Further results are discussed and their implications noted. Residual risk is defined as the threat that remains after every effort has been made to identify and eliminate risks in a given situation. Moreover, for certain cases one gets a reverse bootstrapping, i.e., the residual correlates higher with the criterion than either the model of the man or the global judgment. That which remains of something after taking away a part of it as, the residue of an estate, which is what has not been particularly devised by will. It is shown that the residual enters into achievement most prominently in situations where the model of man is poor. in the law of succession, the part of an estate left over after legacies and bequests have been met. Furthermore, psychological achievement is shown to be a weighted sum of the modelled and residual parts of judgment where the weight parameters are interpreted as “environmental complexity.” Two naturalistic studies are examined to investigate the residual contribution to achievement. The issue is shown to depend on the correlation of the residual and the criterion. The conditions where bootstrapping will work are delineated.
Cue definition is shown to be related to the “bootstrapping” phenomenon. Residuals in a statistical or machine learning model are the differences between observed and predicted values of data. The major question asked is whether the residual portion of judgment has functional value, i.e., does it add to the clinician's predictive efficiency. The part of judgment that cannot be modelled (i.e., the residual) is assumed to consist of cues not adequately defined and mis-specification of the integration function.
The problem of cue definition leads to a consideration of the residual variance in modelling the judge. A major problem in naturalistic studies is the defining of cues.
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