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Groups of diverse problem solvers can outperform groups of high-ability problem solvers
Open accessAbstract
We introduce a general framework for modeling functionally diverse problem-solving agents. In this framework, problem-solving agents possess representations of problems and algorithms that they use to locate solutions. We use this framework to establish a result relevant to group composition. We find that when selecting a problem-solving team from a diverse population of intelligent agents, a team of randomly selected agents outperforms a team comprised of the best-performing agents. This result relies on the intuition that, as the initial pool of problem solvers becomes large, the best-performing agents necessarily become similar in the space of problem solvers. Their relatively greater ability is more than offset by their lack of problem-solving diversity.
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“Jury Theorems argue that under certain conditions, a diverse set of users can arrive at better solutions than experts (Hong & Page, 2004;Dietrich & Spiekermann, 2021).”
From On Epistemic Diversity in Large Language Models (2026) · cites this paper as Hong & Page, 2004
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“By adding members with different life experiences and cognitive problem-solving skills, and given favourable conditions, diverse groups may even outperform experts (Hong and Page 2004).”
From Institutionalised distrust and human oversight of artificial intelligence: towards a democratic design of AI governance under the European Union AI Act (2024) · cites this paper as Hong and Page 2004
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Metadata
- Authors
- Hong, L., & Page, S. E.
- Affiliations
- Loyola University Chicago (US)
- Corresponding author
- Hong, Lu
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2004-11-08
- Volume
- 101
- Issue
- 46
- Pages
- 16385-16389
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC528939
- OpenAlex
- W2158392329
- Cited by
- 2,103
- References
- 10
- Citations by year
- 2026: 168 · 2025: 183 · 2024: 197 · 2023: 215 · 2022: 189 · 2021: 175 · 2020: 157 · 2019: 127 · 2018: 154 · 2017: 84 · 2016: 87 · 2015: 77 · 2014: 63 · 2013: 54 · 2012: 47
- FWCI
- 9.73
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Team Dynamics and Performance
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Distributed Control Multi-Agent Systems
- Keywords
- group composition, team performance, problem-solving agents, agent selection, problem representation, algorithmic search
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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