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Today, Xiaobian brings you a 5.5 example analysis of the intensive doctoral dissertation "Multi-attribute Group Decision Making Method Considering Expert Consensus under Generalized Multiplicative Preference Information" (2).
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Today, the editor brings the " 5.5 example analysis (2) of the intensively read PhD thesis 'A Multi-Attribute Group Decision-Making approach considering expert consensus under generalised nultiplicative preference information'".
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一、内容摘要（Content Summary）
In this issue, we will introduce the 5.5 case analysis of the doctoral dissertation "Multi-attribute Group Decision Making Method Considering Expert Consensus under Generalized Multiplicative Preference Information" from three aspects: mind map, intensive reading content, and knowledge supplementation (2).
This issue of the article will introduce the 5.5 example analysis (2) from three aspects: mind mapping, in-depth reading content, and supplementary knowledge, focusing on the doctoral dissertation "Multi-attribute Group Decision-making Method Considering Expert Consensus under Generalized Multiplicative Preference Information".
二、思维导图（Mind Mapping）
三、精读内容（Detailed Reading Content）
（一）对比分析（Comparative Analysis）
In order to better explore the effectiveness of the MAGDM method based on IMPR considering consistency and consensus proposed in this chapter, the ranking results obtained in this chapter are compared with the results obtained by the other six methods, and the specific decision results and parameter comparison analysis results are shown in the figure below. Among them, the expert weight information, consensus level threshold, and some parameters in the six comparison methods all follow the existing values of each method, but the consensus and consensus level calculation methods use the measures constructed in this chapter.
To better explore the effectiveness of the MAGDM method proposed in this chapter, which is based on the consideration of consistency and consensus using IMPR, the ranking results obtained in this chapter are compared with the results obtained by six other methods. The specific decision results and parameter comparison analysis are shown in the figure below. Among these six comparative methods, the expert weight information, consensus level threshold, and some parameters are all based on the existing values of each method. However, the consistency and consensus level calculation methods are uniformly based on the measures constructed in this chapter.
As can be seen from the above table, the optimal alternative for the decision-making method proposed in this chapter is A4, while the optimal alternative for the decision-making method that does not consider the consensus level among experts is mostly A3, and the result of the method considering the consensus level in the existing IMPR environment is also A3. The authors then performed a detailed analysis of the results, as shown in the figure below.
From the table above, it can be seen that the optimal alternative solution obtained by the decision method proposed in this chapter is A4, while the optimal alternative solutions obtained by the decision methods that do not consider the consensus level among experts are mostly A3. Additionally, the results obtained by the methods considering the consensus level under the existing IMPR environment are also A3. The authors then conducted a detailed analysis of the results, as shown in the figure below.
Based on the above analysis, it can be concluded that the multi-attribute group decision making (MAGDM) process constructed in this chapter has the following advantages in dealing with decision events under Intuitive Multiplicative Preference Information (IMPR).
Based on the analysis above, the advantages of the Multi-Attribute Group Decision Making (MAGDM) process constructed in this chapter for dealing with decision events under Intuitionistic Multiplicative Preference Information (IMPR) are as follows.
(1) In the proposed method, the measurement criteria for the consistency of IMPR under the scenario of different degrees of expert participation in the evaluation are defined, and the IMPR consistency optimization method when experts participate in the re-evaluation and those who do not participate in the re-evaluation are constructed respectively, so as to improve the accuracy and rationality of the MAGDM results; (2) In the proposed method, the calculation method of the consensus level between IMPRs in the context of different degrees of expert participation in the evaluation is defined, and the consensus reaching method of the IMPR when experts participate in the re-evaluation and those who do not participate in the re-evaluation are constructed, so as to improve the reliability of the MAGDM results; (3). (4) In this chapter, the induction set settlement sub under IMPR is constructed, and the consistency and consensus levels of IMPR given by different experts are used as inducing components to automatically generate the weight information of decision-makers, which effectively improves the solution of decision-makers' weights in information assembly.
(1) The proposed method in this chapter defines a measure criterion for the consistency of IMPR in situations where the degree of expert participation in the evaluation is different, and constructs consistency optimization methods for IMPR when experts participate in reassessment and when they do not, thereby improving the accuracy and rationality of the MAGDM results; (2) The proposed method in this chapter defines a method for calculating the consensus level among IMPR in situations where the degree of expert participation in the evaluation is different and constructs methods for achieving consensus among IMPR when experts participate in reassessment and when they do not, thereby improving the reliability of the MAGDM results; (3) The feasibility and rationality of the iterative algorithm for improving the consistency and consensus level of experts not participating in reassessment proposed in this chapter have been proven; (4) This chapter constructs the induction set settlement operator under IMPR, which uses the consistency and consensus level of IMPR provided by different experts as the inducing components to automatically generate the weight information of decision makers, effectively improving the solution process of decision maker weights in information aggregation.
（二）管理启示（Managerial Insights）
In the management enlightenment part, the author first introduces the specific research content of this chapter: this chapter uses the results of the pairwise comparative evaluation of alternative alternatives by experts to represent the intuitive multiplicative preference information, constructs two methods for improving the consistency and consensus level under the intuitive multiplicative preference information, and proposes to induce the intuitionistic multiplicative set settlement to obtain the group evaluation information according to the difference in the consistency and consensus level between the preference information given by different experts, and forms a group decision-making process based on the consistency and consensus level under the intuitive multiplicative preference information.
In the managerial insights section, the authors first introduced the specific research content of this chapter: This chapter uses intuitionistic multiplicative preference information to represent experts' pairwise comparison evaluation results of alternative solutions, and constructs two methods for improving consistency and consensus levels under two different intuitionistic multiplicative preference information. Based on the difference in consistency and consensus levels among different experts' preference information, the authors propose an induced intuitionistic multiplicative aggregation operator to obtain group evaluation information, forming a group decision-making process based on consistency and consensus levels under intuitionistic multiplicative preference information.
Then, the author introduces the practical application of the methods constructed in this chapter, as shown in the figure below.
Then, the authors introduced the practical application of the method constructed in this chapter, as shown in the figure below.
According to the research content of this chapter, it can be concluded that the MAGDM method based on the intuitionistic multiplicative preference information proposed in this chapter can solve the following key problems in practical decision-making. (1) the identification and improvement of non-congruent and non-consensus preference information when experts are willing or necessary to participate in the re-evaluation work, (2) the acceptable consistency and consensus conditions of the preference information when experts are unwilling or not required to participate in the re-evaluation work, (3) the objective determination of expert weights, and (4) the continuation of group preference information consistency, which provides a certain guiding role for complex decision-making problems under specific conditions.
Based on the research content of this chapter, it can be concluded that the MAGDM method proposed under intuitionistic multiplicative preference information in this chapter can solve the following key problems in actual decision-making work: (1) Identification and improvement of non-consistent and non-consensus preference information when experts are willing or necessary to participate in reassessment work; (2) Reaching acceptable consistency and consensus conditions of preference information when experts are unwilling or do not need to participate in reassessment work; (3) Objective determination of expert weights; (4) Continuation of consistency of group preference information, providing guidance for complex decision-making problems under specific conditions.
四、知识补充——决策偏好（Supplementary Knowledge - Decision Preferences）
Preference refers to a concept in utility theory. It refers to the attitude of the decision-maker towards the benefits and risks. The degree to which a decision-maker's preference for a solution or outcome is strong is called the degree of preference. Preferences can be divided into the following three types: (1) Risk-averse type, that is, equal gains and losses, and only when the probability of loss is less than 1/2, the decision-maker may invest. (2) Risk-seeking type, which is the opposite type of aversion. (3) Relative risk neutrality, that is, the introduction of risk has no obvious effect on the preference of decision-makers.
Preference is a concept in utility theory. It refers to the attitude of a decision-maker towards benefits and risks. The degree of preference of a decision-maker for a scheme or consequence is called the degree of preference. Preferences can be divided into the following three types: (1) Risk-averse type, that is, for equal benefits and losses, a decision-maker may only invest when the probability of loss is less than 1/2; (2) Risk-seeking type, which is the opposite of risk-averse type; (3) Relatively risk-neutral type, that is, the introduction of risk has no obvious effect on the preference of decision-makers.
Preference is actually an emotion and tendency hidden in people's hearts, it is non-intuitive, and there are more perceptual factors than rational factors that cause preference. There are obvious individual differences in preferences, and they also show group characteristics.
Preferences are actually a kind of emotion and tendency hidden in people's hearts. They are non-intuitive and are more influenced by emotional factors than rational factors. Preferences exhibit significant individual differences and also show group characteristics.
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参考资料：ChatGPT、百度百科
Bibliography:
WANG Rui. Multi-attribute group decision-making method considering expert consensus under generalized multiplicative preference information[D]. Sichuan: Southwest Jiaotong University, 2022.
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