Abstract
In recent years, some scholars have put forward the concept of green finance, which is to integrate the environmental protection concept of green development into the traditional financial industry. Green credit is one of the important financial services in green finance. In order to construct a more perfect multiple attribute group decision-making (MAGDM) method, this paper proposes a new evaluation based on distance from average solution (EDAS) model based on prospect theory and probabilistic uncertain linguistic term set (PT-PULTS-EDAS model). The PT-PUTLS-EDAS model fully takes the psychological state of decision-makers into account the mathematical logic of the method, which is more suitable for the problem of MAGDM in reality. In addition, the new PT-PULTS-EDAS model reduces the decision-maker's subjective judgment on some objective situations as much as possible. The model uses Criteria Importance Through Intercriteria Correlation (CRITIC) method to obtain the initial attribute weights and makes use of the weighting function to alter the initial weights so that successfully avoids the distortion of subjective judgment to the actual probability to some extent. Moreover, different parameters are introduced in this method to further deal with the part above and below the standard point respectively so that different types of decision-makers' attitudes towards gains and losses are taken into account. In the fifth part, we focus on green finance, prove the usability of this method through an example, and draw a reasonable and scientific conclusion based on the results of comparative analysis. In the future, we will continue to focus on the relevant theories and methods about MADM or MAGDM.
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YS, MZ, GW, CW and XC discussed, constructed the mathematical models and found their applications. MZ, GW and CW wrote the paper together.
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Su, Y., Zhao, M., Wei, G. et al. Probabilistic Uncertain Linguistic EDAS Method Based on Prospect Theory for Multiple Attribute Group Decision-Making and Its Application to Green Finance. Int. J. Fuzzy Syst. 24, 1318–1331 (2022). https://doi.org/10.1007/s40815-021-01184-w
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DOI: https://doi.org/10.1007/s40815-021-01184-w