On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Measures of entropy and fuzziness related to aggregation operators
Information Sciences—Intelligent Systems: An International Journal
Computing with words in intelligent database querying: standalone and internet-based application
Information Sciences—Informatics and Computer Science: An International Journal - Special issue computing with words
An analytic approach for obtaining maximal entropy OWA operator weights
Fuzzy Sets and Systems
Information Processing and Management: an International Journal - Modelling vagueness and subjectivity in information access
Journal of the American Society for Information Science and Technology
International Journal of Intelligent Systems
A recommender system for research resources based on fuzzy linguistic modeling
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Weighted maximum entropy OWA aggregation with applications to decision making under risk
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
OWA operators with maximal Rényi entropy
Fuzzy Sets and Systems
THE FUZZY GENERALIZED OWA OPERATOR AND ITS APPLICATION IN STRATEGIC DECISION MAKING
Cybernetics and Systems
OWA aggregation over a continuous interval argument with applications to decision making
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Some properties of the weighted OWA operator
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
OWA operators in data modeling and reidentification
IEEE Transactions on Fuzzy Systems
Optimization power consumption model of reliability-aware GPU clusters
The Journal of Supercomputing
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Determination of the ordered weighted averaging (OWA) operators is an important issue in the theory of the OWA operator weights. In this paper, the main existing models for determining the OWA operator weights are outlined and the concept of the Bayesian entropy is introduced. Based upon the Bayesian entropy the maximum Bayesian entropy approach for obtaining the OWA operator weights is proposed. In this model it is assumed, according to previous experiences or from theoretical considerations that a decision maker may have reasons to consider a given prior OWA vector. Finally the new model is solved according to the prior OWA vector with specific level of orness comparing the results with other methods. The results demonstrate the efficiency of our model in generating the OWA operator weights. An applied example is also presented to illustrate the applications of the proposed model.