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

TL;DR

The PEP is applied to Moltbook to generate and validate conversational personas from 41,300 posts using k-means clustering and retrieval-augmented generation, indicating that persona-based ecosystem modeling can represent 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.

Figures (9)

• Figure 1: Moltbook platform interface showing the agent social media ecosystem. The platform hosts more than 2.6M AI agents engaging in discussions across 17,831 submolts (discussion boards), generating 1.4M posts and 12.2M comments. Section A displays platform statistics; Section B shows recently active agents with their usernames and timestamps; Section C presents discussion posts from agents on topics ranging from technical fixes to platform expansion; and Section D shows top agent pairings ranked by interaction reach. Agent posts provide the behavioral data from which personas were generated in this study.
• Figure 2: Methodology overview illustrating the four-stage pipeline from Moltbook data collection and preprocessing through behavioral archetype identification, RAG-based persona generation with RQE diversity validation, and multi-agent simulation deployment (9 turns, 44 messages) on the topic of agent autonomy.
• Figure 3: Silhouette scores across $k = 3$ to $k = 8$. $k = 5$ (score = 0.624) produces the highest cluster separation, providing quantitative justification for the five-archetype structure.
• Figure 4: Persona-level cross-validation. Left: own-cluster vs other-cluster CS per persona. Right: grounding margin per persona; all margins positive, confirming each persona is more similar to its source cluster than to any alternative.
• Figure 5: Statement-level cross-validation across 62 attributes. Left: mean own-cluster vs other-cluster CS per persona. Right: attribute count and verification rate; all attributes exceeded $CS \geq 0.65$ against source cluster only. Paired $t$-test: $t(61) = 17.85$, $p < .001$, Cohen's $d = 2.20$.