Abstract
Behavioral parameters such as loss aversion, herding, and extrapolation are central to asset pricing models but remain difficult to measure reliably. We develop a framework that treats large language models (LLMs) as calibrated measurement instruments for behavioral parameters. Using four models and 24\{,\}000 agent--scenario pairs, we document systematic rationality bias in baseline LLM behavior,