From Models To Decisions (M2D)

从模型到决策 (M2D)

• 批准号: EP/P016774/1
• 负责人: Peter Challenor
• 金额: $ 44.34万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FP016774%2F1
• 关键词: Models; Decisions; M2D

Today many decisions are made using evidence from numerical models. Such models are often large and complex and often appear to be without uncertainty. However, even if the model is itself deterministic in that it gives the same result for the same set of inputs, the inputs are almost always uncertain and the model structure itself is not fully known. Thus any model result is uncertain. Determining the uncertainties in models is often referred to as uncertainty quantification. This is a hard problem; even for the forward problem where we know the uncertainty in the model inputs and we want to propagate it to the model outputs. Even harder is inverse modelling when (uncertain) observations are used to reduce the uncertainty in the inputs. Once we have done both inverse and forward modelling we can estimate the uncertainty in the model output that is being used to help make the decision. However, in any decision the evidence from the model is rarely the only consideration. The decision maker has his or her own uncertainties and biases and these must be taken into account and understood. Two decision makers given the same evidence will often make different decisions. Presenting the evidence to decision makers (often untrained in mathematics and statistics) is difficult and complex. The network will look at new ways of presenting narratives and visualisations of uncertainty to decision makers.

今天，许多决策都是使用数值模型的证据来做出的。这种模型往往庞大而复杂，而且往往看起来没有不确定性。然而，即使模型本身是确定性的，因为它对同一组输入给出相同的结果，输入几乎总是不确定的，并且模型结构本身并不完全已知。因此，任何模型结果都是不确定的。确定模型中的不确定性通常被称为不确定性量化。这是一个难题;即使对于正问题，我们知道模型输入中的不确定性，并且我们希望将其传播到模型输出。当（不确定的）观测被用来减少输入的不确定性时，逆建模更难。一旦我们完成了逆向和正向建模，我们就可以估计用于帮助做出决策的模型输出的不确定性。然而，在任何决策中，来自模型的证据很少是唯一的考虑因素。决策者有他或她自己的不确定性和偏见，这些都必须考虑和理解。两个决策者得到相同的证据往往会做出不同的决定。向决策者（往往未受过数学和统计方面的训练）提供证据是困难和复杂的。该网络将研究向决策者呈现不确定性的叙述和可视化的新方法。

项目成果

Modulation of the relationship between summer temperatures in the Qinghai-Tibetan Plateau and Arctic over the past millennium by external forcings

近千年来青藏高原与北极夏季气温受外强迫作用的调节关系

• DOI: 10.1017/qua.2021.3
• 发表时间: 2021
• 期刊: Quaternary Research
• 影响因子: 2.3
• 作者: Shi Feng;Duan Anmin;Yin Qiuzhen;Bruun John T.;Xiao Cunde;Guo Zhengtang
• 通讯作者: Guo Zhengtang

Seasonality of Oithona similis and Calanus helgolandicus reproduction and abundance: contrasting responses to environmental variation at a shelf site

• DOI: 10.1093/plankt/fby007
• 发表时间: 2018-05-01
• 期刊: JOURNAL OF PLANKTON RESEARCH
• 影响因子: 2.1
• 作者: Cornwell, L. E.;Findlay, H. S.;Atkinson, A.
• 通讯作者: Atkinson, A.

A Mechanism for Pacific Interdecadal Resonances

• DOI: 10.1029/2018jc013752
• 发表时间: 2018-09
• 期刊: Journal of Geophysical Research: Oceans
• 作者: J. Skákala;J. Bruun

Probabilistic Landslide-Generated Tsunamis in the Indus Canyon, NW Indian Ocean, Using Statistical Emulation

使用统计模拟研究印度洋西北部印度河峡谷发生山体滑坡概率引发的海啸

• DOI: 10.1007/s00024-019-02187-3
• 发表时间: 2019
• 期刊: Pure and Applied Geophysics
• 影响因子: 2
• 作者: Salmanidou D

Rheological considerations for the modelling of submarine sliding at Rockall Bank, NE Atlantic Ocean

• DOI: 10.1063/1.5009552
• 发表时间: 2018-02
• 期刊: Physics of Fluids
• 影响因子: 4.6
• 作者: D. Salmanidou;A. Georgiopoulou;S. Guillas;F. Dias