Title: Post-estimation Adjustments in Data-driven Decision-making with Applications in Pricing
标题： 数据驱动决策的后估计调整及其在定价中的应用
Speaker：Ningyuan Chen – University of Toronto
时间：2025年10月28日下午14：30 中国科大东区管理科研楼第四教室
Abstract: The predict-then-optimize (PTO) framework is a standard approach in data-driven decision-making, where a decision-maker first estimates an unknown parameter from historical data and then uses this estimate to solve an optimization problem. While widely used for its simplicity and modularity, PTO can lead to suboptimal decisions because the estimation step does not account for the structure of the downstream optimization problem. We study a class of problems where the objective function, evaluated at the PTO decision, is asymmetric with respect to estimation errors. This asymmetry causes the expected outcome to be systematically degraded by noise in the parameter estimate, as the penalty for underestimation differs from that of overestimation. To address this, we develop a data-driven post-estimation adjustment that improves decision quality while preserving the practicality and modularity of PTO. We show that when the objective function satisfies a particular curvature condition, based on the ratio of its third and second derivatives, the adjustment simplifies to a closed-form expression. This condition holds for a broad range of pricing problems, including those with linear, log-linear, and power-law demand models. Under this condition, we establish theoretical guarantees that our adjustment uniformly and asymptotically outperforms standard PTO, and we precisely characterize the resulting improvement. Additionally, we extend our framework to multi-parameter optimization and settings with biased estimators. Numerical experiments demonstrate that our method consistently improves revenue, particularly in small-sample regimes where estimation uncertainty is most pronounced. This makes our approach especially well-suited for pricing new products or in settings with limited historical price variation.
Bio: Dr. Ningyuan Chen is an Associate Professor at the Department of Management, University of Toronto, Mississauga, and the Rotman School of Management, University of Toronto. Previously, he held positions as an Assistant Professor at the Hong Kong University of Science and Technology and as a Postdoctoral Fellow at the Yale School of Management. He earned his Ph.D. in Industrial Engineering and Operations Research (IEOR) from Columbia University in 2015. His research focuses on machine learning and data-driven decision-making, with applications in dynamic pricing, revenue management, online retailing, and experimental design. His work has appeared in leading journals and proceedings such as Management Science, Operations Research, Annals of Statistics, and NeurIPS. His research is supported by the UGC of Hong Kong and the Discovery Grants Program of Canada. Dr. Chen is the recipient of the Roger Martin Award for Excellence in Research and the IMI Research Award. He serves as an Associate Editor for Production and Operations Management and the INFORMS Journal on Data Science. He also collaborates on projects with industry partners including BAM, Polymatiks, and Alibaba.
