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Page Title: Developing Data and Information About Uncertainties.
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individually as it is difficult to interpret the results if combinations of parameters
are simultaneously varied. Sensitivity analysis is typically used as an initial
step in understanding uncertainties to indicate how the parameters within the
aggregation equation affect the prioritization results.
2. Uncertainty Analysis. An uncertainty analysis is performed by developing
probability distribution functions for the parameters within the aggregation
equation, and subsequently propagating these distributions through the
aggregation equation. The most difficult part of performing any uncertainty
analysis is the formulation of the underlying parameter distributions. A variety
of techniques have been developed to propagate the parameter distributions
through the aggregation equation (such as Monte Carlo analysis, meta-
analysis, discrete probability distributions, and the method of moments).
d. Developing Data and Information About Uncertainties. Historical data should be
used to gain insight about the extent of parameter uncertainties; such information
can be analyzed, interpreted, and communicated using standard statistical
methods. If no relevant historical data exist, expert opinion may be solicited and
processed using a variety of methods. There are a variety of formal methods for
eliciting and combining expert judgments of data distribution characteristics, such
as Delhi methods, that should be considered for use as appropriate. Limited
historical data may be combined with expert opinion using Bayesian statistical
methods. The information should be documented for peer review and for future
modeling use.
Guideline 2.6-- Development of Performance Measurement Scales. The RBP system
should be developed in such a way that each performance measure can reflect either
a benefit increase (desirable) or a benefit decrease (undesirable).
Discussion. The implementation of a given decision option can create either an
increase or a decrease in the benefit and/or cost associated with each performance
measure; accordingly, each performance measure scale should be capable of
reflecting either effect. Within a prioritization system, the only way to accurately
assess the net benefit of a decision option is to properly account for both the aspects
of an activity that move one closer to meeting a decision objective and those that
move one farther away. For example, consider an RBP system containing a
performance measure related to public health and safety that is used to assess two
decision options. Implementation of the first decision option may greatly reduce the
risk to worker health and safety (a large increase in benefit associated with this
objective), while it also may somewhat increase the risk to public health and safety (a
small decrease in benefit associated with this objective). The RBP system must be
capable of reflecting these effects, which is achieved through proper construction of
the performance measure scales.
Therefore, the performance measurement scales should reflect both direction
(increase or decrease in benefits) and magnitude (how much increase or decrease in
benefit). A simple way to achieve this need is to assign positive performance

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