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Penalty-Based Aggregation Beyond the Current Confinement to Real Numbers: The Method of Kemeny Revisited

机译:基于惩罚的聚合,超出了当前限制的实际数字:kemeny的方法重新审视了

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The field of aggregation theory addresses the mathematical formalization of aggregation processes. Historically, the developed mathematical framework has been largely confined to the aggregation of real numbers, while the aggregation of other types of structures, such as rankings, has been independently considered in different fields of application. However, one could lately perceive an increasing interest in the study and formalization of aggregation processes on new types of data. Mostly, this aggregation outside the framework of real numbers is based on the use of a penalty function measuring the disagreement with a consensus element. Unfortunately, there does not exist a comprehensive theoretical framework yet. In this paper, we propose a natural extension of the definition of a penalty function to a more general setting based on the compatibility with a given betweenness relation. In particular, we revisit one of the most common methods for the aggregation of rankings - the method of Kemeny - which will be positioned in the penalty-based aggregation framework.
机译:聚合理论领域解决了聚合过程的数学形式化。从历史上看,发达的数学框架主要被限制在实数的聚合中,而其他类型的结构(例如排名)的聚合已经在不同的应用领域中独立地考虑。然而,人们可以最终察觉对新型数据类型的汇总过程的研究和形式化越来越令人兴趣。主要是,实数框架之外的这种聚合是基于使用惩罚函数来测量与共识元素的分歧。不幸的是,尚未存在综合理论框架。在本文中,我们提出了一种自然延伸,根据与给定之间关系的兼容性的兼容性,将惩罚功能的定义进行了自然扩展。特别是,我们重新审视排名汇总的最常见方法之一 - kemeny的方法 - 将定位在基于惩罚的聚合框架中。

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