Toward Understanding the Contents of the “Black Box” for Predicting Complex Decision‐making Outcomes

Abstract

The purpose of this research is to show the usefulness of three relatively simple nonlinear classification techniques for policy-capturing research where linear niodels have typically been used. This study uses 480 cases to assess the decision-making process used by 24 experienced national bank examiners in classifying commercial loans as acceptable or questionable. The results from multiple discriminant analysis (a linear technique) are compared to those of chi-squared automatic interaction detector analysis (a search technique), loglinear analysis, and logit analysis. Results show that while the four techniques are equally accurate in predicting loan classification, chi-squared automatic interaction detector analysis (CHAID) and log-linear analysis enable the researcher to analyze the decision-making structure and examine the "human" variable within the decision-making process. Consequently, if the sole purpose of research is to predict the decision maker's decisions, then any one of the four techniques turns out to be equally useful. If, however, the purpose is to analyze the decision-making process as well as to predict decisions, then CHAID or log-linear techniques are more useful than linear model techniques.Subject Areas: Banking and Flrrrurce, Drcision P r m~e s , Human Information Pro- cessing, and ~a n l z a t i o n a lBehavior.