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Main Author: Gürcan, Önder
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2409.00100
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author Gürcan, Önder
author_facet Gürcan, Önder
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2409_00100
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Modelisation a base d'Agent Augmentes par LLM pour les Simulations Sociales: Defis et Opportunites
Gürcan, Önder
Physics and Society
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.
title Modelisation a base d'Agent Augmentes par LLM pour les Simulations Sociales: Defis et Opportunites
topic Physics and Society
url https://arxiv.org/abs/2409.00100