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
Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Trøndelag 7034, Norway.
The Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim, Trøndelag 7491, Norway.
iScience. 2022 Nov 5;25(12):105512. doi: 10.1016/j.isci.2022.105512. eCollection 2022 Dec 22.
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.
量化与我们的模型相关的不确定性是我们能够表达对任何现象了解程度的唯一途径。对基于模型的不确定性考虑不全面可能会导致在包括保护、流行病学、气候科学和政策等不同领域得出夸大的结论，这些结论会对现实世界产生影响。尽管存在这些潜在的破坏性后果，但我们对不同领域如何量化和报告不确定性仍然知之甚少。我们引入了“不确定性来源”框架，并用它对来自生物、物理和政治科学等七个科学领域的与模型相关的不确定性量化进行了系统审查。我们的跨学科审查表明，没有一个领域能充分考虑所有可能的不确定性来源，但每个领域都有自己的最佳实践以及共同面临的突出挑战。我们提出了十条易于实施的建议，以提高与模型相关的不确定性报告的一致性、完整性和清晰度。这些建议可作为各科学领域最佳实践的指南，并扩展了我们进行高质量研究的工具库。
