Large Language Models for Agent-Based Modelling: Current and possible uses across the modelling cycle

TL;DR

This paper reviews how Large Language Models can enhance various stages of Agent-Based Modelling, highlighting current uses, opportunities, and challenges across the entire modelling cycle.

Contribution

It provides a comprehensive analysis of LLM applications in ABM and discusses future potential and limitations throughout the modelling process.

Findings

LLMs can assist in problem formulation and documentation.

They offer capabilities in data analysis and explanation generation.

Challenges include integration complexity and model reliability.

Abstract

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.