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.

翻译：AI智能体在社交媒体平台上的活跃度日益提升，大规模生成内容并相互交互。然而，这些智能体的行为多样性仍鲜为人知，当前研究也缺乏系统方法来刻画不同智能体类型及其在共享话题中的交互模式。本研究将角色生态系统游乐场（PEP）应用于AI智能体社交平台Moltbook，通过k-means聚类与检索增强生成技术，从41,300条帖子中构建并验证对话角色人格。跨角色验证表明，角色人格在语义上更接近其源聚类而非其他聚类（t(61) = 17.85, p < .001, d = 2.20；源聚类均值 = 0.71 vs. 其他聚类均值 = 0.35）。这些角色人格随后被部署于九轮结构化讨论中，模拟信息被准确归因至源角色的概率显著高于随机水平（二项检验, p < .001）。研究结果表明，基于角色人格的生态系统建模能够有效表征AI智能体群体的行为多样性。