ArticleScientific reports2025
When the crowd gets it wrong - the limits of collective wisdom in machine learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This study examines collective decision-making dynamics using a machine learning framework, drawing parallels between a previously established synthetic population model and a newly introduced ensemble machine learning counterpart. Grounded in the "wisdom of crowds" principle, the research explores scenarios where the accuracy of group decisions may unexpectedly decrease as group size increases, particularly when individuals share highly correlated information. By replicating these conditions with machine learning ensembles, such as decision trees and support vector machines, the study identifies circumstances where collective accuracy declines, challenging the assumption that larger groups inherently make better decisions. The findings reveal the limitations of collective models in machine learning and provide valuable insights for data-scarce environments.
Identifiers
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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.