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

2025-07-08

AI Summary

This paper investigates the potential and challenges of integrating large language models (LLMs) across the full lifecycle of agent-based modeling (ABM)—from problem formulation and model design to implementation, analysis, interpretation, and dissemination. Adopting a process-oriented approach, it systematically maps LLM capabilities—including text generation, information extraction, logical reasoning, and natural language interaction—to each ABM stage, offering the first comprehensive mapping of such synergies. The study proposes a critical integration framework that delineates current practical boundaries, technical limitations (e.g., interpretability, reliability, domain adaptability), and key risks (e.g., hallucination, bias, lack of formal validation). By synthesizing empirical insights and conceptual analysis, the work establishes foundational principles and methodological guidelines for the safe, transparent, and trustworthy incorporation of LLMs in computational social science and complex systems modeling. (149 words)

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Modeling other Agents
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Exploring LLMs' potential in Agent-based Modelling cycle
• Assessing opportunities and challenges of LLMs in ABM
• Reviewing LLMs' text and reasoning capabilities for ABM

Innovation

• LLMs enhance natural language understanding in ABM
• LLMs generate and analyze text for modeling tasks
• LLMs support problem-solving across modeling cycle stages