Three-way multi-attribute decision-making (3W-MADM) in fuzzy environments provides an effective framework for handling uncertain information by introducing a boundary domain, which reduces misjudgment risks and enhances fault tolerance. However, existing 3W-MADM research is largely confined to fine-grained perspective and still faces shortcomings such as non-compliance with the computational principle of conditional probabilities, neglect of the decision-maker's psychological factors, and low decision-making efficiency. Based on this, this paper proposes a multi-granular 3W-MADM method with regret theory based on q-rung orthopair fuzzy granular-balls (q-ROFGBs) from a coarse-grained perspective. First, the q-ROFGBs are developed by combining qrung orthopair fuzzy sets with granular-balls and the generation algorithm is designed. Second, a computational method for conditional probabilities with proper semantic interpretation based on q-ROFGBs is introduced. Third, regret theory is incorporated to account for the influence of psychological factors in decision-making. Next, a new 3W-MADM method with regret theory based on q-ROFGBs is established. Finally, the effectiveness, practicality, and flexibility of the proposed method are demonstrated through experimental analysis on both small-scale synthetic datasets and a large-scale real-world depression dataset. Compared with existing 3WMADM methods, the proposed method not only incorporates the impact of regret psychology but also achieves the shorter decision-making time.

Consensus in group decision-making has become a hotspot to ensure the agreement opinions of decision makers (DMs). The irrational behaviors of DMs, such as confidence, will impact the consensus results, which should be considered. In addition, the existing self-confidence level directly given by DMs rather than exacted from evaluation information may generate malicious manipulation. Furthermore, double-hierarchy hesitant fuzzy linguistic term set (DHHFLTS) is an effective tool to express the complex evaluations of DMs. In this paper, the endo-confidence of DHHFLTS to reflect confidence of DMs from the perspective of evaluation information is defined. Then, we propose a novel consensus model with endo-confidence of DMs based on DHHFLTSs. First, some improved operators of DHHFLTSs are developed. Second, the weight is determined based on both entropy and endo-confidence. Due to the fact that the consensus threshold should decrease as the endo-confidence increases, we give a novel method to obtain the consensus threshold considering endo-confidence level. Moreover, the two-stage adjustment mechanism is presented for non-consensus DMs and the selection process is constructed. Finally, an illustrative example is carried out to demonstrate the feasibility of the proposed model, and a series of comparative analysis is used to show its stability.

Consensus plays a pivotal role in large-scale group decision-making, and the social network relationship significantly impacts the consensus reaching process (CRP). Consequently, there is considerable focus on achieving large-scale consensus within social network (LSC-SN). Typically, experts are used to expressing their evaluations with language that can be effectively characterized by probabilistic linguistic term set (PLTS). However, the existing distance measure of PLTS exhibits drawbacks, and PLTSs provided by experts might be incomplete. To address the two challenges, this research defines an improved distance measure and proposes an estimation method for incomplete PLTS from the perspectives of trust relationships, collaborative filtering, and confidence level. Subsequently, in the CRP, a hierarchical clustering algorithm based on the trust-similarity measure is developed to cluster experts who share mutual trust and similar evaluations. Then, a consensus model employing dynamic trust and optimal reference is introduced to achieve both intra-cluster and inter-cluster consensus simultaneously. Finally, to illustrate the feasibility and advantage of the proposed model, a numerical experiment and the comparative analyses are conducted.

Consensus is an important issue in group decision making to make a reliable and scientific decision, and it has become a hot topic recently. Due to the complexity and uncertainty of decision-making problems, several aspects of alternatives should be considered in decision-making process. Also, the bounded rational characteristics of decision makers (DMs) may significantly affect the decision results. This paper proposes a multi-criteria consensus model based on prospect theory (PT) to consider the unsymmetrical risk attitudes of DM for gain or loss and on endo-confidence (confidence which is derived by DMs' evaluation information) levels of DMs to determine their weights. First, the reference point in PT is selected and how to measure the prospect consensus degree is presented. Then, the way to identify the expert who need to modify his/her evaluation is given and two programming models are proposed to obtain the updated evaluation. Subsequently, both the overall difference and the endo-confidence level of the DM are used to determine the weights of DMs. Finally, an illustrative example and the comparative analysis are used to demonstrate the feasibility of the proposed model.

In real decision-making problems, decision makers (DMs) usually select the most potential project from several ones. However, they unconsciously show different confidence levels in decision-making process because they come from various backgrounds and have different experiences, etc., which affects the decision results. Moreover, the probabilistic linguistic term set, which not only includes the linguistic expressions used by DMs in their daily life but also contains the probability for each linguistic term, can well portray the real perceptions of DMs for the projects. Furthermore, large-scale consensus has gradually been a popular way to effectively solve complex decision-making problems. To sum up, in this paper, we are dedicated to constructing a large-scale consensus model considering the confidence levels of DMs under probabilistic linguistic circumstance. Firstly, the endo-confidence is defined and measured by DM’s probabilistic linguistic information. Then, the DMs are clustered according to the similarities of both evaluation information and the endo-confidence levels. Both evaluation of the non-consensus cluster and evaluation integrated by the clusters with higher endo-confidence level than this non-consensus cluster are used as the reference to adjust its evaluation information. Then, a case study and the comparative analysis are carried out. Finally, some conclusions and future work are given.

Decision making is undertaken by individuals whose perceptions for the objectives are vague due to the dynamics of decision-making objectives, the complexity of decision-making situation and the limitations of individuals’ education backgrounds, experiences, etc. Therefore, decision making which uses fuzzy set (FS) and its variants to describe the individuals’ vague perceptions has become more and more popular. Furthermore, in real-life decision-making situations, individuals are always bounded rationality, which is clearly described by prospect theory (PT). Thus, PT has been integrated into fuzzy decision making (FDM). Since then, FDM with PT have been rapidly developed. In this paper, we systematically analyze the existing FDM with prospect framework. Firstly, a detailed analysis about the existing research on this topic is given separately, including how to determine the reference point, how to express the value function and weighting function in PT under vague conditions. With this in mind, the applications and challenges of the existing methods are described and some possible directions for future research are provided. According to our review, it is worth mentioning that the FDM with psychological factors of decision makers will surely receive much more attention from researchers and practitioners in the future.

With the development of novel technological and societal paradigms, consensus in multi -attribute large-scale group decision making is of great significance. The confidence derived by evaluation is considered in this paper and it is named as endo-confidence. Then, a novel adaptive consensus model to manage endo-confident behavior is proposed. First, the experts are classified by their evaluations as the first cluster, and then by their endo-confidence levels in each cluster named as sub-cluster. Furthermore, the new method con-sidering endo-confidence level is introduced to determine the weights. To better manage consensus reaching process, we further discuss three types of experts in the non -consensus cluster to adjust their evaluations, including i) experts who are lack of endo-confidence, ii) experts who are over endo-confidence, and iii) experts whose evaluations greatly deviate from the overall level. Next, an automatic feedback mechanism which con-siders both evaluations and endo-confidence levels of experts is proposed for the non -consensus experts. Finally, a case study is carried out to demonstrate the feasibility of the proposed model, and a series of comparative analyses are used to show its stability.(c) 2022 Elsevier Inc. All rights reserved.

The fundamental principle of QUALIFLEX is to treat the cardinal and ordinal information in a correct way and to take all the possible rankings of alternatives into account. The focus of QUALIFLEX is the pairwise comparison of alternatives with respect to each attribute under all possible permutations. The optimal permutation is recognized through the comprehensive concordance/discordance index, and the best alternative will be identified according to it.

In real-world decisions, we often encounter situations when decision-makers' (DMs') preferences can only be expressed as uncertain linguistic terms instead of crisp values. Similarly, when decisions involving several risky prospects with linguistic outcome information, it is a challenge to properly calculate the corresponding prospect values. To address this issue, this paper proposes a decision-making framework based on prospect theory where the outcomes are characterized by probabilistic linguistic term sets (PLTSs). The key contributions of this research are twofold: Firstly, it allows DMs to express their assessment of outcomes in terms of linguistic terms with interval probabilities. Secondly, it furnishes a paradigm to extend prospect theory to accommodate other forms of fuzzy and linguistic input. To begin with, this paper first presents different types of PLTSs. Then, gains and losses are calculated based on the positive and negative reference points and the operation rules of PLTSs. In accordance with the value and probability weight functions, the weighted prospect values are determined. Finally, we apply the decision-making framework to a practical case to illustrate its feasibility under linguistic environment.

Multi-criteria decision making (MCDM) is a common method used to solve complex decision-making problems. One such method, TODIM (TOmada de Decisão Iterativa Multicritério), is derived from prospect theory, which considers the psychological behaviors of decision makers (DMs). The perceptions of DMs of the alternatives may be uncertain because, for example, of complex decision-making circumstances or their limited knowledge. Therefore, fuzzy sets (FSs) have been used to describe DMs’ vague perceptions. The combination of TODIM with different types of FSs has developed to deal with different uncertain decision-making problems. We systematically analyze TODIM with different types of FSs to show its state and possible future direction. A bibliometric analysis of existing research on this topic is given, followed by an analysis of the dominance function of TODIM, including how to represent a gain or loss, and how to obtain the original and relative weights. We then describe the combination of TODIM with other methods. Applications of current methods are summarized, and some challenges and possible directions of future research are provided. Fuzzy MCDM with the psychological factors of decision makers will surely receive increased attention from researchers and practitioners in the future.

This study investigates the role of state ownership and corruption expenditure in the relationship between economic policy uncertainty (EPU) and corporate risk-taking. The finding shows that EPU is significantly positively correlated with corporate risk-taking in China. The state ownership affects the link between EPU and corporate risk-taking, and SOEs are more willing to avoid risks in response to the increases of EPU. Corruption expenditure positively affects the relationship between EPU and corporate risk-taking. Compared with SOEs, non-SOEs have more incentive to respond to an increase of EPU through corruption expenditure. Moreover, China's anti-corruption campaign strongly affects the above effects. Our results are robust to alternative variable measures and endogeneity tests.

Based on the heterogeneity of innovation quantity and quality, this study investigates the impact of political connections on enterprises' innovation. Using the data of Chinese listed enterprises from 2003 to 2015, we find that political connections have a positive impact on enterprises' innovation quantity, but they are detrimental to innovation quality. We further find that political connections weaken the promotion of government subsidies on innovation quality and even reduce the R&D intensity of enterprises, which serves as an essential determinant of innovation quality. The government can stimulate enterprises' innovation quality by improving marketization, intellectual property protection, and anti-corruption.

There are always bifurcation points among the DMs in GDM problems. If the DMs ignore those bifurcation points and just simply integrate each DM’s opinion, it may lead unreasonable decision-making result.

Although the classical TODIM considers relative importance of attributes, this method neither provides an appropriate way to determine the weights of attributes.

Government research and development (R&D) subsidy is one of the main policy instruments to deal with market failure, and its effectiveness has attracted attention increasingly. This study investigates the impact of two types of government R&D subsidies on innovation using the data of Chinese listed enterprises from 2010 to 2016. We find that compared with ex-post rewards, ex-ante grants have a better effect on innovation performance by stimulating private R&D investment. Additionally, the effectiveness of government R&D subsidies is weakened in enterprises engaging in rent-seeking and political connections. This study provides a new perspective for understanding the effect of government R&D subsidies, and the research conclusions are the relevant reference for the government to improve the efficiency of allocating public funds.

This paper proposes a consensus model for multi-experts multi-criteria decision making (MEMCDM) problems with probabilistic linguistic term sets (PLTSs), which also considers the regret-rejoice emotions of decision makers (DMs) in their decision-making processes. Additionally, the Dempster-Shafer theory is applied to estimate the probability of the market status which is related to the perceived values of the alternatives. Moreover, an algorithm is given to determine the weight of each DM. Then, a detailed consensus procedure is proposed and an illustrative example is used to show the feasibility of the proposed model. Finally, some comparative analyses are carried out to demonstrate the advantages of the proposed consensus process.

The IFS [1] is an effective tool to comprehensively express the uncertain perceptions of DMs for the objects from the perspective of support and opposition. Hence, the extensions of IFS have been popular, such as PFS, q-ROFS, etc. Moreover, PT is famous for its ability in depicting the bounded rational psychological characteristic of DMs under uncertainty.

The PROMETHEE is an outranking method based on the relative preferences. It is a family of methods developed to solve different kinds of ranking problems. For example, the PROMETHEEs I (Bransin L'ingénièrie de la décision; Elaboration d'instruments d'aide à la décision. La méthode PROMETHEE. In: L’aide à la décision: Nature, Instruments et Perspectives d’Avenir [1]) and II (Brans and Vincke in Manage Sci 31:647–656, 1985 (Brans and Vincke in Manage Sci 31:647–656, 1985 [2])) were introduced for partial and complete rankings of alternatives correspondingly.

Green supply chain has developed rapidly due to the advocacy of ecological civilization, and choosing a proper green supplier is a crucial issue. Considering the fuzziness of evaluation information and the psychological states of decision makers (DMs) in selecting process, a novel TODIM based on prospect theory with q-rung orthopair fuzzy set (q-ROFS) is proposed. The novel TODIM concerns both the perceived transformed probability weighting function and the differences in risk attitudes. A new distance, which concerns the herd mentality, is carried out to measure the perceived difference of the q-ROFS. Besides, a new systematic evaluation index system, named as PCEM (Product, Cooperation ability, Environment, Market), has been established. A case related to pork supplier companies is presented and fully demonstrates the effectiveness of the novel TODIM when compared with the extended one, the intuitionistic fuzzy TODIM, the Pythagorean fuzzy TODIM as well as the TOPSIS with q-ROFS. Finally, a series of comparative analyses illustrate the advantages of the proposed TODIM.

China's overcapacity in the coal industry has become increasingly prominent since 2013, with a severe negative impact on resource allocation and the national economy. The Chinese government has implemented a series of de-capacity policies to resolve overcapacity and improve the total factor productivity (TFP) in the coal industry. In this study, we aim to explore whether the de-capacity policy enhances the TFP growth of China's coal companies. We firstly apply the Super-SBM-Malmquist index method to measure the TFP and its decomposition items (e.g., technical change, efficiency change) of China's coal industry. We then construct the Regression Discontinuity (RD) design to examine the impact of de-capacity policy on the TFP growth of the coal companies. The main results are as follows: (1) After the implementation of the de-capacity policy in 2016, the average TFP growth and technical change of the coal companies have increased significantly. (2) The de-capacity policy can dramatically promote TFP growth and technical change of the coal companies. In contrast, it has a significant and negative effect on efficiency change. (3) The positive impact of de-capacity policy on technical change is more significant than its adverse effects on efficiency change, which is the main reason for the increase in TFP growth. Therefore, the government should give full play to the positive role of administrative means in resolving overcapacity, and promote sustainable technological innovation of the coal industry.