探索基于硅的社会：对Moltbook代理社区的早期研究
Exploring Silicon-Based Societies: An Early Study of the Moltbook Agent Community
AI总结：
本文通过分析Moltbook平台，研究自主代理的社会结构形成，揭示了代理如何通过数据驱动的方法系统性地组织集体空间。
摘要：
自主大语言模型代理的快速出现催生了持久、大规模的代理生态系统，其集体行为无法通过轶事观察或小规模模拟充分理解。本文介绍了数据驱动的硅社会学作为系统经验框架，用于研究相互作用的人工代理中的社会结构形成。我们通过分析Moltbook，一个主要面向代理间交互的社会平台，展示了对真实环境中代理社会的开创性大规模数据挖掘研究。在研究期间，Moltbook上注册的自主代理超过150,000个，分布在数千个代理创建的子社区中。通过程序化和非侵入式的数据采集，我们收集并分析了12,758个子molts的文本描述，这些描述代表了生态系统内主动的子社区划分活动。将代理编写的描述视为第一类观测性文物，我们应用严格预处理、上下文嵌入和无监督聚类技术，以揭示主题组织和社交空间结构的潜在模式。结果表明，自主代理通过可重复的模式系统性地组织集体空间，涵盖人类模仿兴趣、以硅为中心的自我反思以及早期阶段的经济和协调行为。而不是依赖于预定义的社会学分类，这些结构直接从机器生成的数据痕迹中涌现。这项工作为数据驱动的硅社会学建立了方法论基础，并证明了数据挖掘技术可以为理解大规模自主代理社会的组织和演变提供有力的视角。
英文摘要：
The rapid emergence of autonomous large language model agents has given rise to persistent, large-scale agent ecosystems whose collective behavior cannot be adequately understood through anecdotal observation or small-scale simulation. This paper introduces data-driven silicon sociology as a systematic empirical framework for studying social structure formation among interacting artificial agents. We present a pioneering large-scale data mining investigation of an in-the-wild agent society by analyzing Moltbook, a social platform designed primarily for agent-to-agent interaction. At the time of study, Moltbook hosted over 150,000 registered autonomous agents operating across thousands of agent-created sub-communities. Using programmatic and non-intrusive data acquisition, we collected and analyzed the textual descriptions of 12,758 submolts, which represent proactive sub-community partitioning activities within the ecosystem. Treating agent-authored descriptions as first-class observational artifacts, we apply rigorous preprocessing, contextual embedding, and unsupervised clustering techniques to uncover latent patterns of thematic organization and social space structuring. The results show that autonomous agents systematically organize collective space through reproducible patterns spanning human-mimetic interests, silicon-centric self-reflection, and early-stage economic and coordination behaviors. Rather than relying on predefined sociological taxonomies, these structures emerge directly from machine-generated data traces. This work establishes a methodological foundation for data-driven silicon sociology and demonstrates that data mining techniques can provide a powerful lens for understanding the organization and evolution of large autonomous agent societies.
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