We propose a new method for solving high-dimensional dynamic programming problems and recursive competitive equilibria with a large (but finite) number of heterogeneous agents using deep learning. The "curse of dimensionality" is avoided due to four complementary techniques: (1) exploiting...

This paper develops a dynamic programming method when the one-stage deviation principle in the sense of mechanism design literature doesn’t hold. The commonly used dynamic programming method is valid only if the one-stage deviation principle in the sense of mechanism design literature is...

We examine the various senses in which economist use the term "rationality" and then outline some of the commonly drawn implications and auxiliary assumptions. Finally, we confront the implications with the empirical evidence, drawing on the insights from the exciting new field of behavioral...

We introduce a novel approach to solving dynamic programming problems, such as those in many economic models, on a quantum annealer, a specialized device that performs combinatorial optimization. Quantum annealers attempt to solve an NP-hard problem by starting in a quantum superposition of all...

This article compares an ACE system with a CBIT system in an open economy. Using a realoption approach we show that, if a firm can decide when to invest, a tradeoff is found. According to traditional wisdom, a high-income firm investing in an ACE system faces a heavier tax burden at each...

This paper extends the Hartman model to study the optimal rotation age of two interdependent stands when the stream of amenities produces from the two stands may be complements or substitutes, both in space and over time. In the presence of stand interdependence both the ownership structure and...

This paper presents an extensive theoretical and empirical analysis of the choice of schedule buffers by airlines. With airline delays a continuing problem around the world, such an undertaking is valuable, and its lessons extend to other passenger transportation sectors. One useful lesson from...

Anthropogenic greenhouse gas emissions are changing the energy balance of our planet. Various climatic feedbacks make the resulting warming over the next decades and centuries highly uncertain. We quantify how this uncertainty changes the optimal carbon tax in a stochastic dynamic programming...

Previous research on optimal R&D subsidies has focussed on the long run. This paper characterizes the optimal time path of R&D subsidization in a semi-endogenous growth model, by exploiting a recently developed numerical method. Starting from the steady state under current R&D subsidization in...