The ordered weighted averaging (OWA) operator and its associated weight vectors have been both theoretically and practically verified to be powerful and effective in modeling the optimism/pessimism preference of decision makers. When several different OWA weight vectors are offered, it is necessary to develop certain techniques to aggregate them into one OWA weight vector. This study firstly details several motivating examples to show the necessity and usefulness of merging those OWA weight vectors. Then, by applying the general method for aggregating OWA operators proposed in a recent literature, we specifically elaborate the use of OWA aggregation to merge OWA weight vectors themselves. Furthermore, we generalize the normal preference degree in the unit interval into a preference sequence and introduce subsequently the preference aggregation for OWA weight vectors with given preference sequences. Detailed steps in related aggregation procedures and corresponding numerical examples are also provided in the current study.

从理论上和实践上都证明，有序加权平均（OWA）运算符及其关联的权向量在建模决策者的乐观/悲观偏好方面具有强大而有效的作用。当提供几种不同的OWA权重矢量时，有必要开发某些技术以将它们聚合为一个OWA权重矢量。这项研究首先详细说明了一些激励性示例，以表明合并这些OWA权重向量的必要性和实用性。然后，通过应用最近文献中提出的用于汇总OWA运算符的通用方法，我们专门阐述了使用OWA聚合合并OWA权重向量本身的方法。此外，我们将单位间隔中的正常偏好度归纳为一个偏好序列，然后针对具有给定偏好序列的OWA权向量引入偏好聚合。本研究中还提供了有关聚合程序的详细步骤和相应的数值示例。