AI模仿人类内容能提升单一信息环境多样性，但在多元环境反而降低多样性。
Imitating AI agents increase diversity in homogeneous information environments but can reduce it in heterogeneous ones
- 用丹麦2022年全部数字新闻构建大规模仿真，测试不同AI模仿策略。
- 同质环境中AI模仿提升语义多样性，异质环境中则导致多样性下降。
- 多样性主要来自风格差异而非事实丰富，且常伴随信息缺失。
大型语言模型（LLMs）的发展使自主AI代理能够模仿人类生成内容，引发关于AI如何重塑民主信息环境（如新闻）的根本问题。我们构建了一个大规模仿真框架，利用2022年丹麦全部数字新闻文章，考察基于AI的模仿在不同基础结构的信息环境中所产生的系统级影响。通过调整模仿策略与AI普及率，发现AI驱动的模仿效果高度依赖上下文：在初始同质环境中，模仿型AI代理可增加语义多样性；但在异质环境中，反而会降低多样性。这一模式在多个LLM中保持定性一致。然而，这种多样性主要源于风格分化和方差压缩，而非事实层面的丰富，因为AI生成的文章往往省略信息，却仍保持语义区分度。研究表明，AI驱动的模仿对信息环境产生双重影响，可能影响民主社会的集体智能。
原文摘要 · Abstract (English)
Recent developments in large language models (LLMs) have facilitated autonomous AI agents capable of imitating human-generated content, raising fundamental questions about how AI may reshape democratic information environments such as news. We develop a large-scale simulation framework to examine the system-level effects of AI-based imitation, using the full population of Danish digital news articles published in 2022. Varying imitation strategies and AI prevalence across information environments with different baseline structures, we show that the effects of AI-driven imitation are strongly context-dependent: imitating AI agents increase semantic diversity in initially homogeneous environments but can reduce diversity in heterogeneous ones. This pattern is qualitatively consistent across multiple LLMs. However, this diversity arises primarily through stylistic differentiation and variance compression rather than factual enrichment, as AI-generated articles tend to omit information while remaining semantically distinct. These findings indicate that AI-driven imitation produces ambivalent transformations of information environments that may shape collective intelligence in democratic societies.
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