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

• URL: http://arxiv.org/abs/2405.06700v1
• Date: Wed, 8 May 2024 08:57:54 GMT
• Title: LLM-Augmented Agent-Based Modelling for Social Simulations: Challenges and Opportunities
• Authors: Onder Gurcan,
• Abstract summary: Integrating large language models with agent-based simulations offers a transformational potential for understanding complex social systems. We explore architectures and methods to systematically develop LLM-augmented social simulations. We conclude that integrating LLMs with agent-based simulations offers a powerful toolset for researchers and scientists.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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