日期：2026年7月26日

地点：东校区财科馆317

【讲座一】

时间：09:00 - 10:00

题目：Dynamic Pricing and Product Quality with Review–Driven Learning

主讲人：Prof. Sandun Perera

摘要：

Online product reviews play a pivotal role in empowering consumers by reducing uncertainty about product attributes, a phenomenon widely studied from the demand perspective. However, the supply-side implications remain less understood—particularly how firms can leverage consumer reviews to develop an integrated pricing–quality strategy. To address this gap, we examine a firm’s dynamic pricing and product-quality decisions over two selling periods, where consumer reviews play a central role in shaping the market’s perception of a new experience good. Both the firm and its consumers are initially uncertain about the product’s perceived (market-based) quality and rely on early reviews to update their beliefs. Our analysis identifies two critical review metrics—volume and valence—as jointly shaping the firm’s optimal strategy. Review volume reflects initial sales, while valence measures the average rating. Together, these metrics generate what we call the learning precision effect, whereby review information enhances the accuracy of quality inference and, in turn, guides the firm’s dynamic pricing and quality decisions. We find that, without the option to refine (adjust) quality after launch, the firm prefers a higher initial product quality and a lower introductory price to boost review volume and improve learning. When post-launch quality refinement is feasible, the learning precision effect intensifies, as the firm further increases initial quality to enhance learning from reviews. However, the resulting optimal pricing strategy in the first period can depart from conventional intuition. Depending on market conditions, the firm may either raise or lower the introductory price—relative to the case without quality refinement—in order to enhance review generation. A notable outcome is that the firm’s optimal strategy not only increases its overall profit but also improves consumer surplus for both early and late buyers. Although quality refinement is often expected to favor the firm at consumers’ expense, our results show that learning from reviews can generate a genuine win–win outcome. Finally, we extend our model to incorporate under-reporting bias, uninformed consumers, nonzero marginal quality costs, and alternative distributions of perceived quality, and find that our main insights remain robust across these variations.

主讲人简介：

Sandun Perera is a Professor of Business Analytics, Supply Chain, and AI in the College of Business at the University of Nevada, Reno. He received his Ph.D. in Operations Management, MBA, and M.S. in Supply Chain Management from the Jindal School of Management at the University of Texas at Dallas, and also holds a Ph.D. in Financial Mathematics and M.S. degrees in Statistics and Applied Mathematics from Florida Atlantic University. He earned his B.S. in Finance, Business, and Computational Mathematics with first-class honors from the University of Colombo, Sri Lanka. His research spans Supply Chain Management, Disruptive Technologies in Operations Management, Healthcare Operations Management, and the interfaces between Operations and other functional business areas. Sandun has collaborated with multinational technology firms, retailers, hospitals, blood banks, the EV and battery manufacturing sector, and pharmaceutical companies. His work has appeared in journals on the Financial Times Top 50 and UTD Top 24 lists, as well as in more than 40 publications in A/A*-rated ABDC journals. He serves as a Senior Editor for Production and Operations Management, Department Editor for IEEE Transactions on Engineering Management, and Associate Editor for OMEGA, Decision Sciences, and Transportation Research Part E. He is also the Executive Director for Initiatives and Outreach of the Human-Centered AI Society and holds key leadership roles within the Production and Operations Management Society (POMS), where he is the Program Chair for the POMS Annual Conference 2026 and Vice President of Membership.

【讲座二】

时间：10:00 - 11:00

题目：Tails of Two Sides---Conditions Inspired by Thompson Sampling for Multi-armed Bandit that Lead to Further Understandings and Improvements.

主讲人：Prof. Jian Yang

摘要：

We may understand a policy for the multi-armed bandit (MAB) problem as using a contest between arm-specific indices to select the arm to be pulled in each period. Any policy is identifiable by its coefficients that help to form the indices, with Upper confidence bound (UCB), as well as Thompson sampling with Gaussian priors (TSg) and Beta priors (TSb) being no exceptions. Inspired by the latter two, we formulate conditions on the coefficients that lead to tight regret bounds. These conditions basically propound an llrr principle: left tails should be light and right tails should be of the right size. They also guide us to pay particular attention to asymmetric Gaussian-indexed policies AGI(sigma_l,sigma_r) whose coefficients take the form -sigma_l Z^- + sigma_r Z^+, with AGI(1,1) being just TSg. The latter's difference from the empirically more prominent TSb prompts us to generalize AGI further to AAGI policies that allow the magnitudes of index oscillations to depend on the various arms' potential rewards. Their good performances can be explained by another condition concerning a leading arm. Numerical experiments confirm that these policies, especially those with asymmetric tails, % and differentiating treatments of potential rewards, can perform well enough to beat TSb by large margins.  

主讲人简介：Dr. Jian Yang obtained his Ph.D. in Management Science from the University of Texas at Austin.  After working for the Department of Mechanical and Industrial Engineering at New Jersey Institute of Technology, he is now a professor at the Department of Management Science and Information Systems, Rutgers Business School, Rutgers University.  Dr. Yang’s research interests are in combinatorial optimization, logistics, production and inventory control, dynamic pricing, and game theory.  At the present he is particularly interested in the role played by risk and ambiguity in dynamic inventory-price control and game-theoretical settings.