Nested formulation paradigms for induced ordered weighted averaging aggregation for decision‐making and evaluation. Issue 11 (2nd September 2019)

• Record Type: Journal Article
• Authors: Zhu, Chen; Jin, LeSheng; Mesiar, Radko; Yager, Ronald R.; Paternain, Daniel; Bustince, Humberto
• Is Part Of: International journal of intelligent systems. Volume 34:Issue 11(2019)
• Page Start: 3046
• Page End: 3057
• Publication Date: 2019-09-02
• Subjects: aggregation function -- decision aiding model -- decision‐making -- evaluation -- induced ordered weighted averaging operator -- ordered weighted averaging operator; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22186
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Existing extensions to Yager's ordered weighted averaging (OWA) operators enlarge the application range and to encompass more principles and properties related to OWA aggregation. However, these extensions do not provide a strict and convenient way to model evaluation scenarios with complex or grouped preferences. Based on earlier studies and recent evolutionary changes in OWA operators, we propose formulation paradigms for induced OWA aggregation and a related weight function with self‐contained properties that make it possible to model such complex preference‐involved evaluation problems in a systematic way. The new formulations have some recursive forms that provide more ways to apply OWA aggregation and deserve further study from a mathematical perspective. In addition, the new proposal generalizes almost all of the well‐known extensions to the original OWA operators. We provide an example showing the representative use of such paradigms in decision‐making and evaluation problems.