[Submitted on 18 Sep 2026]
Title:The Wisdom of Artificial Deliberative Crowds
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Abstract:The aggregation of many lay estimates often outperforms individual expert judgment, a phenomenon known as the wisdom of crowds. While this is usually attributed to the independence of estimates, an even stronger effect arises through deliberation: averaging the consensus estimates of small deliberating groups outperforms the classical wisdom of crowds, with individual judgments themselves also becoming more accurate after deliberation. Whether these improvements transfer to large language models deliberating amongst themselves is unknown. Here we adapt a three-stage deliberation paradigm previously used with human participants for use with large language models from three different families, and test it across four domains of increasing real-world stakes: visual numerical estimation (Study 1), peer review of machine-learning papers (Study 2), detection of hidden malicious behavior by an artificial intelligence agent (Study 3), and sports forecasting against a real prediction market (Study 4). Across domains, deliberation reduced collective error beyond passive aggregation of independent responses, and post-deliberation individual judgments retained this collective gain. Notably, the advantage required model diversity: groups composed of clones of a single model did not benefit from deliberating. These results establish machine deliberation as a general-purpose aggregation mechanism, and point to diversity as an active ingredient.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.22497 [cs.AI]
(or arXiv:2609.22497v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.22497
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Joaquin Navajas [view email] [v1] Fri, 18 Sep 2026 19:01:43 UTC (1,323 KB)
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View a PDF of the paper titled The Wisdom of Artificial Deliberative Crowds, by Federico Barrera-Lemarchand and 2 other authors
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