Economic theories often progress through the discovery of “anomalies.” Canonical examples of anomalies include the Allais Paradox and the Kahneman-Tversky choice experiments, which are constructed menus of lotteries that highlighted particular flaws in expected utility theory and spurred the development of new theories for decision making under risk. In this paper, we develop algorithmic procedures to automatically generate such anomalies. Our algorithmic procedures take as inputs an existing theory and data it seeks to explain, and then generate examples on which we would likely observe violations of our existing theory if we were to collect data. As an illustration, we produce anomalies for expected utility theory using simulated lottery choice data from individuals who behave according to cumulative prospect theory. Our procedures recover known anomalies for expected utility theory in behavioral economics and discover
novel anomalies based on the probability weighting function. We conduct incentivized experiments to collect choice data on our algorithmically generated anomalies, finding that participants violate expected utility theory at similar rates to the Allais Paradox and Common Ratio Effect. While this illustration is specific, our anomaly generation procedures are general and can be applied in any domain where there exists a formal theory and rich data that the theory seeks to explain.
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