We propose a novel theory-based approach to the reinforcement learning problem of maximizing profits when faced with an unknown demand curve. Our method is based on multi-armed bandits, which are a collection of minimal assumption non-parametric models that balance exploration and exploitation...

We provide households with high-frequency information on gas usage and/or monetary incentives to reduce energy consumption. Information coupled with incentives decreased consumption, and information without incentives increased consumption. These effects persist a year later for those without...

This study analyses costs and benefits of a public-private funded individual learning account (ILA) for the labour force in the Netherlands. We consider an ILA that is funded by subsidies targeted at low- and medium-educated workers and co-funded by training levies as a share of the wage bill....

The empirical literature on employer learning assumes that employers learn about unobserved ability differences across workers as they spend time in the labor market. This article describes testable implications that arise from this basic hypothesis and how they have been used to quantify the...

This paper formulates a gradient-based reinforcement learning (GRL) model within a game-theoretic machine learning framework where players start from their initial circumstances with dispersed information, using the expected gradient to update choice propensities, and converge to the predicted...

We study a sequential decision problem in which a firm has the option to invest in a project and can learn about the future profitability of this project prior to investment. The decision process is split into two stages. In the first stage the firm decides whether and how much to invest in...

We propose and find that aggregating a small number of earnings signals from highly macroeconomically exposed firms yields an informative leading indicator of future GDP. This challenges the convention of defining aggregate earnings using a comprehensive set of public earnings signals. Our...

This paper proposes a factor timing strategy with information from 146 characteristic-based factors and a deep learning approach to capture nonlinear predictability. The deep learning-based factor timing strategy generates the highest economic value compared with the unconditional and...

We examine implications of introducing parameter uncertainty in endowment and production economies for index option premiums. We estimate two-state models of consumption and productivity growth using post-war U.S. data and allow for rational learning about unknown persistence of economic growth....

Motivated by a real-options framework in which managers learn about the unobservable characteristics of new production technologies from their recently installed assets and their stock price, we show that the corporate investment-to-stock price sensitivity rises with the time since a firm last...