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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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