用大模型设计可自适应的税收政策，平衡公平与效率
TaxAgent: How Large Language Model Designs Fiscal Policy
- 将大模型与智能体模拟结合，动态优化税率
- 在模拟中实现比现有税制更优的公平与效率平衡
- 适合政策研究者、数字治理开发者参考
经济不平等是全球性挑战，加剧教育、医疗和社会稳定方面的差距。传统税制如美国联邦所得税虽能缓解不平等，但缺乏灵活性。尽管萨兹最优税收模型可动态调整，却无法处理纳税人异质性及非理性行为。本文提出TaxAgent，一种将大语言模型（LLMs）与基于智能体建模（ABM）融合的新方法，用于设计自适应税收政策。在宏观经济仿真中，异质性H-智能体（家庭）模拟真实纳税人行为，而作为政府的TaxAgent利用大模型迭代优化税率，在公平与生产率间取得平衡。相较于萨兹最优税收、美国联邦所得税和自由市场，TaxAgent实现了更优的公平-效率权衡。该研究提供了一种新型税收解决方案，并构建了一个可扩展、数据驱动的财政政策评估框架。
原文摘要 · Abstract (English)
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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