@inproceedings{ji-etal-2024-srap,
title = "{SRAP}-Agent: Simulating and Optimizing Scarce Resource Allocation Policy with {LLM}-based Agent",
author = "Ji, Jiarui and
Li, Yang and
Liu, Hongtao and
Du, Zhicheng and
Wei, Zhewei and
Qi, Qi and
Shen, Weiran and
Lin, Yankai",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.15/",
doi = "10.18653/v1/2024.findings-emnlp.15",
pages = "267--293",
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, 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 \url{https://github.com/jijiarui-cather/SRAPAgent_Framework}."
}
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<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, 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.</abstract>
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%0 Conference Proceedings
%T SRAP-Agent: Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agent
%A Ji, Jiarui
%A Li, Yang
%A Liu, Hongtao
%A Du, Zhicheng
%A Wei, Zhewei
%A Qi, Qi
%A Shen, Weiran
%A Lin, Yankai
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F ji-etal-2024-srap
%X 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, 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.
%R 10.18653/v1/2024.findings-emnlp.15
%U https://aclanthology.org/2024.findings-emnlp.15/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.15
%P 267-293
Markdown (Informal)
[SRAP-Agent: Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agent](https://aclanthology.org/2024.findings-emnlp.15/) (Ji et al., Findings 2024)
ACL
- Jiarui Ji, Yang Li, Hongtao Liu, Zhicheng Du, Zhewei Wei, Qi Qi, Weiran Shen, and Yankai Lin. 2024. SRAP-Agent: Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agent. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 267–293, Miami, Florida, USA. Association for Computational Linguistics.