论文标题
在离散选择中学习可解释的特征上下文效果
Learning Interpretable Feature Context Effects in Discrete Choice
论文作者
论文摘要
选举，产品销售和社会联系的结构的结果都是由个人在提供一系列选择时做出的选择决定的，因此了解有助于选择的因素至关重要。特别有趣的是上下文效应，当可用选项的集合影响选择者的相对偏好时发生，因为它们违反了传统的理性假设，但实际上是广泛的。但是，从观察到的选择中确定这些效果是具有挑战性的，通常需要预知要测量的效果。相比之下，我们提供了一种从观察到的选择数据自动发现广泛的上下文效应的方法。与现有模型相比，我们的模型更容易训练和灵活，并且还产生了直观，可解释和统计测试的上下文效果。使用我们的模型，我们在广泛使用的选择数据集中确定了新的上下文效应，并提供了对社交网络增长中选择环境效应的首次分析。
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.
