ArticleScience advances2019
How to detect high-performing individuals and groups: Decision similarity predicts accuracy.
Article in Science advances, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Human pairs show collective benefit in olfactory perception despite individual differences and verbal limits.iScience · 2026Article
- Enhancing skin lesion classification: a CNN approach with human baseline comparison.PeerJ. Computer science · 2025Article
- How large language models can reshape collective intelligence.Nature human behaviour · 2024Review
- Boosting wisdom of the crowd for medical image annotation using training performance and task features.Cognitive research: principles and implications · 2024Article
- Automating hybrid collective intelligence in open-ended medical diagnostics.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- How experts' own inconsistency relates to their confidence and between-expert disagreement.Scientific reports · 2022Article
- Crowd control: Reducing individual estimation bias by sharing biased social information.PLoS computational biology · 2021Article
- Article
- Are They Really Trying to Save Their Buddy? The Anthropomorphism of Animal Epimeletic Behaviours.Animals : an open access journal from MDPI · 2020Article
- Strategies for integrating disparate social information.Proceedings. Biological sciences · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Distinguishing between high- and low-performing individuals and groups is of prime importance in a wide range of high-stakes contexts. While this is straightforward when accurate records of past performance exist, these records are unavailable in most real-world contexts. Focusing on the class of binary decision problems, we use a combined theoretical and empirical approach to develop and test a approach to this important problem. First, we use a general mathematical argument and numerical simulations to show that the similarity of an individual's decisions to others is a powerful predictor of that individual's decision accuracy. Second, testing this prediction with several large datasets on breast and skin cancer diagnostics, geopolitical forecasting, and a general knowledge task, we find that decision similarity robustly permits the identification of high-performing individuals and groups. Our findings offer a simple, yet broadly applicable, heuristic for improving real-world decision-making systems.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.