We study how to infer new choices from prior choices using the framework of choice functions, a unifying mathematical framework for decision-making based on sets of preference orders. In particular, we define the natural (most conservative) extension of a given choice assessment to a coherent choice function -- whenever possible -- and use this natural extension to make new choices. We provide a practical algorithm for computing this natural extension and various ways to improve scalability. Finally, we test these algorithms for different types of choice assessments.

翻译：本研究探讨如何利用选择函数框架从先验选择中推断新选择，该框架是基于偏好序集合的决策统一数学框架。具体而言，我们定义了给定选择评估到一致性选择函数的自然（最保守）扩展——在可能的情况下——并利用该自然扩展进行新选择。我们提出了一种计算该自然扩展的实用算法及多种提升可扩展性的方法。最后，我们针对不同类型的选择评估测试了这些算法。