*5.2. Comparative Analysis*

A comparative analysis with existing methods is performed to justify the feasibility and superiority of the proposed method. The existing methods include the method proposed by Maji [17] (p. 1), the three methods carried out by Peng and Liu [25] (p. 2) and the aggregated neutrosophic set method [37] (p. 11).

In the decision making method outlined by Maji [17] (p. 1), the final ranking is obtained based on the comparison matrix through briefly comparing with three membership function values. The three neutrosophic soft decision making methods [25] (p. 2) include the non-linear weighted comprehensive method to determine parameter comprehensive weights by combining objective weights and subjective weights. Objective weights are computed by the grey system method, and subjective weights are directly given determinate values. Then, three neutrosophic soft decision making methods are constructed based on EDAS, similarity measure, and the level soft set to rank alternatives in practical problems. Among the three, EDAS and the similarity measure methods obtain the final ranking based on the accurate calculation of alternative evaluation values. In addition, the level soft set method makes a decision by roughly comparing the threshold value with alternative evaluation values. In terms of the aggregated neutrosophic set method [37] (p. 11), alternatives are aggregated using the arithmetic average and sorted by TOPSIS.

Note that there are two crucial issues. On one hand, the above methods all make decisions under a single decision maker. In order to successfully apply them to group decision making, this paper employs the weighted average algorithm to the score of alternatives to all decision makers, based on the decision maker determinacy degree of this study. On the other hand, the method in [17] (p. 1) and [37] (p. 11) does not take parameter weights into consideration. Although the EDAS, similarity measure and level soft set methods [25] (p. 2) comprehensively consider objective weights and subjective weights, the subjective weights are directly given determinate values, which cannot reflect the hesitancies of decision makers under uncertainties. Considering this, the comparative analysis applies the subjective weights obtained from this study to the three methods in [25] (p. 2).

The final ranking of the stochastic multi-criteria group decision making problem mentioned in Section 5.1 are presented in Table 2, by utilizing the proposed method and the methods in [17,25,37] (pp. 1–2, 11). By comparison, the results of the proposed method are consistent with those of most comparison methods, which prove the effectiveness of the proposed method.


**Table 2.** A comparative study with some existing methods.

From Table 2, we can find that the final rankings of the proposed algorithm are different from Maji's method and the level soft set method. The difference can be attributed to two reasons. One is that both methods are approximate comparisons of the alternative evaluation values, and the original evaluation values are not used to the greatest extent. The other is that the threshold value difference of the level soft set method can directly lead to different final rankings. However, decision makers can hardly decide which threshold value to use.

Through comparison, the final rankings of the other three methods are consistent with the proposed method. Among them, EDAS also adopts the aggregation method just as the proposed method. Different from EDAS, the proposed method considers the psychological expectation of decision makers in the borrower selection issue. Thus, in complex group decision making problems, the proposed method can produce more reasonable results than existing methods.

From the above analysis, the main superiorities of the proposed method can be summarized into three aspects. Firstly, this study originally employs neutrosophic soft sets for handling stochastic multi-criteria group decision making problems, which cannot be solved in existing methods. Secondly, the proposed method expresses the weights of subjective weights of parameters by neutrosophic numbers, which can fully reflect the hesitancies of decision makers. Meanwhile, this study presents the weights of decision makers by neutrosophic numbers, which can better incorporate stochastic into the decision making process. Thirdly, the proposed method considers the psychological expectations of decision makers in the borrower selection process. Therefore, it is able to analyze the decision making behavior more objectively.
