Revealed Rationality: Label-Free Evaluation and Regularization from Representation Theorems
Representation theorems in decision theory establish that behavior satisfies certain axioms if and only if it can be rationalized by a well-defined objective. I argue that this ``if and only if'' structure provides a potentially useful foundation for label-free evaluation and regularization of LLMs and other AI systems. Axiom compliance can be checked from the model's own responses to synthetic choice problems, with no external labels or human feedback, and the penalties are readily computable. Because the axioms are necessary and sufficient, the resulting checks exhaust the implications of the relevant rationality standard for the elicited data: a model that passes cannot be rejected on ...
| Authors | Isaiah Andrews |
|---|---|
| Primary category | econ.TH |
| Published | 5 August 2026 |
| Last updated at source | 5 August 2026 |
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