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In most existing linguistic distribution multiple criteria group decision-making (MCGDM) models, the sample size of group linguistic distribution assessment (LDA) is often neglected, and the decision makers are viewed to be independent. The probabilistic distribution is closely related to the sample size, and there exist more or fewer interference effects among different individual opinions. Thus, this article develops a quantum framework for modeling interference effects in the linguistic distribution MCGDM process. First, considering the sample size information, we redefine the LDAs and provide a new computational model for them. Second, to integrate a series of LDAs, the linguistic distribution weighted averaging (LDWA) operator is presented. Mathematic proofs reveal that the new LDWA operator can not only ensure the information integrality but also can avoid the conjunctive and disjunctive operations. Third, to explore the interference effects among individual opinions, a quantum framework is constructed. In this process, individual opinions are viewed as various wave functions occurring synchronously. They interfere with each and influence the aggregation result. Finally, an illustrative example of Internet finance soft power evaluation is provided to verify the effectiveness. Sensitivity and comparative analyses are also implemented to assess the stability and validity of our method. [ABSTRACT FROM AUTHOR]

In this article, we deal with an improved fusion method for multigranular linguistic distribution assessments, a flexible and practical expression, in a multiple criteria group decision-making problem, which permits an accurate transformation between two linguistic labels without the loss of information. The importance degree of decision makers is allocated on the basis of individual consistency elicited from evaluation assessments represented by linguistic distributions, which is described with the aid of the proposed dominance degree of linguistic distribution. To intuitively reflect the group consensus level, a new measure, Jaccard consensus of linguistic distribution, is introduced. Based on this, a consensus-based multicriteria group preference analysis model with multigranular linguistic distribution information is established within a unified framework, which is developed by means of additive multiattribute value utility theory. Furthermore, a corresponding algorithm is matched to obtain a representative value function for decision analysis and an illustrative example is developed to illustrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Pythagorean fuzzy sets, originally proposed by Yager (IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), pp 57-61, 2013), which are convenient to describe vagueness and impression in real-world problems, can be regarded as a novel extension of intuitionistic fuzzy sets. Generalized Maclaurin symmetric mean comes as an extension of classical Maclaurin symmetric mean. Their prominent feature is that they can both capture overall interrelationships and also focus on individual importance among the arguments. However, the generalized Maclaurin symmetric mean can only be considered for aggregating numeric information. In this paper, we investigate the generalized Maclaurin symmetric mean and further extend to Pythagorean fuzzy environment. We develop the Pythagorean fuzzy generalized Maclaurin symmetric mean (PFGMSM), the Pythagorean fuzzy weighted generalized Maclaurin symmetric mean (PFWGMSM), and discuss a variety of their desirable properties. To better understand the proposed parameterization operators, we provide an innovative way to extend the classical superiority and inferiority ranking (SIR) group decision model to Pythagorean fuzzy environment. Then, an approach formed with the aid of PFWGMSM and SIR to multiple attribute group decision-making with Pythagorean fuzzy information is developed. Finally, an illustrative example concerning supply chain financial risk decision-making is provided to verify the proposed method and demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

The influence of socially responsible investment (SRI) has caused much attention from both institutional and individual investors in capital markets. SRI considers the corporate social responsibility (CSR) criteria of firms is becoming an emerging topic in economics and management. The aim of this paper is to develop a hybrid SRI portfolio selection model with multi-criteria decision making (MCDM) and multi-objective optimization problem (MOOP) techniques. First, a multi-dimensional evaluation criteria system containing fundamental financial indicators, CSR criteria, and stock market factors, is put forward for SRI to make the decision results more comprehensive and applicable. Second, a multi-stage MCDM decision mechanism including the affinity propagation clustering (APC) algorithm, the best-worst method (BWM), and the MULTIMOORA (Multi-Objective Optimization on the basis of a Ratio Analysis plus the full MULTIplicative form) is implemented. The APC algorithm is used for the reduction of financial indicators. The BWM is applied to determine the financial and CSR criteria weights. The MULTIMOORA method is integrated to derive the financial and CSR performance of the firms. Third, a multi-objective SRI portfolio selection model adding financial, CSR, and stock market performance is constructed, which serves as an extension of the classical mean-variance model. The compromise solution is utilized when solving the MOOP. Finally, a case study related to the medical stock investment is examined to recommend the optimal portfolio allocation for investors. Sensitivity and comparative analyses are performed to demonstrate the robustness, effectiveness, and superiority of the proposed methodology. • Develop a hybrid SRI portfolio selection model with MCDM and MOOP techniques • A multi-dimensional evaluation criteria system is put forward. • A multi-stage MCDM decision mechanism is implemented. • A multi-objective SRI portfolio selection model is constructed. • A case study related to the stock investment during the COVID-19 is examined. [ABSTRACT FROM AUTHOR]

The Maclaurin symmetric mean (MSM) was originally introduced by Maclaurin and then generalized by Detemple and Robertson. The prominent characteristic of the MSM is that it can capture the interrelationship among the multi-input arguments. However, the researches on MSM are very rare, especially in fuzzy decision making. In this paper, we investigate the MSM operator and extend the MSM operator to intuitionistic fuzzy environment. Some new aggregation operators based on MSM for dealing with intuitionistic fuzzy information are developed, such as the intuitionistic fuzzy Maclaurin symmetric mean (IFMSM) and the weighted intuitionistic fuzzy Maclaurin symmetric mean (WIFMSM). Some desirable properties and special cases of these operators are discussed in detail. Based on WIFMSM operator, an approach to multiple attribute decision making (MADM) problems with intuitionistic fuzzy information is developed. Finally, a practical example is provided to illustrate the practicality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a method based on Choquet integral is proposed to solve the interval intuitionistic fuzzy multiple attribute group decision making problems. Firstly, some concepts about interval intuitionistic fuzzy measure are defined, through the strict mathematical reasoning to prove the measure we proposed satisfying the axiomatic system of fuzzy measure. Then, on the basis of fuzzy measure and game theory, we propose two models to determine fuzzy measure based on interval intuitionistic fuzzy entropy and weight information matrix. By calculating the Shapely value to determine expert weights, we establish a linear programming model based on relative entropy to determine the fuzzy measure of attribute weights to reflect the interactive characteristics among the criteria, using the Choquet integral to aggregate the decision-making information. Finally, we give the process of decision making in details. [Copyright &y& Elsevier]

In most existing multi-criteria group decision making (MCGDM) problems, the decision makers (DMs) are usually regarded as independent and DMs' psychological behaviors are rarely considered. However, in many cases, the opinions of DMs are likely to influence each other and the DMs often hold subjective bounded rationality in the decision making process. To address this issue, a linguistic distribution behavioral MCGDM model integrating extended generalized TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) method and quantum decision theory is developed. First, linguistic distribution assessments considering the sample size information are used to gather the group linguistic evaluations, which can integrally depict the quantitative distribution and qualitative vagueness. Second, an extended generalized linguistic distribution TODIM method is presented to reflect the DMs' bounded rational behaviors. It is used to calculate the dominance of each alternative. Third, a quantum possibilistic aggregation framework is constructed to explore the interference effects among DMs' opinions. In this process, DMs' opinions are viewed as synchronously occurred wave functions that interfere with each other and influence the aggregated result. Finally, a case study is examined to recommend the optimal automobile for customers based on distributed online reviews in newcar.xcar.com and autohome.com. Three separate scenarios with conjunctive, neural, and disjunctive modes are considered respectively. The sensitivity analysis is given to show the effect of risk factors and interference factors. The parameters show to be dominant in the final best automobile recommendation. The comparisons with some existing methods confirm the validity and stability of the proposed MCGDM method. • Develop a novel linguistic distribution behavioral MCGDM model. • Integrate the generalized TODIM method and quantum decision theory. • Explore the interference effects among DMs' opinions in a quantum framework. • Apply the proposed model to the online automobile recommendation. [ABSTRACT FROM AUTHOR]

• Multi-granular linguistic distribution evidential reasoning method is proposed. • Incomplete and uncertain evaluation is handled in MCGDM problems. • A novel comparison method for linguistic distribution assessments is introduced. • Developed a transformation technique for multi-granular linguistic distribution. • Renewable energy project risk assessment is conducted. Nowadays, renewable energy projects have constantly been emphasized and the assessment of potential risks has been considered as an indispensable activity prior to implementation of projects. To carry out a reasonable assessment, multi-granular linguistic distribution assessments (LDAs), an effective and uncertainty representation tool, are adopted to express and quantify opinions based on personalized individual linguistic term sets. Specifically, this study develops a novel uncertain multiple criteria decision making approach, named multi-granular linguistic evidential reasoning method, which can handle incomplete and personalized preferences. A novel comparison method for LDAs is first put forward based on numerical characteristics, namely an expectation value and central moment. To fuse the multi-granular LDAs, a lossless transformation technique is further introduced and some of itsessential properties are discussed. Finally, a case study on renewable energy project risk assessment is discussed to verify the feasibility of the proposed method. Besides, sensitivity analysis is conducted to investigate the sequencing stability and comparative analysis is included to highlight the superiority of the proposed method. [ABSTRACT FROM AUTHOR]