Chapter

Linguistic Multi-Expert Decision Making Involving Semantic Overlapping

03/2010; DOI:10.1007/978-3-642-11960-6_26

ABSTRACT This paper presents a probabilistic model for linguistic multi-expert decision making (MEDM), which is able to deal with vague
concepts in linguistic aggregation and decision-makers’ preference information in choice function. In linguistic aggregation
phase, the vagueness of each linguistic judgement is captured by a possibility distribution on a set of linguistic labels.
A confidence parameter is also incorporated into the basic model to model experts’ confidence degree. The basic idea of this
linguistic aggregation is to transform a possibility distribution into its associated probability distribution. The proposed
linguistic aggregation results in a set of labels having a probability distribution. As a choice function, a target-oriented
ranking method is proposed, which implies that the decision-maker is satisfactory to choose an alternative as the best if
its performance is as at least “good” as his requirements.

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Keywords

associated probability distribution
 
basic idea
 
basic model
 
choice function
 
confidence parameter
 
decision-maker
 
decision-makers’ preference information
 
labels
 
linguistic aggregation
 
linguistic aggregation results
 
linguistic judgement
 
linguistic labels
 
linguistic multi-expert decision
 
MEDM
 
model experts’ confidence degree
 
paper presents
 
possibility distribution
 
probabilistic model
 
requirements