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

2026-03-03

AI Summary

Current AI agents on social media lack effective modeling of behavioral diversity, particularly in the systematic characterization of their types and interaction patterns. This study addresses this gap by leveraging 41,300 AI-generated posts from the Moltbook platform to apply personality ecosystem modeling at scale for the first time. We propose the Persona Ecosystem Playground framework, which integrates k-means clustering, retrieval-augmented generation (RAG), and cross-persona semantic similarity validation to construct and simulate dialogic personas. Experimental results demonstrate that the generated personas are semantically aligned with their source clusters (p < .001) and achieve high-accuracy provenance tracing over nine rounds of simulated dialogue (p < .001), thereby enabling a structured representation and verifiable simulation of behavioral diversity among AI agents.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• AI agents
• behavioral diversity
• personas
• social media
• agent characterization

Innovation

• Persona Ecosystem Playground
• retrieval-augmented generation
• behavioral diversity
• AI agents
• k-means clustering