  Scale-free networks are one of the most famous examples of emergent behavior
and are ubiquitous in social systems, especially online social media in which
users can follow each other. By analyzing the interactions of multiple
generative agents using GPT3.5-turbo as a language model, we demonstrate their
ability to not only mimic individual human linguistic behavior but also exhibit
collective phenomena intrinsic to human societies, in particular the emergence
of scale-free networks. We discovered that this process is disrupted by a
skewed token prior distribution of GPT3.5-turbo, which can lead to networks
with extreme centralization as a kind of alignment. We show how renaming agents
removes these token priors and allows the model to generate a range of networks
from random networks to more realistic scale-free networks.

Physics and Society;Computers and Society