TaxAgent: How Large Language Model Designs Fiscal Policy

--Economic inequality is a global challenge, intensifying disparities in education, healthcare, and social stability. Traditional systems like the U.S. federal income tax reduce inequality but lack adaptability. Although models like the Saez Optimal T axation adjust dynamically, they fail to address taxpayer heterogeneity and irrational behavior . This study introduces T axAgent, a novel integration of large language models (LLMs) with agent-based modeling (ABM) to design adaptive tax policies. In our macroeconomic simulation, heterogeneous H-Agents (households) simulate real-world taxpayer behaviors while the T axAgent (government) utilizes LLMs to iteratively optimize tax rates, balancing equity and productivity. Benchmarked against Saez Optimal T axation, U.S. federal income taxes, and free markets, T axAgent achieves superior equity-efficiency tradeoffs. This research offers a novel taxation solution and a scalable, data-driven framework for fiscal policy evaluation. Economic inequality is a critical global issue with profound social, political, and economic impacts. Research highlights its detrimental effects on education, healthcare, political stability, and economic growth[1, 2, 3].