TaxAgent: How Large Language Model Designs Fiscal Policy
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909635037888512 |
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| author | Wang, Jizhou Fang, Xiaodan Huang, Lei Huang, Yongfeng |
| author_facet | Wang, Jizhou Fang, Xiaodan Huang, Lei Huang, Yongfeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02838 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TaxAgent: How Large Language Model Designs Fiscal Policy Wang, Jizhou Fang, Xiaodan Huang, Lei Huang, Yongfeng Artificial Intelligence General Economics Economics I.2.11; I.6.5; J.4 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. |
| title | TaxAgent: How Large Language Model Designs Fiscal Policy |
| topic | Artificial Intelligence General Economics Economics I.2.11; I.6.5; J.4 |
| url | https://arxiv.org/abs/2506.02838 |