Virtual Seminar Series: Velibor V. Mišić, Anderson Schol of Management, UCLA
February 5, 2021
2:00 PM - 3:30 PM
Join us for a virtual seminar this semester with Velibor V. Mišić, Anderson Schol of Management, UCLA.
Please use the link below to join via Zoom.
Meeting ID: 975 3576 7242
Password: IDStalk21
Title: Interpretable Optimal Stopping
Abstract: Optimal stopping is the problem of deciding when to stop a stochastic system to obtain the greatest reward, arising in numerous application areas such as finance, healthcare and marketing. State-of-the-art methods for high-dimensional optimal stopping involve approximating the value function or the continuation value function, and then using that approximation within a greedy policy. Although such policies can perform very well, they are generally not guaranteed to be interpretable; that is, a decision maker may not be able to easily see the link between the current system state and the policy's action. In this paper, we propose a new approach to optimal stopping, wherein the policy is represented as a binary tree, in the spirit of naturally interpretable tree models commonly used in machine learning. We show that the class of tree policies is rich enough to approximate the optimal policy. We formulate the problem of learning such policies from observed trajectories of the stochastic system as a sample average approximation (SAA) problem. We prove that the SAA problem converges under mild conditions as the sample size increases, but that computationally even immediate simplifications of the SAA problem are theoretically intractable. We thus propose a tractable heuristic for approximately solving the SAA problem, by greedily constructing the tree from the top down. We demonstrate the value of our approach by applying it to the canonical problem of option pricing, using both synthetic instances and instances involving real S&P-500 data. Our method obtains policies that (1) outperform state-of-the-art non-interpretable methods, based on simulation-regression and martingale duality, and (2) possess a remarkably simple and intuitive structure.
Bio: Velibor Mišić is an assistant professor of Decisions, Operations and Technology Management at the UCLA Anderson School of Management. Prior to joining UCLA, he received his BASc and MASc degrees in industrial engineering from the University of Toronto and his PhD degree in operations research from MIT. His research interests include optimization, machine learning, stochastic control, operations management and marketing science. His work has been recognized with several awards, including second place in the INFORMS Junior Faculty Interest Group (JFIG) Best Paper Award and as finalist in the INFORMS Data Mining Section Best Paper Competition. At UCLA Anderson, he has received the Master of Science in Business Analytics (MSBA) Faculty Excellence Award twice and the Eric and "E" Juline Faculty Excellence in Research Award. He currently serves as an associate editor for the INFORMS journals Manufacturing & Services Operations Management and Service Science.
Date posted
Feb 1, 2021
Date updated
Feb 1, 2021