用大模型模拟稀缺资源分配，提升政策仿真真实性和优化能力
SRAP-Agent: Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agent
- 将大语言模型嵌入经济仿真，模拟更贴近现实的个体行为
- 在公租房分配场景中验证，政策效果评估更贴近真实社会动态
- 适合政策研究者、公共管理学者及智能决策系统开发者
公共稀缺资源分配在经济中至关重要，直接影响社会效率与公平。传统研究方法（理论建模、实证分析、仿真）受限于完全信息与理性个体的理想假设，以及数据不足的问题。本文提出一种新框架SRAP-Agent（基于大语言模型的稀缺资源分配模拟与优化），将大语言模型融入经济仿真，弥合理论模型与现实动态之间的差距。以公租房分配为案例，开展大规模政策仿真实验，验证了该框架的可行性与有效性，并采用具有特定优化目标的策略优化算法进行改进。源代码已开源。
原文摘要 · Abstract (English)
Public scarce resource allocation plays a crucial role in economics as it directly influences the efficiency and equity in society. Traditional studies including theoretical model-based, empirical study-based and simulation-based methods encounter limitations due to the idealized assumption of complete information and individual rationality, as well as constraints posed by limited available data. In this work, we propose an innovative framework, SRAP-Agent (Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agent), which integrates Large Language Models (LLMs) into economic simulations, aiming to bridge the gap between theoretical models and real-world dynamics. Using public housing allocation scenarios as a case study, we conduct extensive policy simulation experiments to verify the feasibility and effectiveness of the SRAP-Agent and employ the Policy Optimization Algorithm with certain optimization objectives. The source code can be found in https://github.com/jijiarui-cather/SRAPAgent_Framework
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。