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Probabilistic hesitant fuzzy multiple attribute decision-making based on regret theory for the evaluation of venture capital projects
被引:34
|作者:
Liu, Xiaodi
[1
]
Wang, Zengwen
[2
]
Zhang, Shitao
[1
]
Liu, Jiashu
[3
]
机构:
[1] Anhui Univ Technol, Sch Math & Phys, Maanshan, Anhui, Peoples R China
[2] Wuhan Univ, Researching Ctr Social Secur, Wuhan, Hubei, Peoples R China
[3] Anhui Univ Technol, Sch Business, Maanshan, Anhui, Peoples R China
来源:
ECONOMIC RESEARCH-EKONOMSKA ISTRAZIVANJA
|
2020年
/
33卷
/
01期
基金:
中国国家自然科学基金;
关键词:
probabilistic hesitant fuzzy set (P;
H;
F;
S;
multiple attribute decision-making;
probability calculation;
mathematical programming model;
investment decision;
PROSPECT-THEORY;
INFORMATION;
MODEL;
REPRESENTATION;
AGGREGATION;
CONSENSUS;
SELECTION;
CHOICE;
RISK;
SETS;
D O I:
10.1080/1331677X.2019.1697327
中图分类号:
F [经济];
学科分类号:
02 ;
摘要:
The selection of venture capital investment projects is one of the most important decision-making activities for venture capitalists. Due to the complexity of investment market and the limited cognition of people, most of the venture capital investment decision problems are highly uncertain and the venture capitalists are often bounded rational under uncertainty. To address such problems, this article presents an approach based on regret theory to probabilistic hesitant fuzzy multiple attribute decision-making. Firstly, when the information on the occurrence probabilities of all the elements in the probabilistic hesitant fuzzy element (P.H.F.E.) is unknown or partially known, two different mathematical programming models based on water-filling theory and the maximum entropy principle are provided to handle these complex situations. Secondly, to capture the psychological behaviours of venture capitalists, the regret theory is utilised to solve the problem of selection of venture capital investment projects. Finally, comparative analysis with the existing approaches is conducted to demonstrate the feasibility and applicability of the proposed method.
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页码:672 / 697
页数:26
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