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Accommodating Uncertainty in Comparative Risk

机译：适应比较风险的不确定性

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摘要

Comparative risk projects can provide broad policy guidance but they rarely have adequate scientific foundations to support precise risk rankings. Many extant projects report rankings anyway, with limited attention to uncertainty. Stochastic uncertainty, structural uncertainty, and ignorance are types of incertitude that afflict risk comparisons. The recently completed New Jersey Comparative Risk Project was innovative in trying to acknowledge and accommodate some historically ignored uncertainties in a substantive manner. This article examines the methods used and lessons learned from the New Jersey project. Monte Carlo techniques were used to characterize stochastic uncertainty, and sensitivity analysis helped to manage structural uncertainty. A deliberative process and a sorting technique helped manage ignorance. Key findings are that stochastic rankings can be calculated but they reveal such an alarming degree of imprecision that the rankings are no longer useful, whereas sorting techniques are helpful in spite of uncertainty. A deliberative process is helpful to counter analytical overreaching.
机译：比较风险项目可以提供广泛的政策指导，但很少有足够的科学基础来支持精确的风险排名。无论如何，许多现存的项目都会报告排名，对不确定性的关注有限。随机不确定性，结构不确定性和无知是影响风险比较的不确定性类型。最近完成的新泽西州比较风险项目在尝试以实质性方式承认和解决一些历史上被忽略的不确定性方面具有创新性。本文研究了从新泽西项目中使用的方法和经验教训。蒙特卡洛技术用于表征随机不确定性，而敏感性分析则有助于管理结构不确定性。协商过程和分类技术有助于管理无知。主要发现是，可以计算随机排名，但它们显示出令人震惊的不精确度，排名不再有用，而排序技术尽管有不确定性，但还是很有用的。审议过程有助于消除分析上的超越。

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