Could It Have Fit Anything? Restrictiveness of Economic Models, LLMs, and AI Workflows

Abstract

How much does a prediction procedure rule out when its model class is unspecified? We extend restrictiveness from economic models to black-box procedures by measuring how well they approximate simulated prediction rules satisfying specified economic restrictions. The measure applies to continuum domains, and converges to model-class restrictiveness under risk consistency. We provide simulation-based estimators and inference, and apply the measure alongside empirical completeness in two settings. For lottery certainty equivalents, LLM completion and a code-execution agent attain nearly identical completeness, but the agent is substantially less restrictive. A two-parameter cumulative prospect theory specification achieves completeness close to the LLM procedures and tabular foundation models (TFM) while being substantially more restrictive. For consumer choice over products, an LLM agent and a TFM are less restrictive than fitted multinomial, nested, and mixed logit models in both shares and implied price elasticities. Despite allowing more flexible substitution patterns, nested and mixed logit fit simulated shares better than multinomial logit without producing more accurate price elasticities.