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Page Title: Assessing the Impact of Uncertainty on Prioritization.
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express the variability by graphing accident frequency, likelihood, or probability as
a function of the number of fatalities, the number of cancer occurrences, or
another measure of accident consequences. Such variabilities are not typically
considered to be uncertainties; rather the term " ncertainty"refers to the fact that
either the risk model is not exact (modeling uncertainty) or that the parameters of
the input probability distributions are not exactly known (parameter uncertainty).
The RBP system and its underlying risk assessments should normally address
b. Types of Uncertainty. Two basic types of uncertainty within the RBP system
should be recognized:
1. Modeling uncertainty. The RBP system may produce inaccurate results if
important performance measures have been omitted from the aggregation
equation or if important dependencies among performance measures have not
been considered. Modeling uncertainty is difficult to assess quantitatively and
is frequently addressed by ensuring that the model is as complete as possible.
Bounding and/or subjective modeling techniques can be employed, when
warranted, to explore the quantitative implications of model limitations in the
more sophisticated applications of RBP. Documentation of a prioritization
project should explain the basis for selecting the decision analysis process
used in the project, including a rationale for including each performance
2. Parameter Uncertainty. The RBP system may produce inaccurate results if the
underlying data and information supplied to the aggregation equation is itself
uncertain. For example, the benefits or costs associated with a particular
decision option may not be exactly known; such information may be
communicated by providing (a) a range of values (that is, a minimum and a
maximum value) or (b) a probability distribution function.
c. Assessing the Impact of Uncertainty on Prioritization. It is important to understand
that the results of a prioritization (that is, an ordered list of decision options) may
be impacted by uncertainties and how they may be affected. It is desirable that
the treatment of uncertainties in the bottom line priority measures reflect all
sources of variability and uncertainty, to avoid misleading the user by reporting
only some contributors to the uncertainty of the bottom line. However,
distinguishing causal contributors to variability or uncertainty is often illuminating to
inform the user about the dominant factors limiting our ability to discriminate the
optimum decision alternative. When assessing uncertainty, it is recommended
that the guidance provided by the OMB be followed (References [k] and [l]).
Typically, the assessment of uncertainties has focused on understanding the
impacts of parameter uncertainty. The basic methods for assessing the impact of
parameter uncertainty include:
1. Sensitivity Analysis. A sensitivity analysis is performed by varying parameters
within the aggregation equation over a range of values and by observing the
effect on the prioritization results. Usually, the parameters are considered

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