Downloadable - About University

Eliciting decision tree representations 159
other main sub-events. In a subsequent experiment the importance of
‘all other problems’ was emphasized:
In particular we would like you to consider its [the fault tree’s] completeness.
That is, what proportion of the possible reason for a car not starting are left
out, to be included in the category, ‘all other problems’?
However, focusing subjects’ attention on what was missing only partially
improved their awareness. Fischhoff labeled this insensitivity to the
incompleteness of the fault tree ‘out of sight, out of mind’. The finding
was confirmed with technical experts and garage mechanics. Neither
self-rated degree of knowledge nor actual garage experience has any
significant association with subjects’ ability to detect what was missing
from the fault tree.
Another finding from the study was that the perceived importance
of a particular sub-event or branch of the fault tree was increased by
presenting it in pieces (i.e. as two separate branches). The implications
of this result are far reaching. Decision trees constructed early in the
analyst/decision maker interaction may be incomplete representations
of the decision problem facing the decision maker.
Eliciting decision tree representations
What methods have been developed to help elicit decision tree representations
from decision makers? One major method, much favored by
some decision analysts, is that of influence diagrams 18 which are designed
to summarize the dependencies that are seen to exist among events and
acts within a decision. Such dependencies may be mediated by the flow
of time, as we saw in our examples of decision trees. As we shall see,
a close relationship exists between influence diagrams and the more
familiar decision trees. Indeed, given certain conditions, influence diagrams
can be converted to trees. The advantage of starting with influence
diagrams is that their graphic representation is more appealing to the
intuition of decision makers who may be unfamiliar with decision technologies.
In addition, influence diagrams are more easily revised and
altered as the decision maker iterates with the decision analyst. Decision
trees, because of their strict temporal ordering of acts and events, need
completely respecifying when additional acts and events are inserted
into preliminary representations. We shall illustrate the applicability of