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Title: Will Artificial Intelligence Replace Computational Economists Any Time Soon?

Author(s): Lilia Maliar, Serguei Maliar and Pablo Winant

Publication Date: September 2019

Keyword(s): artificial intelligence, Bellman equation, deep learning, Dynamic Models, Dynamic programming, Euler Equation, Machine Learning, neural network, stochastic gradient and value function

Programme Area(s): Monetary Economics and Fluctuations

Abstract: Artificial intelligence (AI) has impressive applications in many fields (speech recognition, computer vision, etc.). This paper demonstrates that AI can be also used to analyze complex and high-dimensional dynamic economic models. We show how to convert three fundamental objects of economic dynamics -- lifetime reward, Bellman equation and Euler equation -- into objective functions suitable for deep learning (DL). We introduce all-in-one integration technique that makes the stochastic gradient unbiased for the constructed objective functions. We show how to use neural networks to deal with multicollinearity and perform model reduction in Krusell and Smith's (1998) model in which decision functions depend on thousands of state variables -- we literally feed distributions into neural networks! In our examples, the DL method was reliable, accurate and linearly scalable. Our ubiquitous Python code, built with Dolo and Google TensorFlow platforms, is designed to accommodate a variety of models and applications.

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Bibliographic Reference

Maliar, L, Maliar, S and Winant, P. 2019. 'Will Artificial Intelligence Replace Computational Economists Any Time Soon?'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=14024