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

Introduction

Efficient resource allocation is a critical challenge in various domains, including healthcare, logistics, and disaster management. The SRAP-Agent framework harnesses the capabilities of LLMs to simulate complex scenarios and optimize resource allocation policies effectively. By leveraging LLMs, SRAP-Agent enables decision-makers to test and refine their allocation strategies in a virtual environment, improving outcomes in real-world applications. Explore the source code on GitHub.

Recommended citation: Ji, Jiarui, et al. "SRAP-Agent: Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agent." Findings of the Association for Computational Linguistics: EMNLP 2024.
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