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