用多个大模型模拟不同经济人，分析政策影响。
A Multi-LLM-Agent-Based Framework for Economic and Public Policy Analysis
- 用不同大模型代表有差异认知能力的经济人。
- 在税收政策模拟中揭示群体间响应差异。
- 适合研究政策公平性与微观影响的学者。
本文开创性地提出一种基于多大语言模型（LLMs）的经济与公共政策分析框架。通过评估五种LLMs在两类情景下解决两期消费分配问题的能力——一类基于明确效用函数，另一类依赖直觉推理——发现不同模型在分析能力上存在固有差异。相较于以往仅通过改变提示词来模拟异质性，本方法利用各模型天然的认知差异，构建具有多样决策特质的经济代理人。基于此，我们建立多大模型代理框架（MLAB），将不同模型映射至特定教育水平和收入群体。以利率收入税为例，验证了该框架可模拟政策对异质性代理人的影响，展示了利用大模型类人推理与计算能力进行经济政策分析的新路径。
原文摘要 · Abstract (English)
This paper pioneers a novel approach to economic and public policy analysis by leveraging multiple Large Language Models (LLMs) as heterogeneous artificial economic agents. We first evaluate five LLMs' economic decision-making capabilities in solving two-period consumption allocation problems under two distinct scenarios: with explicit utility functions and based on intuitive reasoning. While previous research has often simulated heterogeneity by solely varying prompts, our approach harnesses the inherent variations in analytical capabilities across different LLMs to model agents with diverse cognitive traits. Building on these findings, we construct a Multi-LLM-Agent-Based (MLAB) framework by mapping these LLMs to specific educational groups and corresponding income brackets. Using interest-income taxation as a case study, we demonstrate how the MLAB framework can simulate policy impacts across heterogeneous agents, offering a promising new direction for economic and public policy analysis by leveraging LLMs' human-like reasoning capabilities and computational power.
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