Estimating incomplete information in group decision making: A framework of granular computing

Francisco Javier Cabrerizo*, Rami Al-Hmouz, Ali Morfeq, María Ángeles Martínez, Witold Pedrycz, Enrique Herrera-Viedma

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)


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.

Original languageEnglish
Article number105930
JournalApplied Soft Computing Journal
Publication statusPublished - Jan 2020


  • Consistency
  • Fuzzy preference relation
  • Group decision making
  • Incomplete information
  • Information granularity

ASJC Scopus subject areas

  • Software


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