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This article examines how machine learning and other computational techniques can contribute to the development of economic models. First, comparing economic models to flexible algorithms can help clarify the strengths and limitations
of existing models, for instance how far they are from the predictive limit. Second, algorithms can adversarially probe economic models to identify cases in which the theoretical predictions fail. Third, “hybrid” models can combine interpretable economic structure with flexible learning methods to leverage the strengths of both. Finally, large language models introduce qualitatively new possibilities, from simulating human responses to generating novel hypotheses. Throughout, I emphasize both promise and limits: while machine learning can uncover patterns that standard models miss, translating these algorithmic insights into interpretable and portable economic understanding typically still requires human judgment.
