Will Artificial Intelligence Replace Computational Economists Any Time Soon?

Serguei Maliar and Pablo Winant

No 14024, CEPR Discussion Papers from Centre for Economic Policy Research

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.

Keywords: Artificial intelligence; Machine learning; Deep learning; Neural network; Stochastic gradient; Dynamic models; Dynamic programming; Bellman equation; Euler equation; Value function (search for similar items in EconPapers)
Date: 2019-09
New Economics Papers: this item is included in nep-big, nep-cmp, nep-dge, nep-ore and nep-pay
References: View complete reference list from CitEc
Citations: View citations in EconPapers (21)

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