Machine learning augmentation reduces prediction error in collective forecasting: development and validation across prediction markets with application to COVID eventsResearch in context

Machine learning augmentation reduces prediction error in collective forecasting: development and validation across prediction markets with application to COVID eventsResearch in context

Summary: Background: The recent COVID-19 pandemic highlighted the challenges for traditional forecasting. Prediction markets are a promising way to generate collective forecasts and could potentially be enhanced if high-quality crowdsourced inputs were identified and preferentially weighted for lik...

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Bibliographic Details
Main Authors: Alexander Gruen, Karl R. Mattingly, Ellen Morwitch, Frederik Bossaerts, Manning Clifford, Chad Nash, John P.A. Ioannidis, Anne-Louise Ponsonby
Format: Article
Language:English
Published: Elsevier 2023-10-01
Series:EBioMedicine
Subjects:
Collective intelligence
Forecast accuracy
Prediction market
Human-machine forecast
COVID
Online Access:http://www.sciencedirect.com/science/article/pii/S2352396423003493

Internet

http://www.sciencedirect.com/science/article/pii/S2352396423003493

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