Sendhil Mullainathan, Ashesh Rambachan
arXiv 15 Apr 2024 · Econometrics · 5 citations (OpenAlex)
arXiv:2404.10111 · PDF · DOI · OpenAlex · Extracted main text
How can we extract theoretical insights from machine learning algorithms? We take a familiar lesson: researchers often turn their intuitions into theoretical insights by constructing "anomalies" -- specific examples highlighting hypothesized flaws in a theory, such as the Allais paradox and the Kahneman-Tversky choice experiments for expected utility. We develop procedures that replace researchers' intuitions with predictive algorithms: given a predictive algorithm and a theory, our procedures automatically generate anomalies for that theory. We illustrate our procedures with a concrete application: generating anomalies for expected utility theory. Based on a neural network that accurately predicts lottery choices, our procedures recover known anomalies for expected utility theory and discover new ones absent from existing work. In incentivized experiments, subjects violate expected utility theory on these algorithmically generated anomalies at rates similar to the Allais paradox and common ratio effect.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Joshua C. Peterson, David D. Bourgin, Mayank Agrawal, Daniel Reichma… (2021) Using large-scale experiments and machine learning to discover theories of human decision-making | 1.000 | 10 | 4 | 100% |
| 2 | Drew Fudenberg \ Annie Liang (2019) Predicting and Understanding Initial Play | 0.928 | 4 | 4 | 100% |
| 3 | David W. Harless \ Colin F. Camerer (1994) The Predictive Utility of Generalized Expected Utility Theories | 0.928 | 4 | 3 | 100% |
| 4 | Toshihiko Hirasawa, Michihiro Kandori \ Akira Matsushita (2025) Using Big Data and Machine Learning to Uncover How Players Choose Mixed Strategies | 0.928 | 4 | 3 | 100% |
| 5 | Meisam Razaviyayn, Tianjian Huang, Songtao Lu, Maher Nouiehed, Mazia… (2020) Nonconvex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances | 0.928 | 4 | 3 | 100% |
| 6 | Maurice Allais (1953) Le Comportement de l'Homme Rationnel devant le Risque: Critique des Postulats et Axiomes de l'Ecole Americaine | 0.874 | 6 | 2 | 100% |
| 7 | Daniel Kahneman \ Amos Tversky (1979) Prospect Theory: An Analysis of Decision under Risk | 0.874 | 5 | 2 | 100% |
| 8 | Isaiah Andrews, Drew Fudenberg, Lihua Lei, Annie Liang \ Chaofeng Wu (2025) The Transfer Performance of Economic Models | 0.843 | 3 | 3 | 100% |
| 9 | Alexander Peysakhovich \ Jeffrey Naecker (2017) Using methods from machine learning to evaluate behavioral models of choice under risk and ambiguity | 0.843 | 3 | 3 | 100% |
| 10 | James R. Wright \ Kevin Leyton-Brown (2017) Predicting human behavior in unrepeated, simultaneous-move games | 0.843 | 3 | 3 | 100% |
Showing the top 10 of 43 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Large Language Models: An Applied Econometric Framework | 0.644 | 2 | 2 |
| 2 | Mining Causality: AI-Assisted Search for Instrumental Variables | 0.405 | 1 | 1 |