An improved framework for uncertainty analysis: Accounting for unsuspected errors

Shlyakhter, AI

HERO ID

1060859

Reference Type

Journal Article

Year

1994

Language

English

HERO ID 1060859
In Press No
Year 1994
Title An improved framework for uncertainty analysis: Accounting for unsuspected errors
Authors Shlyakhter, AI
Journal Risk Analysis
Volume 14
Issue 4
Page Numbers 441-447
Abstract I use an analogy with the history of physical measurements, population and energy projections, and analyze the trends in several data sets to quantify the overconfidence of the experts in the reliability of their uncertainty estimates. Data sets include (i) time trends in the sequential measurements of the same physical quantity; (ii) national population projections; and (iii) projections for the U.S., energy sector. Probabilities of large deviations for the true values are parametrized by an exponential distribution with the slope determined by the data. Statistics of past errors can be used in probabilistic risk assessment to hedge against unsuspected uncertainties and to include the possibility of human error into the framework of uncertainty analysis. By means of a sample Monte Carlo simulation of cancer risk caused by ingestion of benzene in soil, I demonstrate how the upper 95th percentiles of risk are changed when unsuspected uncertainties are included. I recommend to inflate the estimated uncertainties by default safety factors determined from the relevant historical data sets.
Doi 10.1111/j.1539-6924.1994.tb00262.x
Url https://www.proquest.com/scholarly-journals/improved-framework-uncertainty-analysis/docview/16750706/se-2?accountid=171501
Is Certified Translation No
Dupe Override No
Is Public Yes
Language Text English
Keyword uncertainty; Monte Carlo simulation; Risk Abstracts; benzene; soil contamination; statistical analysis; ingestion; public health; R2 23060:Medical and environmental health