This paper presents the prioritized induced heavy ordered weighted average (PIHOWA) operator. This operator combines an unbounded weighting vector, an induced vector and a prioritized vector and can be applied to the group decision-making process where the information provided by each decision maker does not have the same importance. An application of this operator is done in governmental transparency in Mexico based on the Open Government Metric (OGM). Among the main results it is possible to visualize how the relative importance of each component can generate important change in the top 10 ranking.

中文翻译：

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应用于政治建模的优先诱导重算子

本文提出了优先诱导重排序加权平均（PIHOWA）算子。该算子结合了无界加权向量、诱导向量和优先向量，可应用于每个决策者提供的信息不具有相同重要性的群体决策过程。该运营商的应用是在墨西哥基于开放政府度量 (OGM) 的政府透明度中完成的。在主要结果中，可以可视化每个组件的相对重要性如何在前 10 名排名中产生重要变化。