Eawag: Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland E-mail:
Water Sci Technol. 2020 Apr;81(8):1588-1596. doi: 10.2166/wst.2020.032.
Uncertainty quantification is very important in environmental management to allow decision makers to consider the reliability of predictions of the consequences of decision alternatives and relate them to their risk attitudes and the uncertainty about their preferences. Nevertheless, uncertainty quantification in environmental decision support is often incomplete and the robustness of the results regarding assumptions made for uncertainty quantification is often not investigated. In this article, an attempt is made to demonstrate how uncertainty can be considered more comprehensively in environmental research and decision support by combining well-established with rarely applied statistical techniques. In particular, the following elements of uncertainty quantification are discussed: (i) using stochastic, mechanistic models that consider and propagate uncertainties from their origin to the output; (ii) profiting from the support of modern techniques of data science to increase the diversity of the exploration process, to benchmark mechanistic models, and to find new relationships; (iii) analysing structural alternatives by multi-model and non-parametric approaches; (iv) quantitatively formulating and using societal preferences in decision support; (v) explicitly considering the uncertainty of elicited preferences in addition to the uncertainty of predictions in decision support; and (vi) explicitly considering the ambiguity about prior distributions for predictions and preferences by using imprecise probabilities. In particular, (v) and (vi) have mostly been ignored in the past and a guideline is provided on how these uncertainties can be considered without significantly increasing the computational burden. The methodological approach to (v) and (vi) is based on expected expected utility theory, which extends expected utility theory to the consideration of uncertain preferences, and on imprecise, intersubjective Bayesian probabilities.
不确定性量化在环境管理中非常重要，可使决策者考虑到决策替代方案后果预测的可靠性，并将其与他们的风险态度和对偏好的不确定性联系起来。然而，环境决策支持中的不确定性量化通常是不完整的，并且对于不确定性量化所做假设的结果稳健性通常也没有进行调查。本文试图通过结合成熟的和很少应用的统计技术，展示如何在环境研究和决策支持中更全面地考虑不确定性。特别是，讨论了以下不确定性量化的要素：(i) 使用随机的、机械的模型，这些模型考虑并传播从其起源到输出的不确定性；(ii) 利用现代数据科学技术的支持，增加探索过程的多样性，对机械模型进行基准测试，并找到新的关系；(iii) 通过多模型和非参数方法分析结构替代方案；(iv) 在决策支持中定量地形成和使用社会偏好；(v) 在决策支持中除了考虑预测的不确定性之外，还明确考虑到被诱发偏好的不确定性；(vi) 通过使用不精确概率，明确考虑到对预测和偏好的先验分布的不确定性。特别是，(v) 和 (vi) 在过去大多被忽视，提供了一个如何在不显著增加计算负担的情况下考虑这些不确定性的指导方针。(v) 和 (vi) 的方法学方法基于期望效用理论，该理论将期望效用理论扩展到对不确定偏好的考虑，以及不精确的、主体间的贝叶斯概率。
