Agentic Economic Modeling

Bohan Zhang, Jiaxuan Li, Ali Horta\c{c}su, Xiaoyang Ye, Victor Chernozhukov, Angelo Ni and Edward W Huang

Papers from arXiv.org

Abstract: We introduce Agentic Economic Modeling (AEM), a framework that aligns synthetic LLM choices with small-sample human evidence for econometric inference. AEM first generates task-conditioned synthetic choices via LLMs, then learns a bias-correction mapping from task features and raw LLM choices to human-aligned choices, upon which standard econometric estimators perform inference to recover demand elasticities and treatment effects. We validate AEM in two experiments. In a large scale conjoint study, using only 10% of the original data to fit the correction model lowers the error of the demand-parameter estimates, while uncorrected LLM choices increase the errors. In a regional field experiment, a mixture model calibrated on 10% of geographic regions estimates a treatment effect of -65$\pm$10 bps on the hold-out regions, closely matching the full human experiment (-60$\pm$8 bps). These results demonstrate AEM's potential to improve RCT efficiency and represent a step toward LLM-based counterfactual generation.

Date: 2025-10, Revised 2026-07
New Economics Papers: this item is included in nep-ain, nep-ecm and nep-exp
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Citations: View citations in EconPapers (1)

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