Deep Learning for Solving Economic Models

The ongoing revolution in artificial intelligence, especially deep learning, is transforming research across many fields, including economics. Its impact is particularly strong in solving equilibrium economic models. These models often lack closed-form solutions, so economists have relied on numerical methods such as value function iteration, perturbation, and projection techniques. While powerful, these approaches face the curse of dimensionality, making global solutions computationally infeasible as the number of state variables increases. Recent advances in deep learning offer a new paradigm: flexible tools that efficiently approximate complex functions, manage high-dimensional problems, and expand the reach of quantitative economics. After introducing the basic concepts of deep learning, I illustrate the approach with the neoclassical growth model and discuss related ideas, including the double descent phenomenon and implicit regularization.