TaxAgent: How Large Language Model Designs Fiscal Policy

Jizhou Wang; Xiaodan Fang; Lei Huang; Yongfeng Huang

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arXiv · 2506.02838

TaxAgent: How Large Language Model Designs Fiscal Policy

Jizhou Wang·Xiaodan Fang·Lei Huang·Yongfeng Huang

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Abstract

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 Taxation adjust dynamically, they fail to address taxpayer heterogeneity and irrational behavior. This study introduces TaxAgent, 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 TaxAgent (government) utilizes LLMs to iteratively optimize tax rates, balancing equity and productivity. Benchmarked against Saez Optimal Taxation, U.S. federal income taxes, and free markets, TaxAgent achieves superior equity-efficiency trade-offs. This research offers a novel taxation solution and a scalable, data-driven framework for fiscal policy evaluation.

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BibTeX RIS

Jizhou Wang, Xiaodan Fang, Lei Huang, Yongfeng Huang. 2025-06-03. TaxAgent: How Large Language Model Designs Fiscal Policy. https://arxiv.org/abs/2506.02838

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