@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://aclanthology.org/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."
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<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\pm10 bps, closely matching the full human experiment (-60\pm8 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.</abstract>
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%0 Conference Proceedings
%T Agentic Economic Modeling
%A Zhang, Bohan
%A Li, Jiaxuan
%A Hortacsu, Ali
%A Ye, Xiaoyang
%A Chernozhukov, Victor
%A Ni, Anqi
%A Huang, Edward W.
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F zhang-etal-2026-agentic
%X 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\pm10 bps, closely matching the full human experiment (-60\pm8 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.
%U https://aclanthology.org/2026.acl-industry.80/
%P 1136-1149
Markdown (Informal)
[Agentic Economic Modeling](https://aclanthology.org/2026.acl-industry.80/) (Zhang et al., ACL 2026)
ACL
- Bohan Zhang, Jiaxuan Li, Ali Hortacsu, Xiaoyang Ye, Victor Chernozhukov, Anqi Ni, and Edward W Huang. 2026. Agentic Economic Modeling. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 1136–1149, San Diego, California, USA. Association for Computational Linguistics.