Details

Autor(en) / Beteiligte

• Simmonds, Emily G.
• Adjei, Kwaku Peprah
• Andersen, Christoffer Wold
• Hetle Aspheim, Janne Cathrin
• Battistin, Claudia
• Bulso, Nicola
• Christensen, Hannah M.
• Cretois, Benjamin
• Cubero, Ryan
• Davidovich, Iván A.
• Dickel, Lisa
• Dunn, Benjamin
• Dunn-Sigouin, Etienne
• Dyrstad, Karin
• Einum, Sigurd
• Giglio, Donata
• Gjerløw, Haakon
• Godefroidt, Amélie
• González-Gil, Ricardo
• Gonzalo Cogno, Soledad
• Große, Fabian
• Halloran, Paul
• Jensen, Mari F.
• Kennedy, John James
• Langsæther, Peter Egge
• Laverick, Jack H.
• Lederberger, Debora
• Li, Camille
• Mandeville, Elizabeth G.
• Mandeville, Caitlin
• Moe, Espen
• Navarro Schröder, Tobias
• Nunan, David
• Sicacha-Parada, Jorge
• Simpson, Melanie Rae
• Skarstein, Emma Sofie
• Spensberger, Clemens
• Stevens, Richard
• Subramanian, Aneesh C.
• Svendsen, Lea
• Theisen, Ole Magnus
• Watret, Connor
• O’Hara, Robert B.

Titel

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

Ist Teil von

• iScience, 2022-12, Vol.25 (12), p.105512-105512, Article 105512

Ort / Verlag

Elsevier Inc

Erscheinungsjahr

2022

Quelle

Electronic Journals Library

Beschreibungen/Notizen

• 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, climate science, and policy. Despite these potentially damaging consequences, we still know little about how different fields quantify and report uncertainty. We introduce the “sources of uncertainty” framework, using it to conduct a systematic audit of model-related uncertainty quantification from seven scientific fields, spanning the biological, physical, and political sciences. Our interdisciplinary audit shows no field fully considers all possible sources of uncertainty, but each has its own best practices alongside shared outstanding challenges. We make ten easy-to-implement recommendations to improve the consistency, completeness, and clarity of reporting on model-related uncertainty. These recommendations serve as a guide to best practices across scientific fields and expand our toolbox for high-quality research. Statistical physics