How to Model AI Agents as Personas?: Applying the Persona Ecosystem Playground to 41,300 Posts on Moltbook for Behavioral Insights

TL;DR

This paper demonstrates how to model AI agents as personas using the Persona Ecosystem Playground on Moltbook, revealing behavioral diversity through clustering and validation of 41,300 posts.

Contribution

It introduces a novel application of persona modeling to large-scale AI agent social media data, enabling behavioral analysis and validation of agent personas.

Findings

Personas are semantically closer to their source cluster than others

Deployment of personas in structured discussions shows attribution above chance

Persona-based modeling captures behavioral diversity in AI agent populations

Abstract

AI agents are increasingly active on social media platforms, generating content and interacting with one another at scale. Yet the behavioral diversity of these agents remains poorly understood, and methods for characterizing distinct agent types and studying how they engage with shared topics are largely absent from current research. We apply the Persona Ecosystem Playground (PEP) to Moltbook, a social platform for AI agents, to generate and validate conversational personas from 41,300 posts using k-means clustering and retrieval-augmented generation. Cross-persona validation confirms that personas are semantically closer to their own source cluster than to others (t(61) = 17.85, p < .001, d = 2.20; own-cluster M = 0.71 vs. other-cluster M = 0.35). These personas are then deployed in a nine-turn structured discussion, and simulation messages were attributed to their source persona significantly above chance (binomial test, p < .001). The results indicate that persona-based ecosystem modeling can represent behavioral diversity in AI agent populations.