University of Stavanger, 4036 Stavanger, Norway.
Risk Anal. 2010 Mar;30(3):354-60; author reply 381-4. doi: 10.1111/j.1539-6924.2009.01314.x. Epub 2009 Nov 16.
It is common perspective in risk analysis that there are two kinds of uncertainties: i) variability as resulting from heterogeneity and stochasticity (aleatory uncertainty) and ii) partial ignorance or epistemic uncertainties resulting from systematic measurement error and lack of knowledge. Probability theory is recognized as the proper tool for treating the aleatory uncertainties, but there are different views on what is the best approach for describing partial ignorance and epistemic uncertainties. Subjective probabilities are often used for representing this type of ignorance and uncertainties, but several alternative approaches have been suggested, including interval analysis, probability bound analysis, and bounds based on evidence theory. It is argued that probability theory generates too precise results when the background knowledge of the probabilities is poor. In this article, we look more closely into this issue. We argue that this critique of probability theory is based on a conception of risk assessment being a tool to objectively report on the true risk and variabilities. If risk assessment is seen instead as a method for describing the analysts' (and possibly other stakeholders') uncertainties about unknown quantities, the alternative approaches (such as the interval analysis) often fail in providing the necessary decision support.
在风险分析中，人们普遍认为存在两种不确定性：i）由异质性和随机性（或然不确定性）导致的可变性；ii）由于系统测量误差和知识缺乏而导致的部分无知或认知不确定性。概率论被认为是处理或然不确定性的适当工具，但对于如何描述部分无知和认知不确定性，存在不同的观点。主观概率常用于表示这种类型的无知和不确定性，但也提出了几种替代方法，包括区间分析、概率界限分析和基于证据理论的界限。有人认为，当概率的背景知识较差时，概率论会产生过于精确的结果。本文更深入地探讨了这个问题。我们认为，这种对概率论的批评是基于风险评估被视为客观报告真实风险和可变性的工具的观念。如果将风险评估视为描述分析师（和可能的其他利益相关者）对未知量的不确定性的方法，那么替代方法（如区间分析）通常无法提供必要的决策支持。
