社会影响与AI群体中科学注意力的分配
Social Influence and the Allocation of Scientific Attention in AI Populations
- University of Oxford（牛津大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过两个实验表明，社会影响信号显著减少AI智能体选择的论文数量、增加选择集中度，并提高随机论文的后续选择率，塑造了科学注意力的分配。
AI中文摘要：
AI系统正成为科学研究评估与使用的参与者。它们会接触到围绕人类读者开发的引用计数、下载统计和热门文章列表，但这些信号对人工读者的集体后果仍不确定。本文将音乐实验室设计应用于学术注意力市场。在第一个实验中，1000个AI智能体从《美国经济评论》2025年发表的全部114篇常规研究文章的标题和摘要中选择论文。实验包含五个独立选择社区和五个社会影响社区，每个社区有100个顺序智能体。只有社会影响条件下的智能体观察其社区内的先前选择。智能体可选择任意数量的论文。社会信息社区每个智能体选择的论文数量少17.2%，选择更集中，共覆盖73篇论文，而独立选择社区覆盖90篇。在社会信息下，社区间差异更大。在第二个实验中，200个智能体分布在二十个社会社区，随机给论文分配五次初始选择，使其后续选择率提高45.55个百分点（95%置信区间：41.20至49.90）。选择与外部引用有适度对应，与下载计数对应很少。结果表明，一个简单的信息规则如何塑造人工群体中科学注意力的数量、广度和分布。
英文摘要：
AI systems are becoming participants in the evaluation and use of scientific research. They encounter citation counts, download statistics and lists of popular articles developed around human readers, but the collective consequences of these signals for artificial readers remain uncertain. This paper adapts the Music Lab design to a market for academic attention. In the first experiment, 1,000 AI agents choose papers from the titles and abstracts of all 114 regular research articles published in the American Economic Review in 2025. The experiment has five independent-choice communities and five social-influence communities, each with 100 sequential agents. Only agents in the social-influence condition observe earlier selections within their community. Agents may select any number of papers. Social-information communities select 17.2 percent fewer papers per agent, concentrate their choices more heavily, and collectively cover 73 papers, compared with 90 independently. Between-community variation is greater under social information. In a second experiment with 200 agents across twenty social communities, randomly assigning papers five initial selections raises their subsequent selection rate by 45.55 percentage points (95% CI: 41.20 to 49.90). Choices have modest correspondence with external citations and little correspondence with download counts. The results show how a simple information rule shapes the volume, breadth and distribution of scientific attention in an artificial population.
