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

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 likely accura...

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Detalles Bibliográficos
Autores principales: Gruen, Alexander, Mattingly, Karl R., Morwitch, Ellen, Bossaerts, Frederik, Clifford, Manning, Nash, Chad, Ioannidis, John P.A., Ponsonby, Anne-Louise
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10502359/
https://www.ncbi.nlm.nih.gov/pubmed/37708701
http://dx.doi.org/10.1016/j.ebiom.2023.104783

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10502359/
https://www.ncbi.nlm.nih.gov/pubmed/37708701
http://dx.doi.org/10.1016/j.ebiom.2023.104783

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