LLM-Augmented Agent-Based Modelling for Social Simulations: Challenges and Opportunities

2024/05/08 by Önder Gürcan, Gurcan, Onder · 7 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Physical sciences #Multi-Agent Systems and Negotiation #Physics and Society (physics.soc-ph) #Transportation and Mobility Innovations

paper · pdf · doi:10.48550/arxiv.2405.06700

openalex publication_date 2024/05/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

As large language models (LLMs) continue to make significant strides, their better integration into agent-based simulations offers a transformational potential for understanding complex social systems. However, such integration is not trivial and poses numerous challenges. Based on this observation, in this paper, we explore architectures and methods to systematically develop LLM-augmented social simulations and discuss potential research directions in this field. We conclude that integrating LLMs with agent-based simulations offers a powerful toolset for researchers and scientists, allowing for more nuanced, realistic, and comprehensive models of complex systems and human behaviours.

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• Applying Psychometrics to Large Language Model Simulated Populations: Recreating the HEXACO Personality Inventory Experiment with Generative Agents
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