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Scale-free networks are one of the most famous examples of emergent behaviorand are ubiquitous in social systems, especially online social media in whichusers can follow each other. By analyzing the interactions of multiplegenerative agents using GPT3.5-turbo as a language model, we demonstrate theirability to not only mimic individual human linguistic behavior but also exhibitcollective phenomena intrinsic to human societies, in particular the emergenceof scale-free networks. We discovered that this process is disrupted by askewed token prior distribution of GPT3.5-turbo, which can lead to networkswith extreme centralization as a kind of alignment. We show how renaming agentsremoves these token priors and allows the model to generate a range of networksfrom random networks to more realistic scale-free networks.

Emergence of Scale-Free Networks in Social Interactions among Large Language Models