Agentic Economic Modeling

TL;DR

Agentic Economic Modeling (AEM) leverages large language models to generate synthetic choices and correct biases, enabling reliable econometric inference with significantly reduced human data, demonstrated through large-scale and regional experiments.

Contribution

AEM introduces a novel framework combining LLM-generated synthetic choices with bias correction for improved econometric analysis using minimal human data.

Findings

AEM reduces demand-parameter estimate errors with only 10% of original data.

AEM accurately estimates treatment effects in regional field experiments.

Time-wise extrapolation with AEM improves over human-only data baseline.

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.