NDLI: Multiple attribute decision making with completely unknown weights based on cumulative prospect theory and grey system theory

Content Provider	ACM Digital Library
Author	Li, Aihua Wen, Mengke Zhao, Zhangyan
Abstract	The purposes of this paper are to study multiple attribute decision making problems by considering the behavioral characteristics of decision makers where the attribute weights are completely unknown. To determine the attribute weights, an optimization model based on prospect theory and the grey relation deep coefficient, from which the attribute weights can be determined, was established. The value function and decision weight function were used to calculate the overall prospect values of attributes for each alternative, and then rank the alternatives to select the most desirable one in accordance with the scores. In order to verify this method, it was used to study an illustrative example using, with the results demonstrating its feasibility and effectiveness. And it can be drawn the conclusion that the proposed method can be applied to decision making problems when the attribute weights are completely unknown while considering the decision maker's behavior at the same time.
Starting Page	1
Ending Page	7
Page Count	7
File Format	PDF
ISBN	9781450347990
DOI	10.1145/3028842.3028848
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-12-23
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Prospect theory Completely unknown weights Multiple attribute decision making Grey correlation deep coefficient
Content Type	Text
Resource Type	Article

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Multiple attribute decision making with completely unknown weights based on cumulative prospect theory and grey system theory