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Clarifying Types of Uncertainty: When Are Models Accurate, and Uncertainties Small?

机译:澄清不确定性的类型:模型何时准确且不确定性很小?

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Professor Aven has recently noted the importance of clarifying the meaning of terms such as "scientific uncertainty" for use in risk management and policy decisions, such as when to trigger application of the precautionary principle. This comment examines some fundamental conceptual challenges for efforts to define "accurate" models and "small" input uncertainties by showing that increasing uncertainty in model inputs may reduce uncertainty in model outputs; that even correct models with "small" input uncertainties need not yield accurate or useful predictions for quantities of interest in risk management (such as the duration of an epidemic); and that accurate predictive models need not be accurate causal models.
机译:Aven教授最近指出了澄清术语“科学不确定性”的含义在风险管理和政策决策(例如何时触发实施预防原则)中使用的重要性。该评论通过显示模型输入中不确定性的增加可以减少模型输出中的不确定性,研究了定义“准确”模型和“较小”输入不确定性方面的一些基本概念挑战。即使输入不确定性很小的正确模型也不需要对风险管理中的关注数量(例如流行病的持续时间)产生准确或有用的预测;准确的预测模型不必是准确的因果模型。

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