Capturing Non-linear Judgment Policies Using Decision Tree Models of Classification Behavior

Abstract

Policy capturing is a decision analysis method that typically uses linear statistical modeling to estimate the basis of expert judgments. Using more flexible data mining algorithms may yield more accurate models or instead result in poor functional estimations. The objective of this study is to test the effectiveness of a decision tree induction algorithm for policy capturing in comparison to the standard linear approach. We examined human classification behavior using a simulated naval air-defense task in order to empirically compare the C4.5 decision tree algorithm to linear regression on their ability to capture individual decision policies. The pattern of results shows that C4.5 outperformed linear regression in terms of goodness-of-fit and cross-validation accuracy. Results also show that the decision tree models of individuals’ judgment policies actually classified contacts more accurately than their human counterparts. We conclude that non-linear policy capturing can yield useful models for training and decision support applications.