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Iranian Journal of Fuzzy Systems، جلد ۱۹، شماره ۵، صفحات ۷۹-۹۴
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عنوان انگلیسی An extended MABAC method based on prospect theory for multiple attribute group decision making under probabilistic uncertain linguistic environment
چکیده انگلیسی مقاله Cyber security is a hot topic in recent years and one of the main performances in the decision-making is the choice of cyber security service providers (CSSPs). For enterprises and government units, the selection of appropriate NSSPs needs to be measured and evaluated from multiple perspectives, which is obviously a multiple attribute decision making (MADM) or multiple attribute group decision making (MAGDM). In this paper, the traditional MABAC method is improved by using the prospect theory, and the evaluation information is collected and sorted by using PULTS. The improved PUL-PT-MABAC method can not only deal with the uncertainty well, but also fully consider the influence of the psychological state of decision maker (DM) on the decision result. More importantly, we apply the improved PUL-PT-MABAC model to the selection of NSSPs and prove the reliability of this proposed method by taking advantage of comparative analysis with three existed methods. These results show that the model can deal with practical problems, and has good practicability and science.
کلیدواژه‌های انگلیسی مقاله Multi-attribute group decision making (MAGDM), MABAC method, probabilistic uncertain linguistic term set, prospect theory, cyber security service providers (CSSPs)
نویسندگان مقاله M. Zhao |
School of Business, Sichuan Normal University, Chengdu, 610101, P.R. China

M. Zhao |
School of Business, Sichuan Normal University, Chengdu, 610101, P.R. China

G. Wei |
School of Business, Sichuan Normal University, Chengdu, 610101, P.R. China

C. Wei |
School of Statistics, Southwestern University of Finance and Economics, Chengdu, 611130, P.R. China

X. Chen |
School of Accounting, Southwestern University of Finance and Economics, Chengdu, 611130, P.R. China

نشانی اینترنتی https://ijfs.usb.ac.ir/article_7158_5071fa3c895a89487002ddcb1270b9e9.pdf
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