Integrating Choice and Predictive Theories: A Comparative Study of Set Generation and Machine Learning in Consideration Set Modeling

Abstract This study introduces a comparative framework designed to evaluate the predictive capabilities of two distinct modeling approaches: traditional consideration and choice set generation models, and Machine Learning (ML) models. The framework focuses on a critical and often overlooked stage in decision–making process, which includes the set of alternatives that were considered prior to making a choice. To demonstrate the framework’s applicability, a synthetic route choice scenario was employed as a case study. In this context, routes with randomly assigned attributes were generated alongside synthetic populations of decision-makers, whose socioeconomic profiles were sampled from observed survey data. Each agent was assigned a set generation models to construct their individual consideration sets. ML models were then trained on these consideration sets to predict the considered alternatives of independent decision-maker populations. Two population scenarios were examined: one homogeneous, where all agents used the same set generation model, and one heterogeneous, where models were randomly assigned across agents. In both cases, the accuracy of the prediction was evaluated, along with a comparison between existing set generation and predictive models. The results showed that certain ML models can achieve over 80% accuracy in predicting individual considered items and over 60% accuracy in predicting entire consideration sets, across both population scenarios.