Regret Theory Research Articles

Regret cross-efficiency evaluation using attitudinal entropy approach

Data envelopment analysis (DEA) is a popular nonparametric tool for measuring the performance of decision making unit (DMU). Cross-efficiency evaluation is an efficient means to rank DMUs, which compensates for efficiency overestimation and incomplete ranking of conventional DEA models. With respect to incomplete rational cross-efficiency evaluation, the common methods construct models with two extreme efficiencies as reference, such as efficiency values of the aggressive and benevolent models. In addition, they only explore the irrational preferences of decision maker (DM) at a certain stage in the evaluation process. They fail to investigate the cross-efficiency evaluation synthetically under incomplete rationality and characterize the complexity of the individuals’ decision making. To fill this gap, a new method must be proposed. This paper proposes a new cross-efficiency termed regret cross-efficiency model using attitudinal entropy approach (RACE), which constructs a secondary goal model from a comprehensive perspective under the framework of regret theory, and introduces attitudinal entropy to aggregate the cross-efficiencies. Besides, this paper presents some empirical examples to illustrate the validity and robustness of the RACE method.

Decision threshold models in medical decision making: a scoping literature review

BackgroundDecision thresholds play important role in medical decision-making. Individual decision-making differences may be attributable to differences in subjective judgments or cognitive processes that are captured through the decision thresholds. This systematic scoping review sought to characterize the literature on non-expected utility decision thresholds in medical decision-making by identifying commonly used theoretical paradigms and contextual and subjective factors that inform decision thresholds.MethodsA structured search designed around three concepts—individual decision-maker, decision threshold, and medical decision—was conducted in MEDLINE (Ovid) and Scopus databases from inception to July 2023. ProQuest (Dissertations and Theses) database was searched to August 2023. The protocol, developed a priori, was registered on Open Science Framework and PRISMA-ScR guidelines were followed for reporting on this study. Titles and abstracts of 1,618 articles and the full texts for the 228 included articles were reviewed by two independent reviewers. 95 articles were included in the analysis. A single reviewer used a pilot-tested data collection tool to extract study and author characteristics, article type, objectives, theoretical paradigm, contextual or subjective factors, decision-maker, and type of medical decision.ResultsOf the 95 included articles, 68 identified a theoretical paradigm in their approach to decision thresholds. The most common paradigms included regret theory, hybrid theory, and dual processing theory. Contextual and subjective factors that influence decision thresholds were identified in 44 articles.ConclusionsOur scoping review is the first to systematically characterizes the available literature on decision thresholds within medical decision-making. This study offers an important characterization of the literature through the identification of the theoretical paradigms for non-expected utility decision thresholds. Moreover, this study provides insight into the various contextual and subjective factors that have been documented within the literature to influence decision thresholds, as well as these factors juxtapose theoretical paradigms.

A behavior three-way decision approach under interval-valued triangular fuzzy numbers with application to the selection of additive manufacturing composites

Additive manufacturing composites, also recognized as three-dimensional (3D) printing composites, are highly anticipated for their potential to replace industrial materials due to the availability of multiple printing processes and optional materials. However, research gaps exist in cognitive deficiencies and psychological behaviors of decision-makers, as well as experimental error effects caused by material testing, resulting in material selection as a challenging issue. Therefore, this study proposes a novel behavior three-way decision model under the interval-valued triangular fuzzy number (IVTFN) to settle the selection issue of 3D printing composites. The research contributions are summarized as follows. First, the IVTFN is presented to account for the impacts of cognitive deficiency and experimental errors, based on which the concepts of information entropy and fuzzy measure are further developed to conduct the criterion weights. In addition, by integrating the prospect theory and regret theory, a framework for constructing the behavioral decision matrix is presented. Moreover, a novel behavior three-way decision model with the perspectives of objective and preference is proposed to classify the decision region. This study presents a comprehensive methodology integrating the three-way decision model and multi-criteria decision-making method to achieve both alternative ranking and alternative classifying. Finally, a research case of 3D printing composites reinforced by continuous hybrid fibers is adopted to illustrate the validity of the methodology. Comparative analysis and sensitivity analysis are also performed. This study offers valuable insights and tools for systematically tackling the 3D printing composite material selection issues.

Service recommendation in JointCloud environments: An efficient regret theory-based Qos-aware approach

With the proliferation of data-intensive applications, there arises an urgent demand for a substantial amount of cloud services to meet their requirements for data analysis. This globalized yet cooperative business landscape necessitates new cooperative models across the world. JointCloud, as a novel cross-cloud cooperation computing model, takes the first step towards establishing an evolving cloud ecosystem where all cloud service providers could collaboratively serve globalized computation needs. The collaboration among various cloud service providers enhances both the availability and Quality of Services(QoS) of cloud services, enabling a cloud service provider to concurrently serve users with differentiated QoS requirements. This unique characteristic further complicates the problems of QoS-aware service recommendations, rendering conventional approaches obsolete and inefficient. Thus, there is an urgent need to improve the efficiency and effectiveness of the service recommendation method, which is of vital importance for the JointCloud environment. In this paper, we present a two-stage efficient regret theory-based service recommendation method for the JointCloud environment. In the first stage of our proposed method, we cluster the cloud service providers to reduce the choice space to improve the efficiency of cloud service recommendations. In the second stage, we meticulously identify the most appropriate services within one cluster. To enhance the overall rationality of service recommendation, we introduce a subjective and objective combined weighting method and a regret theory based ranking method. Extensive experimental results demonstrate that our approach can facilitate fast and accurate service recommendations.

Assessment of Iranian airlines using network cross-efficiency DEA and the regret theory

Network Data Envelopment Analysis (NDEA) has been extensively applied to evaluate the air transportation sector. NDEA provides a tool for evaluating the internal processes of Decision-Making Units (DMUs). Optimistic Network Cross-Efficiency (ONCE) has recently been extended to the basic two-stage system. However, there are still two main shortcomings that need to be addressed. First, the ONCE evaluates DMUs based only on the optimistic viewpoint, neglecting the pessimistic viewpoint. The optimistic viewpoint assumes that there is only one set of reference points, which includes the best practice DMUs. The first contribution of this study is to develop a new Pessimistic Network Cross-Efficiency (PNCE) method. This method is based on a new set of reference points, which includes the worst-performing DMUs. The PNCE is developed as an extension of the ONCE. Second, both the ONCE and newly developed PNCE methods may lead to unrealistic results because they neglect the subjective preferences of Decision Makers (DMs). These NDEA models employ the Arithmetic Mean (AM) as the cross-evaluation aggregation method, which not only underestimates the importance of self-evaluation but also overestimates the importance of peer evaluations. Consequently, ONCE and PNCE may lead to biased efficiency results. To address this drawback, the second contribution of this study is to develop a new Aggregation method based on the Regret theory and Consensus (ARC). This method aims to reflect the psychological preferences of DMs when estimating cross-evaluation weights. To achieve this goal, we obtained new optimistic and pessimistic efficiencies by utilizing the newly developed ONCE-ARC and PNCE-ARC methods. Subsequently, a Double-Frontier Network Cross-Efficiency with ARC (DFNCE-ARC) is developed as a more comprehensive NDEA. Finally, a practical application is conducted to assess the performance of a set of Iranian airlines, demonstrating the usefulness and applicability of DFNCE-ARC.

Sequential three-way group decision-making for double hierarchy hesitant fuzzy linguistic term set

Group decision-making (GDM) characterized by complexity and uncertainty is an essential part of various life scenarios. Most existing researches lack tools to fuse information quickly and interpret decision results for partially formed decisions. This limitation is particularly noticeable when there is a need to improve the efficiency of GDM. To address this issue, a novel multi-level sequential three-way decision for group decision-making (S3W-GDM) method is constructed from the perspective of granular computing. This method simultaneously considers the vagueness, hesitation, and variation of GDM problems under double hierarchy hesitant fuzzy linguistic term sets (DHHFLTS) environment. First, for fusing information efficiently, a novel multi-level expert information fusion method is proposed, and the concepts of expert decision table and the extraction/aggregation of decision-leveled information based on the multi-level granularity are defined. Second, the neighborhood theory, outranking relation and regret theory (RT) are utilized to redesign the calculations of conditional probability and relative loss function. Then, the granular structure of DHHFLTS based on the sequential three-way decision (S3WD) is defined to improve the decision-making efficiency, and the decision-making strategy and interpretation of each decision-level are proposed. Furthermore, the algorithm of S3W-GDM is given. Finally, an illustrative example of diagnosis is presented, and the comparative and sensitivity analysis with other methods are performed to verify the efficiency and rationality of the proposed method.

An Online Review Data-Driven Fuzzy Large-Scale Group Decision-Making Method Based on Dual Fine-Tuning

Large-scale group decision-making (LSGDM) involves aggregating the opinions of participating decision-makers into collective opinions and selecting optimal solutions, addressing challenges such as a large number of participants, significant scale, and a low consensus. In real-world scenarios of LSGDM, various challenges are often encountered due to factors such as fuzzy uncertainties in decision information, the large size of decision groups, and the diverse backgrounds of participants. This paper introduces a dual fine-tuning-based LSGDM method using an online review. Initially, the sentiment analysis is conducted on online review data, and the identified sentiment words are graded and quantified into a fuzzy data set to understand the emotional tendencies of the text. Then, the Louvain algorithm is used to cluster the decision-makers. Meanwhile, a method combining Euclidean distances with Wasserstein distances is introduced to accurately measure data similarities and improve clustering performances. During the consensus-reaching process (CRP), a two-stage approach is employed to adjust the scores: to begin with, by refining the scores of the decision representatives via minor-scale group adjustments to generate a score matrix. Then, by identifying the scores corresponding to the minimum consensus level in the matrix for adjustment. Subsequently, the final adjusted score matrix is integrated with the prospect–regret theory to derive the comprehensive brand scores and rankings. Ultimately, the practicality and efficiency of the proposed model are demonstrated using a case study focused on the purchase of solar lamps. In summary, not only does the model effectively extract the online review data and enhance decision efficiency via clustering, but the dual fine-tuning mechanism in the model to improve consensus attainment also reduces the number of adjustment rounds and avoids multiple cycles without achieving the consensus.

A Southeast Asia National Aviation Company benchmarking by regret theory-based data envelopment analysis model

National airlines play a pivotal role in fostering economic growth, facilitating trade, and promoting tourism. With Asia's aviation economic expansion and diverse cultural landscapes, the aviation industry has a role in linking nations, facilitating cultural exchange, and enhancing cross-border cooperation. In the aftermath of the global COVID-19 pandemic, the aviation industry in Asia has faced unprecedented challenges. This study aims to provide an overview of the performance of nine national airlines in the Asian aviation market, using cross-efficiency evaluation combined with the Regret theory based on the DEA model. Traditional DEA models often utilize a self-evaluation mode to evaluate DMUs, resulting in drawbacks such as overestimating efficiency and incomplete ranking. Cross-efficiency evaluation has addressed these limitations. However, cross-efficiency evaluation overlooks the significance of the irrational psychology of decision-makers (DMs). Hence, this study utilizes the Regret theory to depict the regret aversion behavior of DMs. The integration of emotional factors in assessing operational performance offers a holistic view of airlines' challenges and opportunities after the COVID-19 pandemic, ultimately facilitating informed decision-making and strategic planning for the industry's revival.

A regret theory-based even swaps method with complex linguistic information and its application in early-stage lung cancer treatment selection

Lung cancer is the cancer with the highest morbidity and mortality rate, and selecting treatment options for early-stage lung cancer patients is of great significance in improving cure rate. To help individuals make rational decisions, the even swap method, a multiple criteria decision-making (MCDM) model, provides an effective mechanism to make tradeoffs between criteria; however, the original even swap method ignored psychological characteristics of individuals and could not deal with complex linguistic information. To deal with the early-stage lung cancer treatment selection problem, this paper introduces generalized probabilistic linguistic term sets (GPLTSs) to describe complex linguistic information and proposes a generalized probabilistic linguistic even swap method based on the regret theory. Firstly, the upper and lower bounds corresponding to linguistic expressions are extracted to represent the uncertainty of GPLTSs. Then, a regret-rejoice function is introduced to express psychological characteristics of individuals when they making tradeoffs between criteria. Afterwards, a regret theory-based even swap method is proposed to rank alternatives. The proposed method is validated in the selection of early-stage lung cancer treatment options. Sensitivity analysis and comparative analysis are given to show the feasibility of the proposed method.

A generalized interval-valued p,q Rung orthopair fuzzy Maclaurin symmetric mean and modified regret theory based sustainable supplier selection method

A novel interval valued p,q Rung orthopair fuzzy (IVPQ-ROF) multiple attribute group decision making (MAGDM) method for sustainable supplier selection (SSS) is proposed in this paper. This study mainly contains two research points: (1) tackling the interrelation between attributes; and (2) describing the psychological state and risk attitude of decision makers (DMs). For the first research point, we introduce the Archimedean operation rules for interval valued p,q Rung orthopair fuzzy sets (IVPQ-ROFSs), then the generalized interval valued p, q Rung orthopair fuzzy Maclaurin symmetric mean (GIVPQ-ROFMSM) operator and the generalized interval valued p, q Rung orthopair fuzzy weighted Maclaurin symmetric mean (GIVPQ-ROFWMSM) operator are defined to reflect the correlation between attributes. For the second research point, we introduce the positive ideal degree (PID) and negative ideal degree (NID) based on projection of IVPQ-ROFSs, and modified regret theory. Both of them consider the best alternative and worst alternative, so as to reflect the psychological state and risk attitude of DMs. Finally, a SSS problem is presented to manifest the effectiveness of the designed method. We also provide sensitivity analysis and comparative analysis to further demonstrate the rationality and validity of the proposed method.

Cloud model-based multi-stage multi-attribute decision-making method under probabilistic interval-valued hesitant fuzzy environment

Due to the complexity of multi-stage multi-attribute decision-making (MADM) problems, decision-makers (DMs) frequently provide incomplete and hesitant evaluation information. A crucial difficulty in multi-stage MADM problems is how to make a valid decision under uncertain environment. Firstly, we employ probabilistic interval-valued hesitant fuzzy set (PIVHFS) to characterize the hesitancy of DMs, and then build an optimization model to obtain the probability information based on the score function, deviation function of probabilistic interval-valued hesitant fuzzy element (PIVHFE) and information entropy. Secondly, cloud model is introduced to optimize the calculation process and an optimization model is developed to realize the transformation from PIVHFS to cloud model. Thirdly, a method to solve the stage weights is proposed by synthesizing the within-stage and between-stage decision information. Similarly, the attribute weights are obtained by constructing a mathematical model based on the within-attribute and between-attribute decision information. The noted method can utilize decision information to its fullest. Then, the regret theory is applied to a multi-stage MADM problem. Finally, the applicability and effectiveness of the method are illustrated through case analysis and methods comparison.

A novel Hellinger distance-based regret theory method for spherical fuzzy decision making model and its application in logistics

Actual decision making problems are often based on the company decision maker’s behavior factors, such as risk attitude, subjective preference, etc. Regret theory can well express the behavior of the decision maker. In this pursuit, a novel decision making method was developed, based on the regret theory for the multi-attribute decision making problem, in which attribute values were expressed by spherical fuzzy numbers. Distance measurement not only has extensive applications in fields such as pattern recognition and image processing, but also plays an important role in the research of fuzzy decision theory. The existing distance measures of spherical fuzzy set either have special cases of anti-intuition or are more complex in calculation, so finding suitable distance measures is also an important research topic in the decision-making theory of spherical fuzzy set. For this reason, we first establish a new distance of spherical fuzzy sets based on Hellinger distance of probability distribution. A decision maker’s perception utility value function is proposed using the new distance formula, which is used to measure the regretful and rejoice value. Then we establish an optimization model for solving the attribute weights, when the information of attribute weight was partially known. Subsequently, the comprehensive perceived utility values were utilized to rank the order of the alternatives. Finally, a numerical example of assessment of logistics providers is used to show that the new decision making method is effective and feasible.

Considering personalized individual semantics with ordinal and cardinal consensus reaching processes via three-way decision and regret theory

The seamless integration of computerized methodologies into industrial engineering problem-solving is pivotal for optimizing efficiency. In the specific domain of multi-attribute group decision-making (MAGDM) with probabilistic linguistic term sets (PLTSs), these methodologies offer systematic approaches to consensus building, ensuring effective decision processes in intricate scenarios. Within the realm of PLTSs, the consensus-reaching process (CRP) for MAGDM is gaining prominence. This paper addresses this evolving area by proposing ordinal and cardinal CRPs within the framework of PLTSs, specifically incorporating the regret theory (RT) of three-way decisions (TWD). The paper introduces an initial distance formula under PLTSs, providing a complementary approach to assess similarity relations among decision-makers (DMs). To account for diverse semantics across DMs, personalized individual semantics (PIS) is integrated into the CRP, recognizing variations in DMs’ alternatives and attributes. To enhance realism, the paper introduces the concepts of individual alternative sets and individual attribute sets. Additionally, the paper integrates ordinal and cardinal consensus, establishing a dynamic feedback adjustment mechanism grounded in the principles of RT and TWD. The method’s reasonableness is validated through a real case study, and a comparative analysis with the existing methods underscores the superiority of the approach presented in this paper.

Successful Surfing in Capital Market: Phenomenological Study of Indonesian “Warren Buffett”

Objective: The purpose of this study is to uncover the successful secrets of a No. 1 individual investor on the Indonesian stock exchange who has proven to earn huge amounts of profits. Theoretical Framework: Some theories related to investor behavior theory, among others, are: Regret Theory, this theory is a development of previous decision-making theories, namely prospect theory and expected usability theory. Method: This research explores the philosophy, behavior, strategies, techniques, and ways to find stocks that can provide great returns based on the experience of an individual investor. This research also examines in depth the experience of a famous investor named Lo Kheng Hong (LKH), known as Warren Buffett of Indonesia. Results and Discussion: The results showed that the factors that support the success of LKH in investing in the IDX can be: (1) reading the company's financial statements; (2) analyzing financial data; considering the integrity of its owners and management; (3) investing by buying shares on the IDX; (4) daring to make decisions to buy shares based on their own analysis; (5) patiently waiting; (6) simple to live their lives and frugal. Research Implications: One of the secrets of success, the strategy in finding stocks that provide big profits is to read and look at financial statements, analyze deeply using simple fundamental analysis, look for stocks that are undervalued. Originality/Value: This study contributes to literatures regarding how to be successful investor in stock market.

Regret-based three-way decisions with set pair analysis in incomplete information systems

Set pair analysis is a valuable tool for managing uncertain systems and can effectively handle missing information in the decision-making process. In addition, regret theory can objectively describe the impact of decision-maker psychology on decision-making. However, traditional decision-making methods rarely combine set pair analysis and regret theory to discuss decision problems. Thus, this article aims to integrate regret theory into the three-way decision theory based on set pair analysis, presenting a novel approach to address multi-attribute decision-making problems in real life. Specifically, we first propose a data-driven approach based on set pair analysis to determine attribute weights. Then, a similarity relationship is established based on the weighted distance to calculate conditional probability. Meanwhile, the average value of regret and joy under each attribute is selected as a reference point to determine the relative utility function using regret theory. Subsequently, we develop a new three-way decision method in incomplete information systems and apply it to solve practical problems. Finally, the effectiveness and superiority of our method are demonstrated by case analysis and comparison. We also conduct sensitivity analysis to examine the stability of the new method.

Who follows through? Different factors predict initial commitment vs. Following through in a national survey of organ donor registration.

Little research has investigated factors that determine whether people falter in the face of an obstacle or successfully follow through on an initial commitment to act. We integrated multiple theories (the Reasoned Action Approach [RAA], Prototype Willingness Model, and anticipated regret theory) to test which factors predict initial commitment to register as an organ donor and to discover whether different factors predict initial commitment vs. following through with registration. Participants from a nationally representative UK sample (N = 1,008) reported their beliefs about organ donation and indicated their decision to register. An obstacle that participants could not foresee was that they had to complete registration in a second survey 3 days after making their initial commitment. Findings showed that 14.8% of participants followed through, 19.7% demonstrated initial commitment, and 65.5% declined to register. Linear discriminant function analysis derived two functions that distinguished these registration patterns. The first function discriminated participants who declined to register from the other groups. The declined group had lower scores on RAA variables compared to their counterparts. The second function distinguished participants who made an initial commitment to register from those who followed through. Follow-through was associated with less anticipated negative affect, more favorable descriptive norms, and stronger identification with organ donors. The present findings indicate that even modest friction leads to a large reduction in follow-through. Moreover, different factors influence initial commitment vs. following through. Whereas RAA variables predicted initial commitment, following through was a function of anticipated negative affect and social processes.

A Two-Stage Sustainable Supplier Selection Model Considering Disruption Risk

The global spread of the pandemic has changed many aspects of life and placed the supply chain at risk of disruption. To solve the problem of supplier selection under the risk of supply chain disruption, in this paper, we propose a two-stage evaluation model to address the issue of supplier selection in the context where a pandemic requires a lockdown. First, we incorporate the lead time into the epidemic model that predicts the evolution of the pandemic to identify suppliers that have a high risk of disruption caused by the pandemic’s evolution. Second, we propose a best–worst method combined with regret theory to rank candidate suppliers. Our model provides a dynamic link between the pandemic’s evolution and supplier selection, and it allows selecting suppliers according to various criteria while avoiding supply chain disruptions due to inappropriate supplier selection. We validate the proposed model on a real case study with epidemic data from China. This paper is the first to consider the impact of lockdowns during the pandemic on supplier selection. We develop a novel MCDM model BWM-RT for supplier selection; our model can be an effective decision support approach to help decision makers better cope with the risk of supply chain disruptions.

Evaluating edge artificial intelligence-driven supply chain management platforms using collaborative large-scale fuzzy information fusion

The rapid development of edge artificial intelligence (AI) has brought about revolutionary changes in supply chain management (SCM). It not only provides real-time data processing capabilities but also accelerates the decision-making process, injecting more innovative elements into SCM. In this context, SCM platforms require new technological developments and effective evaluations, as they collectively drive the efficient coordination of complex business processes. SCM platforms enable the seamless coordination of various supply chain elements, facilitating streamlined operations and enhanced decision-making processes. The evaluation of these platforms not only serves to validate their performance and effectiveness, but also contributes to continual improvements in design and application, and addressing the evolving demands within the realms of global commerce and dynamic market conditions. The ongoing assessment and enhancement of SCM platforms play a crucial role in optimizing resource allocation, improving production efficiency, and fostering adaptability to changing market dynamics. However, the acquisition of assessment data often introduces imprecise data. Additionally, the large-scale nature and bounded rationality of decision-makers (DMs) significantly impact the evaluation of SCM platforms. This article aims to explore a collaborative large-scale information fusion approach and provide a robust fuzzy framework for evaluating SCM platforms. This approach employs a combination of spherical fuzzy sets (SFSs), large-scale group decision-making (LSGDM), behavioral theories and three-way decisions (TWD) to thoroughly explore collaborative large-scale information fusion and its practical application in assessing SCM platforms. It introduces an innovative spherical fuzzy (SF) LSGDM technique and integrates TWD with the inclusion of prospect theory (PT) and regret theory (RT) to mitigate potential decision risks. The developed collaborative large-scale information fusion approach is assessed for validity, effectiveness and practicality in the context of evaluating SCM platforms using online data. Experimental results demonstrate that this approach provides reasonable evaluation outcomes, considering uncertain information processing capabilities, large-scale characteristics, bounded rationality and decision risks.

Understanding the route choice behavior of metro passenger using the smartphone applications

Understanding the distribution of passenger flow is one of the crucial prerequisites for improving operational efficiency and implementing travel demand management strategies. This paper proposes an improved Random Regret Minimization (i-RRM) model based on the regret theory, where the passenger’s regret on selecting routes is minimized. Considering the growing trend of passengers relying on smartphone apps for route planning and navigation, three factors are investigated, namely, travel time, the number of transfer stations, and the total number of stations traveled. Automatic Fare Collection (AFC) is used to get the proportion of each route, and mobile phone signaling data is used to obtain the ground truth of passengers’ routes proportions for parameter calibration and model validation. The results show that, compared to the ground truth data, the average errors of the proposed i-RRM model, the classic RRM model, and the direct path matching algorithm are 6.03%, 8.96%, and 10.45%, respectively.

Development and application of a power Bonferroni mean operator based on the foreign fiber content grade evaluation method

Cubic q-rung orthogonal fuzzy sets (C q-ROFSs) are a sophisticated mathematical tool used to handle complex evaluation information in multi-attribute decision-making problems. In specific decision-making problems, the power Bonferroni mean (PBM) operator can reflect the correlation between different attributes and mitigate the impact of extreme evaluation information, thereby providing more practical value. This paper focuses on expanding the PBM operator into the C q-ROFS environment and deriving new PBM operators: the cubic q-rung orthogonal power Bonferroni averaging operator and weight cubic q-rung orthogonal PBM operator. The proposed operator shows strong flexibility and stability in the cubic q-rung orthogonal fuzzy environment. In the absence of weight information, there is a dearth of literature addressing the acceptable advantage and decision stability in the C q-ROFS environment; considering the regret behavior of decision information, a VIKOR method based on regret theory is proposed. The proposed method aggregates information using the proposed operator, determines the scheme and weights at two levels of attributes, and constructs a relative proximity decision matrix. Then, the VIKOR method calculates the group utility value and individual regret value based on the regret perception value to rank the alternatives. Finally, the method is applied to evaluate the cotton foreign fiber content, and its stability and effectiveness are verified through sensitivity analysis and comparison with existing methods.