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Decision Makers’ Opinions Changing Attitude-Driven Consensus Model under Linguistic Environment and Its Application in Dynamic MAGDM Problems

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Granular Computing and Decision-Making

Part of the book series: Studies in Big Data ((SBD,volume 10))

Abstract

Reaching acceptable agreement among decision makers before selecting the suitable alternatives is an important issue in multi-attribute group decision making (MAGDM) process. The aim of this chapter is to present a flexible consensus method in linguistic contexts that adopts a new advice generation scheme by incorporating decision maker’s attitude to achieve agreement at each round of consensus process. Different consensus models for MAGDM problems have been proposed in the literature. However in all of these processes, it is assumed that all the decision makers are equally interested to change their initial opinions. But practically different decision makers may have different levels of confidences in their own opinions and that make their inclinations in changing opinions significantly different to each others’. Moreover, the decision makers who have sufficient agreement levels may not be interested to change their opinions further. This analysis motivates us to develop a new consensus reaching process under linguistic environment wherein decision makers’ opinions will be changed according to the opinion changing indices provided by them and, thus, decision makers’ moral right to modify their opinions will be preserved. Theoretical foundation of the proposed consensus model is laid down and we further implement the consensus algorithm in a linguistic MAGDM problem under dynamic environment. The main contribution of our work is that sovereignty of each decision maker is under consideration in the process of reaching consensus. Finally, a practical example is presented to illustrate the functioning of the proposed method.

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Correspondence to Bapi Dutta .

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Dutta, B., Guha, D. (2015). Decision Makers’ Opinions Changing Attitude-Driven Consensus Model under Linguistic Environment and Its Application in Dynamic MAGDM Problems. In: Pedrycz, W., Chen, SM. (eds) Granular Computing and Decision-Making. Studies in Big Data, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-16829-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-16829-6_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16828-9

  • Online ISBN: 978-3-319-16829-6

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