Social Influence and the Allocation of Scientific Attention in AI Populations

2026-09-18

AI Summary

研究通过模拟AI代理选择论文的过程，探讨社会影响如何改变科学关注的分配，发现社会信息减少了个体选择多样性并增加了初始被选论文的后续选择率。

Abstract

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.

Problem

• Social Influence
• Scientific Attention
• AI Systems
• Academic Market
• Citation Counts

Innovation

• social influence
• artificial intelligence
• academic attention
• information rule
• selection behavior