Interpretable Optimal Stopping
Wednesday, October 3, 2018 - 2:45pm - 3:30pm
Optimal stopping is the problem of deciding when to stop a stochastic system to obtain the greatest reward; this arises in numerous application areas, such as finance, healthcare and marketing. State-of-the-art methods for high-dimensional optimal stopping involve determining an approximation to the value function or to the continuation value, 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 action prescribed by the policy. In this paper, we propose a new approach to optimal stopping, wherein the policy is represented as a binary tree, in the spirit of the naturally interpretable tree models commonly used in machine learning. 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 calibrated with 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. This talk is based on joint work with Florin Ciocan (INSEAD, Technology & Operations Management area).