A Machine Learning Approach to Predict Human Judgments in Compensatory and Noncompensatory Judgment Tasks

Abstract

Traditionally, in judgment analysis, multiple linear regression based lens model, which assumes decision makers assess every cue, weigh, and combine them to make overall judgments, has been used to model and analyze human judgments. However, linear regression assumptions are limited in situations where logical rules for making decisions are not consistent with a weighting and summing formula. In this study, we sought to extend the body of knowledge in the judgment analysis research by adopting the rule-based lens model and using machine learning models to predict human judgments in compensatory and noncompensatory judgment tasks. Overall, the selected machine learning models outperformed the linear logistic regression (LgR) model in both compensatory and noncompensatory tasks. Our own results suggest that, at least for the present application, machine learning models may be better at predicting human judgments in compensatory and noncompensatory judgment tasks than linear multiple LgR models. We conclude that machine learning algorithms can yield useful models for training and decision support applications.