Insights into the quantification and reporting of model-related uncertainty across different disciplines

Insights into the quantification and reporting of model-related uncertainty across different disciplines

Summary: Quantifying uncertainty associated with our models is the only way we can express how much we know about any phenomenon. Incomplete consideration of model-based uncertainties can lead to overstated conclusions with real-world impacts in diverse spheres, including conservation, epidemiology,...

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Bibliographic Details
Main Authors: Emily G. Simmonds, Kwaku Peprah Adjei, Christoffer Wold Andersen, Janne Cathrin Hetle Aspheim, Claudia Battistin, Nicola Bulso, Hannah M. Christensen, Benjamin Cretois, Ryan Cubero, Iván A. Davidovich, Lisa Dickel, Benjamin Dunn, Etienne Dunn-Sigouin, Karin Dyrstad, Sigurd Einum, Donata Giglio, Haakon Gjerløw, Amélie Godefroidt, Ricardo González-Gil, Soledad Gonzalo Cogno, Fabian Große, Paul Halloran, Mari F. Jensen, John James Kennedy, Peter Egge Langsæther, Jack H. Laverick, Debora Lederberger, Camille Li, Elizabeth G. Mandeville, Caitlin Mandeville, Espen Moe, Tobias Navarro Schröder, David Nunan, Jorge Sicacha-Parada, Melanie Rae Simpson, Emma Sofie Skarstein, Clemens Spensberger, Richard Stevens, Aneesh C. Subramanian, Lea Svendsen, Ole Magnus Theisen, Connor Watret, Robert B. O’Hara
Format: Article
Language:English
Published: Elsevier 2022-12-01
Series:iScience
Subjects:
Statistical physics
Online Access:http://www.sciencedirect.com/science/article/pii/S2589004222017849

Internet

http://www.sciencedirect.com/science/article/pii/S2589004222017849

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