Publication:
Comprehensive rules-based and preferences induced weights allocation in group decision-making with BUI

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Date

2022

Authors

Li, GePeng
Yager, Ronald R.
Zhang, XinXing
Mesiar, Radko
Jin, LeSheng

Director

Publisher

Springer
Acceso abierto / Sarbide irekia
Artículo / Artikulua
Versión publicada / Argitaratu den bertsioa

Project identifier

Abstract

Decision-makers' subjective preferences can be well modeled using preference aggregation operators and related induced weights allocation mechanisms. However, when several different types of preferences occur in some decision environment with more complex uncertainties, repeated uses of preferences induced weights allocation sometimes become unsuitable or less reasonable. In this work, we discuss a common decision environment where several invited experts will offer their respective evaluation values for a certain object. There are three types of preferences which will significantly affect the weights allocations from experts. Instead of unsuitably performing preference induced weights allocation three times independently and then merging the results together using convex combination as some literatures recently did, in this work, we propose some organic and comprehensive rules-based screen method to first rule out some unqualified experts and then take preference induced weights allocation for the refined group of experts. A numerical example in business management and decision-making is presented to show the cognitive reasonability and practical feasibility. © 2022, The Author(s).

Keywords

Aggregation operators, Basic uncertain information, Decision-making, Information fusion, Preference modeling

Department

Estadística, Informática y Matemáticas / Estatistika, Informatika eta Matematika

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Degree

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Funding entities

This paper is supported by Jiangsu Social Science Foundation with grant 18JD009. This paper is supported by grant APVV-18-0052.

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