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452 Alternative decision-support systems
clear-cut signal of its financial viability, then subjects would be expected
to incorporate available prior probability information into their judgment
processes. Such additional information is, of course, likely to be
available in the everyday situations of loan officers.
Although early studies of linear modeling in clinical settings showed
evidence that the model of the judge outperforms the judge on whom
the model was based, more recent evidence is contradictory. 53 This
shows that experts can recognize the significance of extra-model information
and use it appropriately. Such characteristics of experts can,
potentially, be captured in expert system representations of knowledge.
However, research on the combination of bootstrapping models with
expert systems is, as yet, relatively undeveloped. 54
Comparisons
Essentially, decision analysis using decision trees is utilized for unique
or one-off decisions under uncertainty. The decision maker provides
the decision analyst with the temporal sequencing of possible acts and
events (the decision tree), his or her opinion about the likelihood of events
(subjective probabilities) and his or her subjective valuation (utilities) of
the consequences of particular act and event combinations or outcomes.
The whole approach is predicated on the notion that decomposition and
subsequent recomposition of a decision problem will improve decision
making. As we have seen, the implicit theory is that we humans have
limited information-processing capacity and that the expected utility
computations are best left to the analyst’s computer. Nevertheless,
decision analysis still makes the assumption that the decision maker’s
prime inputs of subjective probability and utility have validity. Recall
that the practice of sensitivity analysis focuses elicitation methodologies
on ‘critical’ assessments. In a similar manner, the bootstrapping approach
involves the assumption that decision makers are able to identify the key
predictor variables to be entered into the prediction equation. Optimal
weighting of the predictor variables’ impact on the prediction equation
is best left to the statistical modeling techniques. In contrast to decision
analysis, bootstrapping models are best deployed in repetitive decisionmaking
situations where only scores on the predictor variables vary
from one prediction to another.
In more dynamic environments, where fresh predictor variables may
be expected to be added to the cue variable set or where the possibility of