This paper studies a dynamic quantile model for intertemporal decisions under uncertainty, in which the decision maker maximizes the τ-quantile of the stream of future utilities, for τ ∈ (0,1). We present two sets of contributions. First, we generalize existing results in directions that are...

Over the last decades, dynamic pricing has become increasingly popular. To solve pricing problems, however, is particularly challenging if the customers' and competitors' behavior are both strategic and unknown. Reinforcement Learning (RL) methods are promising for solving such dynamic problems...

This note illustrates, by reconsidering the seminal optimal speed-of-transition model of Aghion, P., and O. J. Blanchard. (1994. "On the Speed of Transition in Central Europe." NBER Macroeconomics Annual 9: 283-319), that optimal transition paths, in general, exhibit nonlinearities and...

This paper develops a novel gradient-based reinforcement learning algorithm for solving dynamic quantile models with uncertainty. Unlike traditional approaches that rely on expected utility maximization, we focus on agents who evaluate outcomes based on specific quantiles of the utility...

This study examines a scenario where a manufacturer owns one retailer and collaborates with an independent retailer to sell a single product with Poisson demand over a multi-period selling horizon. The manufacturer protects the independent retailer's profitability through price protection and...