报告题目：Extreme Values Reductions and Extreme Values Amplifications: Aggregating information with EVR-OWA

报告摘要：Consensus Reaching Processes (CRPs) deal with those group decision-making situations in which conflicts among experts' opinions make difficult the reaching of an agreed solution. This situation, worsens in large scale group decision situations, in which opinions tend to be more polarized, because in problems with extreme opinions it is harder to reach an agreement. Several studies have shown that experts' preferences may not always follow a linear scale, as it has commonly been assumed in previous CRP. Therefore, modeling this nonlinear behavior of experts' preferences (expressed by fuzzy preference relations) in CRPs could provide better ways to agreements. To do that, one way could be to remap experts’s preferences by using nonlinear deformations which amplify or reduce the distance between the extreme values. Such automorphisms to remap the preferences has been introduced as Extreme Values Amplifications (EVAs) and Extreme Values Reductions (EVRs), study their main properties and several families of these EVA and EVR functions proposed.

This talk aims to provide clear view about how to deal with these nonlinear representation in group decision making can facilitate the agreements in final decisions and also it has been pointed out its use for generating special aggregation operators that can be used in decision making solving processes.

报告人简介：Luis Martinez Lopez 教授是西班牙哈恩大学计算机科学系教授,主要研究方向为模糊决策、词计算、多准则决策等，目前担任 International Journal of Computational Intelligence Systems 主编，Information Sciences、Expert System with Applications、Knowledge-based System、IEEE Transactions on fuzzy systems、Information Fusion 等期刊副主编，IFSA 会士（IFSA Fellow），IEEE senior member，欧洲模糊逻辑与技术学会（ESFLT）会士。发表 270 余篇文章，2017-2023 年连续入选全球高被引学者榜单。