Estimating incomplete information in group decision making: A framework of granular computing
Metadatos
Afficher la notice complèteAuteur
Cabrerizo Lorite, Francisco Javier; Martínez Sánchez, María Ángeles; Herrera-Viedma, Enrique; Pedrycz, Witold; Al-Hmouz, Rami; Morfeq, AliMateria
fuzzy preference relation information granularity group decision making
Date
2020-01Referencia bibliográfica
Francisco Javier Cabrerizo, Rami Al-Hmouz, Ali Morfeq, María Ángeles Martínez, Witold Pedrycz, Enrique Herrera-Viedma, Estimating incomplete information in group decision making: A framework of granular computing, Applied Soft Computing, Volume 86, 2020, 105930.
Résumé
A general assumption in group decision making scenarios is that of all individuals possess accurate knowledge of the entire problem under study, including the abilities to make a distinction of the degree up to which an alternative is better than other one. However, in many real world scenarios, this may be unrealistic, particularly those involving numerous individuals and options to choose from conflicting and dynamics information sources. To manage such a situation, estimation methods of incomplete information, which use own assessments provided by the individuals and consistency criteria to avoid discrepancy, have been widely employed under fuzzy preference relations. In this study, we introduce the information granularity concept to estimate missing values supporting the objective of obtaining complete fuzzy preference relations with higher consistency levels. We use the concept of granular preference relations to form each missing value as a granule of information in place of a crisp number. This offers the flexibility that is required to estimate the missing information so that the consistency levels related to the complete fuzzy preference relations are as higher as possible.