SRAP-Agent: Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agent
This addresses the challenge of improving efficiency and equity in public scarce resource allocation, such as housing, by overcoming limitations of traditional methods, though it appears incremental as it builds on existing simulation approaches with LLM integration.
The paper tackles the problem of scarce resource allocation in economics by proposing SRAP-Agent, a framework that integrates Large Language Models into economic simulations to bridge the gap between theoretical models and real-world dynamics, using public housing allocation as a case study and achieving verified feasibility and effectiveness through policy simulation experiments.
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