For the interpretation of the results of probabilistic risk assessment
s it is important to have measures which identify the basic events tha
t contribute most to the frequency of the top event but also to identi
fy basic events that are the main contributors to the uncertainty in t
his frequency. Both types of measures, often called Importance Measure
and Measure of Uncertainty Importance, respectively, have been the su
bject of interest for many researchers in the reliability field. The m
ost frequent mode of uncertainty analysis in connection with probabili
stic risk assessment has been to propagate the uncertainty of all mode
l parameters up to an uncertainty distribution for the top event frequ
ency. Various uncertainty importance measures have been proposed in or
der to point out the parameters that in some sense are the main contri
butors to the top event distribution. The new measure of uncertainty i
mportance suggested here goes a step further in that it has been devel
oped within a decision theory framework, thereby providing an indicati
on of on what basic event it would be most valuable, from the decision
-making point of view, to procure more information. (C) 1997 Elsevier
Science Limited.