We consider the problem of stopping a diffusion process with a payoff functional involving probability distortion. The problem is inherently time-inconsistent as the level of distortion of a same event changes over time. We study stopping decisions of naive agents who reoptimize continuously in...

This paper considers a time-inconsistent stopping problem in which the inconsistency arises from non-constant time preference rates. We show that the smooth pasting principle, the main approach that has been used to construct explicit solutions for conventional time-consistent optimal stopping...

We aim to cluster financial assets in order to identify a small set of stocks to approximate the level of diversification of the whole universe of stocks. We develop a data-driven approach to clustering based on a correlation blockmodel in which assets in the same cluster have the same...

This document contains supplementary materials to "Optimal Exit Time from Casino Gambling: Strategies of Pre-committed and Naive Gamblers" by X.D. He, S. Hu, J. Obloj, and X.Y. Zhou.The paper to which these supplementary materials apply will appear in the SIAM Journal on Control and Optimization...

We consider reinforcement learning (RL) in continuous time and study the problem of achieving the best trade-off between exploration of a black box environment and exploitation of current knowledge. We propose an entropy-regularized reward function involving the differential entropy of the...

This paper presents an asymptotic analysis of hierarchical production planning in a manufacturing system with two tandem machines that are subject to breakdown and repair. The buffer between the two machines is assumed finite. Therefore, the number of parts in that buffer needs to be...

This paper presents an asymptotic analysis of hierarchical production planning in a manufacturing system with serial machines that are subject to breakdown and repair, and with convex costs. The machines capacities are modeled as Markov chains. Since the number of parts in the internal buffers...

We strive to seek optimality, but often find ourselves trapped in bad "optimal" solutions that are either local optimizers, or too rigid to leave any room for errors, or simply based on wrong models. A way to break this "curse of optimality" is to engage exploration through randomization....

Time inconsistency is prevalent in dynamic choice problems: a plan of actions to be taken in the future that is optimal for an agent today may not be optimal for the same agent in the future. If the agent is aware of this intrapersonal conflict but unable to commit herself in the future to...