@inproceedings{zhang-etal-2026-agentic,
    title = "Agentic Economic Modeling",
    author = "Zhang, Bohan  and
      Li, Jiaxuan  and
      Hortacsu, Ali  and
      Ye, Xiaoyang  and
      Chernozhukov, Victor  and
      Ni, Anqi  and
      Huang, Edward W",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.80/",
    pages = "1136--1149",
    ISBN = "979-8-89176-394-4",
    abstract = "We introduce Agentic Economic Modeling (AEM), a framework that aligns synthetic LLM choices with small-sample human evidence for reliable 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 with millions of observations, using only 10{\%} of the original data to fit the correction model lowers the error of the demand-parameter estimates, while uncorrected LLM choices even increase the errors. In a regional field experiment, a mixture model calibrated on 10{\%} of geographic regions estimates an out-of-domain treatment effect of -65$\pm$10 bps, closely matching the full human experiment (-60$\pm$8 bps). Under time-wise extrapolation, training with only day-one human data yields -24 bps (95{\%} CI: [-26, -22], p$<$1e-5), improving over the human-only day-one baseline (-17 bps, 95{\%} CI: [-43, +9], p=0.2049). These results demonstrate AEM{'}s potential to improve RCT efficiency and establish a foundation method for LLM-based counterfactual generation."
}