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448 Alternative decision-support systems
Patient information
Score (S) on each of the I I M M P I
sub-scales related to neuroticism
and psychoticism
Judge
Weight (W) derived for each of
the I I M M P I sub-scales
Linear additive model of
judge
C = m + W 1 S 1 + W 2 S 2 . . . + W 11 S 11
Criterion (C) to be predicted
Whether the patient was diagnosed
neurotic or psychotic after
extensive psychological
and psychiatric evaluation
Figure 17.3 – Basic paradigm for the construction of a linear additive model of a judge
‘model’ or ‘policy’. Figure 17.3 sets out the basic paradigm for a study
of multi-attributed inference.
How do these models make out as predictors themselves? That is, if
the regression weights (generated from an analysis of one clinical judge)
were used to obtain a ‘predicted score’ for each patient, would these
scores be more valid, or less valid, than the original clinical judgments
from which the regression weights were derived? To the extent that the
model fails to capture valid non-linear variance to the judges’ decision
processes, it should perform worse than the judge; to the extent that it
eliminates the random error component in human judgments, it should
perform better than the judge.
What were the results of this research? The overwhelming conclusion
was that the linear model of the judge’s behavior outperformed the
judge. Dawes 39 noted:
I know of no studies in which human judges have been able to improve upon
optimal statistical prediction ...A mathematical model by its very nature is
an abstraction of the process it models; hence if the decision-maker’s behavior