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An extended EDAS approach based on cumulative prospect theory for multiple attributes group decision making with probabilistic hesitant fuzzy information

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Abstract

The Probabilistic Hesitant Fuzzy Sets (PHFS) based on the hesitant fuzzy sets has been paid great attention. Though numerous methods have been applied in this environment since the PHFS has been introduced, there are still new fields to be explored. The EDAS method which is the abbreviation of the evaluation based on distance from average solution is one of the practical methods in circumstances which is with contradictory attributes. Considering the uncertain character of the PHF condition and the psychological factors which influence decision makers’ behaviors such as the character and risk reference, the probabilistic hesitant fuzzy EDAS integrating with cumulative prospect theory (PHF-CPT-EDAS) is built for multiple attributes group decision making (MAGDM) problem. Meanwhile, the information of entropy is also utilized to calculate the unknown weighting vector of attributes. At last, we utilize two case studies to compare the designed method with other MADM methods. Through this article, we learn that the PHF-CPT-EDAS method is effective and stable to solve the MAGDM issues.

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Acknowledgement

The work was supported by the the Humanities and Social Sciences Foundation of Ministry of Education of the People’s Republic of China (No. 17XJA630003).

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Authors and Affiliations

  1. School of Business, Sichuan Normal University, Chengdu, 610101, People’s Republic of China

    Ningna Liao, Hui Gao & Guiwu Wei

  2. School of Economics and Management, Chongqing University of Arts and Sciences, Chongqing, 402160, China

    Rui Lin

  3. School of Accounting, Southwestern University of Finance and Economics, Chengdu, 611130, People’s Republic of China

    Xudong Chen

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  1. Ningna Liao
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  2. Hui Gao
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  4. Guiwu Wei
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Correspondence to Hui Gao.

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Liao, N., Gao, H., Lin, R. et al. An extended EDAS approach based on cumulative prospect theory for multiple attributes group decision making with probabilistic hesitant fuzzy information. Artif Intell Rev 56, 2971–3003 (2023). https://doi.org/10.1007/s10462-022-10244-y

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