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A Structured Solution of Prescriptive Analytics: Theory & Experiment

Dr. Justin Jia
Assistant Professor
Department of Business Analytics & Statistics
The University of Tennessee
Friday, October 25, 2019   2:30-3:30pm
JDT 410

Abstract:

In a prescriptive analytics problem, a decision maker aims to solve an optimization problem involving certain random variables. The decision maker does not know the distribution of the random variables, but instead observes a sample from the distribution and makes a decision based on the sample. In this study, we develop a solution framework and show that for a practical class of problems, the decision, a high-dimensional function of data, can be reduced to a simple single-dimensional function of a “sufficient prescriptive statistic.” We fully characterize the solution and derives its asymptotic properties and finite-sample performance guarantees. Applying the solution procedure to data-driven auction design, we obtain insights on the impact of data on decisions. We further report on a laboratory experiment with financially incentivized human subjects and show that behavior qualitatively matches theoretical predictions.

Bio:

Justin Jia is an assistant professor in the Department of Business Analytics and Statistics at the University of Tennessee.  He holds a Ph.D. in Supply Chain Management from Penn State University and served as a faculty member at Purdue University before joining UT. His research interests lie in the areas of prescriptive analytics, healthcare supply chain, e-operations and inventory management. He has published articles in journals such as Production and Operations Management and Management Science.