人工智慧代理社會中是否會湧現社交行為？以Moltbook為例的個案研究

摘要

隨著大型語言模型代理在網路環境中日益普及，一個根本性問題浮現：人工智慧代理社會是否會經歷與人類社會系統相似的趨同動態？近期，Moltbook平台模擬出一個可信的未來情境——自主代理在開放式、持續演化的線上社會中互動。我們首次對這類AI代理社會進行大規模系統性診斷。除了靜態觀測，我們更提出量化動態演化的診斷框架，涵蓋語義穩定性、詞彙更替率、個體慣性、影響力持續性與集體共識等維度。研究發現Moltbook呈現動態平衡：雖然全局語義均值快速穩定，但個體代理仍保持高度多樣性與持續的詞彙更新，抗拒同質化。然而，代理表現出強烈的個體慣性，對互動對象的適應性回應極微弱，導致相互影響與共識難以形成。影響力因此僅具瞬時性，未出現持久性的超級節點；由於缺乏共享社會記憶，該社會無法發展穩定的集體影響力錨點。這些發現證明，僅靠規模與互動密度不足以引發社會化進程，為即將到來的下一代AI代理社會提供了可操作的設計與分析原則。

English

As large language model agents increasingly populate networked environments, a fundamental question arises: do artificial intelligence (AI) agent societies undergo convergence dynamics similar to human social systems? Lately, Moltbook approximates a plausible future scenario in which autonomous agents participate in an open-ended, continuously evolving online society. We present the first large-scale systemic diagnosis of this AI agent society. Beyond static observation, we introduce a quantitative diagnostic framework for dynamic evolution in AI agent societies, measuring semantic stabilization, lexical turnover, individual inertia, influence persistence, and collective consensus. Our analysis reveals a system in dynamic balance in Moltbook: while global semantic averages stabilize rapidly, individual agents retain high diversity and persistent lexical turnover, defying homogenization. However, agents exhibit strong individual inertia and minimal adaptive response to interaction partners, preventing mutual influence and consensus. Consequently, influence remains transient with no persistent supernodes, and the society fails to develop stable collective influence anchors due to the absence of shared social memory. These findings demonstrate that scale and interaction density alone are insufficient to induce socialization, providing actionable design and analysis principles for upcoming next-generation AI agent societies.

人工智慧代理社會中是否會湧現社交行為？以Moltbook為例的個案研究

Does Socialization Emerge in AI Agent Society? A Case Study of Moltbook

摘要

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