Abstract
AbstractDecision makers involved in complex decision making problems usually provide information about their preferences by eliciting their knowledge with different assessments. Usually, the complexity of these decision problems implies uncertainty that in many occasions has been successfully modelled by means of linguistic information, mainly based on fuzzy based linguistic approaches. However, classically these approaches just allow the elicitation of simple assessments composed by either one label or a modifier with a label. Nevertheless, the necessity of more complex linguistic expressions for eliciting decision makers’ knowledge has led to some extensions of classical approaches that allow the construction of expressions and elicitation of preferences in a closer way to human beings cognitive process. This paper provides an overview of the broadest fuzzy linguistic approaches for modelling complex linguistic preferences together some challenges that future proposals should achieve to improve complex ...
Highlights
In spite of decision making processes have been an object of research during many years, new requirements and challenges within the topic arise often, because of new problems and new necessities of decision makers
This paper aims at providing an overview of the fuzzy approaches that model complex linguistic expressions together with their computational models
The linguistic expressions provided by this approach are more elaborated and flexible than previous one (Section 3.1), but their formalization is still far from common language used by decision makers in decision making, unless for mathematician experts that are familiar with logic expressions
Summary
In spite of decision making processes have been an object of research during many years, new requirements and challenges within the topic arise often, because of new problems and new necessities of decision makers. Nowadays the complexity of decision making problems is due to the existence of multiple and conflicting goals and the necessity of dealing with huge amounts of information and alternatives, and because of time pressure, lack of knowledge and so on It implies that these problems are ill-structured whose definition framework often involves uncertainty, vagueness and incomplete information that cannot be properly modelled by probabilistic models. Across specialized literature different fuzzy linguistic based approaches for modelling preferences in decision making and computational models for CW processes can be found [18,22,26,28,32], these approaches provide just either simple terms or labels that hardly can express in many complex decision situations the decision makers’ knowledge in a proper and adequate way according to decision makers’ aims.
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