The GWS algorithm works effectively for either centralized or decentralized planners under a much weaker condition, which we call context variation. Furthermore, we extend our framework to the multi-product pricing and ranking problem and study the model misspecification issue. We validate our results using simulations on synthetic and real data. Numerical studies show the superior performance of the two proposed frameworks for different types of planners.
Biography:
Junyu Cao is an Assistant Professor in the Department of Information, Risk, and Operations Management at the McCombs School of Business, The University of Texas at Austin. She earned her Ph.D. in Industrial Engineering and Operations Research from the University of California, Berkeley in 2020. Her research focuses on AI-driven decision making, including sequential decision processes, human-AI collaboration, and trustworthy decision systems, as well as smart-city analytics and urban operations with applications in logistics, transportation, and autonomous mobility. Her work has been recognized with multiple competitive research awards, including winner of the INFORMS ISS Cluster Best Paper Award, Second Place in the POMS College of Healthcare Operations Management Best Paper Competition, and finalist honors in Service Science, CSAMSE, and MSOM Best Paper competitions.
