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Multiplicative consistency analysis of linguistic preference relation with self‐confidence level and self‐doubting level and its application in a group decision making
Author(s) -
Mandal Prasenjit,
Samanta Sovan,
Pal Madhumangal
Publication year - 2021
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22516
Subject(s) - multiplicative function , consistency (knowledge bases) , preference , group decision making , mathematics , convergence (economics) , relation (database) , aggregate (composite) , computer science , algorithm , statistics , psychology , data mining , discrete mathematics , social psychology , mathematical analysis , materials science , economics , composite material , economic growth
This article focuses on a group decision‐making (GDM) approach based on the multiplicative consistency of linguistic preference relation (LPR) with experts' self‐confidence and self‐doubting (SC&SD) levels. To give their preferences, the experts use their knowledge of the experience according to their degree of SC&SD levels. First, we propose the concepts of multiplicative consistent LPR‐SC&SD using the experts' general minimum self‐confidence level and the maximum self‐doubting level. We suggest then a consensus‐building iterative process, that is, a consensus reaching process (CRP) algorithm to achieve multiplicative consistency of LPR‐SC&SD according to identification and adjustment rules. A theorem is given for convergence of the CRP algorithm. In a GDM problem, social network analysis is studied for the experts to obtain their weight according to the degree of SC&SD. When we achieve the acceptable preferences of all the experts using the CRP algorithm of the multiplicative consistent LPR‐SC&SD, then we aggregate all the preferences by the experts' weights. The aggregation of all preferences is also an LPR‐SC&SD, known as the weight collective LPR‐SC&SD. Finally, a case‐by‐case example and several comparative analyses are done with the current GDM processes to demonstrate the viability and applicability of the proposed GDM system.