Recently introduced cone distribution functions from statistics are turned into multi-criteria decision making (MCDM) tools. It is demonstrated that this procedure can be considered as an upgrade of the weighted sum scalarization insofar as it absorbs a whole collection of weighted sum scalarizations at once instead of fixing a particular one in advance. Moreover, situations are characterized in which different types of rank reversal occur, and it is explained why this might even be useful for analyzing the ranking procedure. A few examples will be discussed and a potential application in machine learning is outlined.

翻译：近年来统计学中引入的锥分布函数被转化为多准则决策（MCDM）工具。研究表明，该流程可视为加权求和标量化的升级，其优势在于能一次性整合整个加权求和标量化集合，而非预先固定某个特定方法。此外，本文刻画了不同类型排序反转的发生条件，并阐释了此类现象对分析排序流程的潜在价值。通过若干实例讨论，我们概述了该方法在机器学习中的可能应用。