The emergence of Large Language Models (LLMs) with increasingly sophisticated natural language understanding and generative capabilities has sparked interest in the Agent-based Modelling (ABM) community. With their ability to summarize, generate, analyze, categorize, transcribe and translate text, answer questions, propose explanations, sustain dialogue, extract information from unstructured text, and perform logical reasoning and problem-solving tasks, LLMs have a good potential to contribute to the modelling process. After reviewing the current use of LLMs in ABM, this study reflects on the opportunities and challenges of the potential use of LLMs in ABM. It does so by following the modelling cycle, from problem formulation to documentation and communication of model results, and holding a critical stance.
@article{arxiv.2507.05723,
title = {Large Language Models for Agent-Based Modelling: Current and possible uses across the modelling cycle},
author = {Loïs Vanhée and Melania Borit and Peer-Olaf Siebers and Roger Cremades and Christopher Frantz and Önder Gürcan and František Kalvas and Denisa Reshef Kera and Vivek Nallur and Kavin Narasimhan and Martin Neumann},
journal= {arXiv preprint arXiv:2507.05723},
year = {2025}
}
Comments
18 pages, including 2 pages of appendix, accepted for publication at the Social Simulation Conference 2025 (https://ssc2025.tbm.tudelft.nl/)