The outcomes of elections, product sales, and the structure of social connections are all determined by the choices individuals make when presented with a set of options, so understanding the factors that contribute to choice is crucial. Of particular interest are context effects, which occur when the set of available options influences a chooser's relative preferences, as they violate traditional rationality assumptions yet are widespread in practice. However, identifying these effects from observed choices is challenging, often requiring foreknowledge of the effect to be measured. In contrast, we provide a method for the automatic discovery of a broad class of context effects from observed choice data. Our models are easier to train and more flexible than existing models and also yield intuitive, interpretable, and statistically testable context effects. Using our models, we identify new context effects in widely used choice datasets and provide the first analysis of choice set context effects in social network growth.

给定一组选择，理解导致个人作出选择的因素至关重要，本研究提出了一种来自观察选择数据自动发现广泛上下文效应的方法，该模型易于训练，在现有模型上更灵活，可产生直观，可解释且统计测试的上下文效应。

学习离散选择中可解释特征的上下文效应