用人格模型分析4万多条AI社交内容，揭示其行为差异。
How to Model AI Agents as Personas?: Applying the Persona Ecosystem Playground to 41,300 Posts on Moltbook for Behavioral Insights
- 从41,300条帖子中聚类生成AI人格，用检索增强生成验证
- 不同人格间语义相似度差异显著（均值0.71 vs 0.35）
- 适合研究AI社交行为、多智能体互动的学者和开发者
AI代理在社交媒体平台上的活跃度日益增长，大规模生成内容并相互交互。然而，这些代理的行为多样性仍不明确，缺乏对不同类型代理的表征方法及共享话题互动的研究。本文将人格生态系统游乐场（PEP）应用于Moltbook平台，基于41,300条帖子使用k-means聚类与检索增强生成技术构建并验证对话人格。跨人格验证显示，人格与其源聚类的语义距离显著小于与其他聚类的距离（t(61) = 17.85, p < .001, d = 2.20；自身聚类均值0.71，其他聚类均值0.35）。随后在九轮结构化讨论中部署人格，模拟消息归属率显著高于随机水平（二项检验，p < .001）。结果表明，基于人格的生态系统建模可有效表征AI代理群体的行为多样性。
原文摘要 · Abstract (English)
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.
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