Collaborative Research: Artificial intelligence and deep learning solution methods for dynamic economic models

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AbstractArtificial intelligence (AI) has many impressive applications, including self-driving cars, computer vision, and speech recognition. This project demonstrates that many challenging economic models and applications can be successfully analyzed by using the same break-ground AI technologies and the same state-of-the-art combinations of software and hardware as those used by data scientists for dealing with their impressive applications. This project develops open-source AI software that makes it possible to examine complex economic models that were intractable under the earlier solution methods. Applications of this AI framework include central-banking models of monetary policy, growth models of wealth inequality, and models of social security and population aging. The developed AI tools will be disseminated in the profession by providing carefully documented replicated materials, examples and tutorials.This project consists of several components. First, the project shows how to convert three fundamental objects of economic dynamics -- lifetime reward, Bellman equation and Euler equation -- into objective functions suitable for deep learning. Second, the project adapts the stochastic gradient descent method to maximizing the objective on few randomly drawn grid points instead of a large fixed grid used by conventional solution methods. Third, the project shows how to construct the expectation operators for dynamic economic models by combining multiple expectation operators into a single unbiased expectation operator. Fourth, the project automates the AI solution framework to make it ubiquitous and portable to other applications. Fifth, the project solves a collection of empirically relevant applications that represent challenges to the existing solution methods in economics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.