Modelisation a base d'Agent Augmentes par LLM pour les Simulations Sociales: Defis et Opportunites

TL;DR

This paper explores the integration of large language models with agent-based social simulations, highlighting challenges, proposing architectures, and discussing future research directions to enhance modeling of complex social systems.

Contribution

It introduces new architectures and methods for systematically developing LLM-augmented social simulations and discusses their potential research opportunities.

Findings

LLMs can enhance the realism of social simulations

Integration poses significant technical challenges

Future research directions are outlined

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.