REVIEW 1 cited by
LLM-Augmented Agent-Based Modelling for Social Simulations: Challenges and Opportunities
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 1 Pith paper
-
Human-Agent Interaction in Synthetic Social Networks: A Framework for Studying Online Polarization
The authors present and test a framework that combines LLM-based social media agents with formal opinion dynamics, finding that polarized agent discussions change how human participants perceive emotionality, group id...
Discussion (0). Continue with ORCID to comment.