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Statistical models of judgment 451
In fact, Shepanski 47 reported an experiment to test a linear representation
and various non-linear representations of information-processing
behavior in the task of credit evaluations. Participants in the experiment
were presented with sets of information describing prospective business
borrowers in terms of payment record, financial condition and quality of
the company’s management. Shepanski argued that the credit judgment
task is best represented by a non-linear model. Additionally, in real-life
credit valuations the composition and size of the information employed
will change. Information gathering is costly and, for example, applications
for a large loan will entail a much more comprehensive credit
investigation than a small loan application. Such flexibility in information
search cannot be captured by statistical modeling that is better suited
to repetitive forecasts with a static number of predictor variables. However,
as Dawes et al. 48 have pointed out, the small number of studies that
have provided clinicians with access to preferred sources of information
have generally shown the superiority of the statistical model. As these
authors note, human judgment can theoretically improve on statistical
modeling by recognizing events that are not included in the model’s
formula and that countervail the actuarial conclusion. Dawes et al.argue
that such events are rare but, as we have already shown in Chapter 9,
this is the exact situation where forecasting practitioners advocate the
need for judgment. Indeed, recent studies 49,50 provide evidence of the
quality of human judgment compared to statistical models when ‘broken
leg’ cues are part of the information available for decision making. The
term ‘broken leg cue’ is due to Meehl. 38 He noted that the knowledge
that a certain person had just broken his or her leg would invalidate
any predictions of a person’s movements (e.g. to the theater, particular
restaurants, etc.) based on historic statistical data.
To illustrate, Chalos 51 investigated the ability of an outcome-based
credit-scoring model to assess financial distress and compared the
performance of the model with that of loan review committees and
individual loan officers. The major finding was the loan review committees
significantly outperformed the model and the individual officers.
The model was a stepwise discriminant model built using eight financial
ratios as cue variables. The loan review officers/committees had
additional information for each judgment in the previous three years’
financial statements. Chalos’s results indicated that loan committees
may be beneficial, and the additional time required may be more than
offset by the reduction in loan cost errors. In a related study, Casey
and Selling 52 used MBA students as subjects in a bankruptcy prediction
task and noted that if a firm’s specific financial data do not provide a