Will Artificial Intelligence Replace Computational Economists any Time Soon?
Serguei Maliar, Lilia Maliar, and Pablo Winant
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 highdimensional
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.