LLM经济学家：大规模人口模型与多智能体生成模拟中的机制设计

摘要

我们提出了LLM经济学家这一创新框架，它采用基于主体的建模方法，在具有层级决策的战略环境中设计和评估经济政策。在底层，有限理性的工人主体——通过从美国人口普查校准的收入和人口统计数据中采样，以人物角色为条件生成提示——选择劳动供给，以最大化在上下文中学习的基于文本的效用函数。在顶层，规划者主体运用上下文强化学习，提出以当前美国联邦税率为基准的分段线性边际税率表。这一构建赋予经济模拟三个进行可信财政实验所必需的能力：(i) 异质效用的优化，(ii) 原则性地生成大规模、人口统计上真实的主体群体，以及(iii) 完全用自然语言表达的机制设计——即终极的助推问题。在多达一百个互动主体的群体实验中，规划者收敛于接近斯塔克尔伯格均衡的状态，相较于Saez解决方案，提高了整体社会福利，而周期性的、基于人物角色的投票程序在去中心化治理下进一步提升了这些收益。这些结果表明，基于大型语言模型的主体能够共同建模、模拟和治理复杂的经济系统，为社会规模的政策评估提供了一个可操作的测试平台，助力构建更美好的文明。

English

We present the LLM Economist, a novel framework that uses agent-based modeling to design and assess economic policies in strategic environments with hierarchical decision-making. At the lower level, bounded rational worker agents -- instantiated as persona-conditioned prompts sampled from U.S. Census-calibrated income and demographic statistics -- choose labor supply to maximize text-based utility functions learned in-context. At the upper level, a planner agent employs in-context reinforcement learning to propose piecewise-linear marginal tax schedules anchored to the current U.S. federal brackets. This construction endows economic simulacra with three capabilities requisite for credible fiscal experimentation: (i) optimization of heterogeneous utilities, (ii) principled generation of large, demographically realistic agent populations, and (iii) mechanism design -- the ultimate nudging problem -- expressed entirely in natural language. Experiments with populations of up to one hundred interacting agents show that the planner converges near Stackelberg equilibria that improve aggregate social welfare relative to Saez solutions, while a periodic, persona-level voting procedure furthers these gains under decentralized governance. These results demonstrate that large language model-based agents can jointly model, simulate, and govern complex economic systems, providing a tractable test bed for policy evaluation at the societal scale to help build better civilizations.

LLM经济学家：大规模人口模型与多智能体生成模拟中的机制设计

LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra

摘要

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