This paper aims at developing new models to combine the best of two paradigms: the performance of ensembles and the transparency of choice models. Specifically, the work explores several blind methods to build ensembles of single choice-based models. They were fit using a dataset that includes rational, emotional and attentional variables, all of them gathered from humans during a choice task. Two main strategies, 1-Learner and N-Learners type ensembles, were analyzed in terms of their accuracy as well as frequency of best-model metrics. The results point out the superior performance of N-Learners ensembles and show the potential of personalized arrangements.

本文旨在开发新的模型，以结合两个范式的优点：合奏的性能和选择模型的透明度。具体来说，这项工作探索了几种盲法来构建基于单选择模型的集合体。使用包含理性，情感和注意力变量的数据集对它们进行拟合，所有这些变量都是在选择任务期间从人类那里收集的。根据其准确性以及最佳模型指标的频率，分析了两种主要策略，即1-Learner和N-Learners类型的合奏。结果指出了N学习者合奏的出色表现，并展示了个性化布置的潜力。