How do we know how much we know? Quantifying uncertainty associated with our modelling work is the only way we can answer how much we know about any phenomenon. With quantitative science now highly influential in the public sphere and the results from models translating into action, we must support our conclusions with sufficient rigour to produce useful, reproducible results. Incomplete consideration of model-based uncertainties can lead to false conclusions with real world impacts. Despite these potentially damaging consequences, uncertainty consideration is incomplete both within and across scientific fields. We take a unique interdisciplinary approach and conduct a systematic audit of model-related uncertainty quantification from seven scientific fields, spanning the biological, physical, and social sciences. Our results show no single field is achieving complete consideration of model uncertainties, but together we can fill the gaps. We propose opportunities to improve the quantification of uncertainty through use of a source framework for uncertainty consideration, model type specific guidelines, improved presentation, and shared best practice. We also identify shared outstanding challenges (uncertainty in input data, balancing trade-offs, error propagation, and defining how much uncertainty is required). Finally, we make nine concrete recommendations for current practice (following good practice guidelines and an uncertainty checklist, presenting uncertainty numerically, and propagating model-related uncertainty into conclusions), future research priorities (uncertainty in input data, quantifying uncertainty in complex models, and the importance of missing uncertainty in different contexts), and general research standards across the sciences (transparency about study limitations and dedicated uncertainty sections of manuscripts).
翻译:我们如何知道我们知道我们知道的多少? 量化与我们的建模工作有关的不确定性是我们能够回答我们对任何现象了解多少的唯一方法。由于定量科学现在在公共领域具有很大影响力,而且模型转化为行动,我们必须以足够严谨的方式支持我们的结论,以便产生有用的、可复制的结果。对基于模型的不确定性的考虑不彻底,会导致错误的结论,并产生真实世界的影响。尽管存在这些潜在的破坏性后果,但不确定性的考虑在科学领域内部和跨科学领域之间都是不完全的。我们采取独特的跨学科方法,对七个科学领域,跨越生物、物理和社会科学的与模型有关的不确定性量化进行系统审计。我们的结果显示,没有一个单一的领域是完全考虑模型不确定性,但我们可以一起填补这些空白。我们提出机会,通过使用不确定性的源框架,模型类型特定准则、改进的介绍和共同的最佳做法来改进不确定性的量化。我们还确定了共同面临的共同挑战(投入数据不确定性、平衡交易、错误传播以及确定需要多少不确定性)。 最后,我们提出了九项具体建议,用于当前的做法(在复杂的科学背景中遵循良好做法准则,提出复杂的不确定性,在复杂的研究中提出不确定性,在复杂的不确定性方面提出数据清单结论中提出不确定性,在与数据上提出各种的不确定性方面提出各种的不确定性,在与数据分析中提出各种的不确定性的不确定性方面提出,在与数据分析中提出,在与数据上提出一个与不确定性的结论性分析中提出不确定性的结论性标准,在与数据上提出不确定性的结论。