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Social Influence and the Allocation of Scientific Attention in AI Populations — arXiv:2609.22408

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.22408
License (as archived): CC0-1.0
Archived: 2026-10-09T06:59:12.528Z
SHA-256 of the archived text: 38f3844c7d360b24728c7a1dcda4ddd3a1fb07119e4e8a892466431f952412bd
Stable URL of this page: https://metraipolis.com/documento/dc_5feed36085ed

How to cite

Social Influence and the Allocation of Scientific Attention in AI Populations — arXiv:2609.22408 — arXiv (en), https://arxiv.org/abs/2609.22408 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_5feed36085ed (Metraipolis archive dc_5feed36085ed, sha256 38f3844c7d360b24728c7a1dcda4ddd3a1fb07119e4e8a892466431f952412bd)

BibTeX

@misc{dc5feed36085ed,
  title = {Social Influence and the Allocation of Scientific Attention in AI Populations — arXiv:2609.22408},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.22408},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_5feed36085ed; sha256 38f3844c7d360b24728c7a1dcda4ddd3a1fb07119e4e8a892466431f952412bd},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.22408. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: economia dei servizi IA (retro-catalogo arXiv: annunci del 2026-09-22 in econ.GN).
Categorie arXiv: Artificial Intelligence (cs.AI) ; General Economics (econ.GN).
Pagina del lavoro: https://arxiv.org/abs/2609.22408 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T06:52:42.697Z dalla pagina https://arxiv.org/catchup/econ.GN/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).