The Mallows model is a popular distribution for ranked data. We empirically and theoretically analyze how the properties of rankings sampled from the Mallows model change when increasing the number of alternatives. We find that real-world data behaves differently than the Mallows model, yet is in line with its recent variant proposed by Boehmer et al. [2021]. As part of our study, we issue several warnings about using the model.

翻译：Mallows模型是排名数据的常用分布。我们通过实验与理论分析，探究从Mallows模型中采样的排名性质如何随可选方案数量的增加而变化。研究发现，真实世界数据的行为与Mallows模型存在差异，但与Boehmer等人[2021]近期提出的变体模型一致。作为研究的一部分，我们就该模型的使用提出了若干警示。