Modelling Heterogeneity among Experts in Multi-criteria Group Decision Making Problems
Identificadores
URI: https://hdl.handle.net/10481/77925Metadata
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Springer
Materia
Group decision making Multi-criteria decision making Heterogeneous decision frameworks Linguistic approach
Date
2011Referencia bibliográfica
Published version: Pérez, I.J... [et al.] (2011). Modelling Heterogeneity among Experts in Multi-criteria Group Decision Making Problems. In: Torra, V., Narakawa, Y., Yin, J., Long, J. (eds) Modeling Decision for Artificial Intelligence. MDAI 2011. Lecture Notes in Computer Science(), vol 6820. Springer, Berlin, Heidelberg. [https://doi.org/10.1007/978-3-642-22589-5_7]
Sponsorship
FUZZYLINGProject TIN200761079; FUZZYLING-II Project TIN201017876; PETRI Project PET20070460; Andalusian Excellence Project TIC-05299; project of Ministry of Public Works 90/07Abstract
Heterogeneity in group decision making problems has been recently
studied in the literature. Some instances of these studies include the use of heterogeneous
preference representation structures, heterogeneous preference representation
domains and heterogeneous importance degrees. On this last heterogeneity
level, the importance degrees are associated to the experts regardless of
what is being assessed by them, and these degrees are fixed through the problem.
However, there are some situations in which the experts’ importance degrees do
not depend only on the expert. Sometimes we can find sets of heterogeneously
specialized experts, that is, experts whose knowledge level is higher on some alternatives
and criteria than it is on any others. Consequently, their importance
degree should be established in accordance with what is being assessed. Thus,
there is still a gap on heterogeneous group decision making frameworks to be
studied. We propose a new fuzzy linguistic multi-criteria group decision making
model which considers different importance degrees for each expert depending
not only on the alternatives but also on the criterion which is taken into account
to evaluate them.