AI 代理的信息聚合

Economics > General Economics arXiv:2604.20050 (econ) [Submitted on 21 Apr 2026 (v1), last revised 25 Sep 2026 (this version, v4)] Title:Information Aggregation with AI Agents Authors:Spyros Galanis View a PDF of the paper titled Information Aggregation with AI Agents, by Spyros Galanis View PDF HTML (experimental) Abstract:Can Large Language Models (AI agents) aggregate dispersed private information through trading and reason about the knowledge of others by observing price movements? We conduct a controlled experiment where AI agents trade in a prediction market after receiving private signals, across four information structures of increasing complexity. We find that although the median market is effective at aggregating information in the easy information structures, performance deteriorates in the harder structures, suggesting that AI agents struggle in environments where more than two levels of interactive reasoning are required, a ceiling close to the one documented in human subjects. Consistent with our theoretical predictions, market accuracy does not improve from allowing cheap talk communication, changing the duration of the market, or strategic prompting; initial price has little average effect but matters in the very hard structure. We also find that ``smarter'' AI agents perform better at aggregation and are more profitable. Surprisingly, giving them feedback about past performance does not improve aggregation. A further wave of markets, run three months later with capability-frontier models, aggregates information more often in the three easier structures but not in the hardest one, where higher capability replaces markets that are confidently wrong with markets that hedge near 0.5. Comments: Subjects: General Economics (econ.GN); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT) Cite as: arXiv:2604.20050 [econ.GN] (or arXiv:2604.20050v4 [econ.GN] for this version) https://doi.org/10.48550/arXiv.2604.20050 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Spyros Galanis Dr [view email] [v1] Tue, 21 Apr 2026 23:17:24 UTC (901 KB) [v2] Thu, 7 May 2026 14:26:43 UTC (898 KB) [v3] Wed, 22 Jul 2026 11:41:57 UTC (901 KB) [v4] Fri, 25 Sep 2026 11:24:10 UTC (501 KB) Full-text links: Access Paper: View a PDF of the paper titled Information Aggregation with AI Agents, by Spyros Galanis View PDF HTML (experimental) TeX Source view license Current browse context: econ.GN new | recent | 2026-04 Change to browse by: cs cs.AI cs.GT econ q-fin q-fin.EC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)