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Consequence Modeling Lack of Accuracy and Its Effect on Quantitative Risk Analysis Results

机译:结果建模的准确性及其对定量风险分析结果的影响

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Making an order of magnitude error in consequence modeling has a far GREATER effect on QRA results than making a similar order of magnitude error in probability estimates. This is the case since, in the process of obtaining QRA results, small variations in consequence model outputs are multiplied thousands of times. This very large effect on QRA results remains true irrespective of the actual consequence models used in deriving the risk results- including the type, characteristics, features, or other details of such models.rnAs an important safety decision-making tool where the fate of multi-billion dollar projects hinges, and where multi-million dollar safety recommendations are generated to mitigate calculated risks; one would expect QRAs to be subject to some sort of minimum performance-criteria of their associated consequence modeling packages. This, however, is not often the case.
机译:与在概率估计中进行相似数量级误差相比,在后果模型中进行数量级误差对QRA结果的影响更大。之所以如此,是因为在获得QRA结果的过程中,结果模型输出的微小变化被成千上万倍。不管用于得出风险结果的实际后果模型(包括此类模型的类型,特征,特征或其他细节)如何,对QRA结果的巨大影响仍然是正确的。作为重要的安全决策工具,数十亿美元的项目,取决于枢纽,并在其中产生了数百万美元的安全建议以减轻计算得出的风险;人们会期望QRA会受到与其关联的结果建模包的某种最低性能标准的约束。但是,这种情况并不常见。

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