SRAP-Agent: Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agent

• URL: http://arxiv.org/abs/2410.14152v1
• Date: Fri, 18 Oct 2024 03:43:42 GMT
• Title: SRAP-Agent: Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agent
• Authors: Jiarui Ji, Yang Li, Hongtao Liu, Zhicheng Du, Zhewei Wei, Weiran Shen, Qi Qi, Yankai Lin,
• Abstract summary: We propose an innovative framework, SRAP-Agent, which integrates Large Language Models (LLMs) into economic simulations. We conduct extensive policy simulation experiments to verify the feasibility and effectiveness of the SRAP-Agent.
• Score: 45.41401816514924
• License:
• Abstract: 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

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