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Background: Decision 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. Methods: A 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. Results: Of 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. Conclusions: Our 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. [ABSTRACT FROM AUTHOR]

Failure Mode and Effect Analysis (FMEA) is acknowledged as a beneficial instrument for identifying and mitigating system failures. However, the traditional FMEA method has its limitations. For instance, crisp numbers fail to adequately represent the intricate information and cognitive nuances of experts. Additionally, the conventional approach overlooks the significance of weights assigned to FMEA experts and risk factors (RFs). Furthermore, the simplistic ranking of failure modes in traditional FMEA does not accurately reflect priorities. In light of these drawbacks, this paper introduces an innovative, fully data‐driven FMEA method, leveraging a probabilistic uncertain linguistic term sets (PULTSs) environment and the Weighted Aggregates Sum Product Assessment (WASPAS) method. In the assessment process, PULTSs serve as linguistic tools that express probability distribution, allowing for a more reasonable and precise description of information. To address the issue of weights for RFs, the regret theory and Modified CRITIC method are employed. Subsequently, the WASPAS method is applied to determine the risk rankings of failure modes. To illustrate the feasibility and rationality of this novel FMEA model, the paper includes an example involving the production of Lithium‐ion batteries. To emphasize the excellence of the proposed FMEA model, sensitivity and comparative analyses are carried out. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xihua University (Natural Science Edition) is the property of Xihua University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A social planner wants to rank people. Utilitarianism implies that a natural ranking which orders people from most to least sensitive to their rank is the best ranking. But people's feelings toward the social planner, like blame or gratitude, can depend both on their assigned rank, and on the alternatives they might have been assigned. As a result, the social planner needs to do pairwise comparisons and there may not be a best ranking due to intransitive cycles. This paper shows the possibility of the coexistence of a best choice and non-transitivity. It illustrates how to tell when a best ranking exists, and that when it exists, it is indeed the natural ranking. When this best does not exist, an alternative second-best group ranking strategy is proposed. [ABSTRACT FROM AUTHOR]

Background: One of several logistics contexts in which pricing decisions are made involves truckload carriers using reverse auctions to bid for prices they want for their transportation services while operating under uncertainty about factors such as their (i) operations costs and (ii) rivals' bids. This study's main purpose is to explore humans' use of fast and frugal heuristics (FFHs) to navigate those uncertainties. In particular, the study clarifies the logic, theoretical underpinnings, and performance of human FFHs. Methods: The study uses behavior experiments as its core research method. Results: The study's key findings are that humans use rational FFHs, yet, despite the rationality, human decisions yield average profits that are 35% below profits from price optimization models. The study also found that human FFHs yield very unstable outcomes: the FFH coefficient of variation in profit is twice as large as price optimization. Novel contributions inherent in these findings include (a) clarifying connections between spot market auction pricing and behavioral theories and (b) adding truckload spot markets to the literature's contexts for measuring performance gaps between human FFHs and optimization models. Conclusions: The contributions have implications for practical purposes that include gauging spot pricing decisions made under constraints such as limited access to price optimization tools. [ABSTRACT FROM AUTHOR]

Abstract 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.

Abstract 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.

Selecting emergency suppliers from a wide range of candidates based on their performance under each criterion can be regarded as a multi-criterion decision making (MCDM) problem. Existing MCDM models to solve the emergency supplier selection problem ignored situations where large-scale suppliers exist, the influence of criteria weights on the robustness of ranking results, and the influence of psychology of regret aversion and disappointment aversion on decision results. To make up for these deficiencies, this paper proposes an MCDM model to solve emergency supplier selection problem with large-scale alternatives. Firstly, to avoid the influence of criteria weights on ranking of alternatives, the Robustness, Correlation, and Standard Deviation (ROCOSD) method is introduced to determine objective weights of criteria based on three objectives. Secondly, the τ-balanced clustering method is applied to cluster large-scale alternatives into balanced clusters. Next, considering the psychology of regret aversion and disappointment aversion of decision makers, a two-stage method is proposed to rank alternatives, which identifies the optimal alternative within each cluster and forms a new cluster consisting of these optimal alternatives in the first stage, and selects the optimal alternative from the new-formed cluster in the second stage. A numerical case is given to validate the proposed model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study investigates the distinctive modeling of regret utility when compared with common utility. I also introduce the interplay between common utility and regret utility. Using this model, I examine the differences in decision making, which encompasses issues such as risk sharing and principal–agent dilemmas. Regret utility is set so that its risk aversion shows common utility's prudence (i.e., downside risk aversion). This paper reveals, both qualitatively and quantitively and with a concrete model, that regret utility leads to a more balanced and optimal ratio of agent payouts to outputs compared with common utility, meaning when major outputs are kept by principal, there are relatively larger agent payouts, and when major outputs are kept by the agent, there are relatively smaller agent payouts. This means that regret makes a more balanced distribution, and regret utility is more conservative (not biased). In addition, preliminary empirical research was performed in which people were asked risk preference or averseness questions, and their risk averseness was calculated by using the CRRA (Constant Relative Risk Aversion) utility function. The regret condition leads to a more conservative attitude. Furthermore, the regret model can be used in other areas, like in conservative investment portfolio optimization. [ABSTRACT FROM AUTHOR]

This paper examines the production and hedging decisions of the competitive firm under price uncertainty when the firm is not only risk averse but also regret averse. Regret-averse preferences are characterized by a modified utility function that includes disutility from having chosen ex post suboptimal alternatives. The extent of regret depends on the difference between the actual profit and the maximum profit attained by making the optimal production, and hedging decisions had the firm observed the true realization of the random output price. While the separation theorem holds under regret aversion, the prevalence of hedging opportunities may have perverse effect on the firm's optimal output level, particularly when the firm is sufficiently regret averse. The full-hedging theorem, however, does not hold. We derive sufficient conditions under which the regret-averse firm's optimal futures position is an under-hedge (over-hedge). We further show that the firm optimally increases (decreases) its futures position when the price risk possesses more positive (negative) skewness. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated framing approach to investigate the disposition effect by linking it to regret and prospect theory. We build upon the wide framing hypothesis and extend prior research by including metrics of perceived risk and the number of days stocks are at a gain. Using a sample of 24 million trading operations conducted by nearly 4,000 Italian investors from 2010 to 2018, we demonstrate that investment decisions are influenced not only by portfolio aggregate performance but also by asset trading history and perceived volatility. Our results align with several theoretical frameworks that could explain the disposition effect, such as mental accounting, prospect theory and regret aversion. Furthermore, by incorporating both narrow and wide framing factors, along with their interactions, our approach provides a more comprehensive understanding of the disposition effect. • We link the disposition effect to perceived risk and portfolio performance. • We leverage a new dataset of 24 million trading operations. • Disposition effect diminishes as the proportion of gains in the portfolio rises. • The perceived risk of an asset is positively related to the willingness to sell it. • Past performance reduces the asymmetry in the willingness to sell at a loss or gain. [ABSTRACT FROM AUTHOR]

The impact of social influence on the dynamic of awareness diffusion and epidemic spreading has been researched adequately, while the feedback effect of individuals on social influence does not receive enough attention. In this study, we sought to propose a model composed of a two-layer network with complex awareness. Initially, we design expert nodes based on regret theory to characterize social influence and symmetrical interaction, including the impact of experts and the feedback of individuals. Then, we describe the social structure in the form of the simple contagion model and consider the superposition effect. According to the microscopic Markov chain approach (MMCA) derivation, a clear association emerges between epidemic threshold and complex awareness diffusion. Extensive numerical simulations reveal two implications: (1) Widening the channels of information dissemination could contribute to epidemic prevention; (2) Intensifying the recurrent diffusion of complex social awareness is beneficial to epidemic control. [ABSTRACT FROM AUTHOR]

Copyright of Electric Drive is the property of Electric Drive Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problem that the assessment method of urban water resources vulnerability too subjective and not taking into account the finite rationality of natural person and the psychological behavior characteristics of regret avoidance, which resulting in the distortion of the evaluation results, this paper proposed an urban water resources vulnerability assessment method based on OWA operator empowerment and regret theory. The DPSIR model was used to construct 29 evaluation indexes from the following five aspects including driving force, pressure, state, influence and response. OWA operator was used to reorder the decision data, which fully considered the location and size of data, so as to weaken the negative effect of extreme decision data on weight. The difference between the utility value of decision data and the ideal utility value in the regret theory was selected as the basis to evaluate the subjectivity of experts, so as to realize the objectification of the subjective data of decision making and improve the objectivity of the evaluation results. Finally, this paper evaluated the water resources vulnerability of Zhengzhou from 2017 to 2022, and compared the evaluation results with those of AHP and regret theory, OWA operator and fuzzy mathematics. The results show that the evaluation results of urban water resources vulnerability assessment method based on OWA operator empowerment and regret theory are basically consistent with the evaluation results of other methods, all of which are considered as severely vulnerable water resources in Zhengzhou City. However, the evaluation results of this method have less relative error and are more close to the actual situation, which can be better used for the assessment of urban water resources vulnerability. [ABSTRACT FROM AUTHOR]

As a decision information preference which includes membership degree (MD), non-membership degree (NMD), and probability, the probabilistic dual hesitant fuzzy set (PDHFS) is a crucial tool for effectively expressing uncertain information. In the domains of multi-attribute decision making (MADM) and multi-attribute group decision making (MAGDM), distance measures are extremely helpful tools. In this study, a novel PDHFS distance measure is put out, on which a MAGDM method that takes decision-makers' (DMs') psychological behavior into account is proposed. First, a novel distance measure is put forward to effectively assess the difference between different PDHFSs by adding consideration of the distances between MDs and between NMDs. Second, a similarity-trust analysis method based on the new distance measure is employed to calculate expert weights for integrating group decisions, and the group satisfaction index and regret theory are extended to a probabilistic dual hesitant fuzzy information environment. A MAGDM method based on the novel distance measure and regret theory is proposed. Finally, the proposed method is applied to the selection of radiation protection strategies in nuclear power plants, and it is also determined through parametric analysis that DMs' tendency to avoid regret has an impact on the outcomes of decisions. When the method proposed in this study is compared to existing approaches, the findings demonstrate that the method's performance in resolving MAGDM issues in a PDHFS environment is superior. [ABSTRACT FROM AUTHOR]

Three-way decision (3WD) can be used to handle complexity and uncertainty in decision-making problems, and is compatible with human cognitive systems. For insufficient existing experiences, decision-makers (DMs) can select interval fuzzy information, as the information about medicine company is more hesitation, imprecision, and ambiguity. In the determination process, DMs may not take suitable decisions by choosing membership and non-membership degree of imprecise information. To improve fault-tolerance and validate the plausibility of DMs' evaluation, the probabilistic interval-valued q-rung orthopair hesitant fuzzy set (PIVq-ROHFS) is introduced. Additionally, distinct psychological behaviours of DMs have an impact on the outcomes of decision-making. For this situation, we first develop a regret theory based 3WD model in PIVq-ROHFS to evaluate the utility value of the objects. The core focus of regret theory is to develop a new regret-rejoice function based on projection theory. Another core focus of this inquisition is to propose a novel multi-criteria decision making (MCDM) method for evaluating conditional probability in 3WD model. The criteria's weight in MCDM method is evaluated by a newly proposed multi-objective optimization (MOO) problem. To solve the MOO problem, we utilize a hybrid technique by combining particle swarm optimization and multi-choice goal programming with utility function. [ABSTRACT FROM AUTHOR]

In any conflict analysis model, agents are decision-makers (DMs). In reality, the regret psychology of DMs often influence decision outcomes due to uncertain risks. However, the existing conflict analysis model avoids DMs' regret psychology. The q-rung orthopair fuzzy information (q-ROFIS) with regret theory (RT)-based conflict analysis in detail is a primary aim of this study to solve more complex and uncertain-conflict problems than possible within the confinement of current knowledge. First, we define RT-based q-rung orthopair fuzzy conflict distance (q-ROFCD) in a pair and show that our q-ROFCD holds symmetry and triangular inequality. Using q-ROFCD, we have studied pre-defined parameters and three-way decision (3WD) theory-based conflict analysis. In pre-defined parameters-based conflict analysis, we trisect the agents according to single and multiple issues, define q-rung orthopair fuzzy conflict of each issue and issue set, trisect issues, provide a feasible strategy for a conflict situation, define the conflict degree of each agent and agent set, and finally find out the intrinsic reasons for the conflict are. In 3WD theory based-conflict analysis, we trisect the agents according to single and multiple issues using Bayesian minimum cost theory, define conflict region, rank the issues, introduce three-way coalitions of agents and finally define maximal coalition of agents. At last, the stability and validity of the proposed model are verified via an application, sensitive analysis and comparative analysis with similar studies. [ABSTRACT FROM AUTHOR]

Since thermodynamics can describe the energy of matter and its form of storage or transformation in the system, it is introduced to resolve the uncertain decision-making problems. The paper proposes the thermodynamic decision-making method which considers both the quantity and quality of the probabilistic linguistic decision information. The analogies for thermodynamical indicators: energy, exergy and entropy are developed under the probabilistic linguistic circumstance. The probabilistic linguistic thermodynamic method combines the regret theory which captures decision makers' regret-aversion and the objective weight of criterion obtained by the water-filling algorithm. The proposed method is applied to select the optimal solution to respond to the floods in Chongqing, China. The self-comparison is conducted to verify the effectiveness of the objective weight obtained by the water-filling algorithm and regret theory in the probabilistic linguistic thermodynamic method. The reliability and feasibility of the proposed method are verified by comparative analysis with other decision-making methods by some simulation experiments and non-parametric tests. [ABSTRACT FROM AUTHOR]

The traditional order allocation model primarily adopts a multi-objective programming approach. This approach is optimized for profit maximization and cost minimization to obtain the order allocation scheme. However, supplier efficiency is typically an important overlooked index in the order allocation process. In addition, only one decision-maker (DM) usually participates in the decision-making process. Furthermore, the bounded rationality of DMs under the risk state is not considered. Therefore, we established a group game cross-efficiency order allocation model (GGCE-OA) based on regret theory and decision consensus. First, regret theory was used to describe DMs’ subjective preferences and capture their bounded rational behaviours. Second, based on the contribution degree and the corresponding iterative algorithm, the weight modification model is established under the group game cross-efficiency evaluation framework to improve the decision consensus among DMs. Third, to allocate orders from the perspective of improving supplier efficiency, we established the GGCE-OA model based on regret theory and decision consensus, as well as establishing the corresponding iterative algorithm. This enabled us to obtain a fair, unique and accepted order allocation scheme by all DMs. Finally, the model established in this paper was applied for order allocations to green suppliers to verify its effectiveness and practicability. [ABSTRACT FROM AUTHOR]

In reality, decision-making is usually influenced by vague evaluation information and irrational psychology behavior of decision-makers. To solve the multi-attribute decision-making problem with completely unknown weight vague information, an extended hybrid decision-making method is proposed under the Fermatean hesitant fuzzy environment. Firstly, the weight information is determined from two levels of alternative and attribute based on the score function and entropy value. Secondly, the dominance matrix is constructed according to the dominance of each alternative obtained by the TODIM method. Then, the regret theory is introduced to compute the perception values, which is the factor to rank the alternatives by the VIKOR method. Afterwards, an illustrative example is provided to prove the effectiveness and validity of the proposed hybrid method. By sensitivity analysis and comparative analysis, this method is shown to be more advantageous in considering the regret psychological characteristics of the DM. [ABSTRACT FROM AUTHOR]

Inspired by Li (2019) who considers one parameter, we propose a novel two-parameter coherent fuzzy number (TPCFN) that can flexibly capture investors' attitudes (pessimistic, optimistic, or neutral). We define the possibilistic density function, the possibilistic distribution function, the possibilistic mean, and the possibilistic variance of the TPCFN for the first time. Furthermore, we derive the above statistical characteristics with numerical expressions through rigorous mathematical proof. The monotonicity of the possibilistic mean and possibilistic variance are presented by the first-order derivative and illustrated with figures in detail. In addition, we discuss the investors' attitudes by using different parameter values and their influences on the mean and variance. Then, we construct an equal-weighted model, a mean–variance model, and a regret minimization model with TPCFN, respectively. We carry out a sensitivity analysis to explore the parametric influence on the model's solution. At the same time, we compare different models with the same parameter values. Finally, we use a numerical example to demonstrate the feasibility and effectiveness of our proposed models. We compare the performance of the three models by five indexes (annual return, Sharpe ratio, beta value, unsystematic risk, and alpha value). The results show that optimistic investors can obtain more gains in the three models. Our minimization model considering the regret factor outperforms the mean–variance model and the equal-weighted portfolio in returns when the parameter values are the same. [ABSTRACT FROM AUTHOR]

Due to an increase in decision making sophistication and risk, behavioural three-way decision making has replaced traditional two-way decision making. Accordingly, this study proposes a novus three-way multi-attribute decision making model by creating three-way decision rules in context of multi-attribute decision-making with two key behavioural theories, namely, regret and prospect theories. To capture uncertainty in the evaluation information, q-rung orthopair fuzzy set is implemented in the information system. After that, a new method is forwarded based on correlation coefficient and variance to calculate the weights of attributes. In light of the conventional Mahalanobis distance, q-rung orthopair fuzzy Mahalanobis distance is developed. Then, regret-based dominated relation and rejoice-based dominating relation are defined. Thereafter, a new procedure is developed to estimate the conditional probability according to two above-mentioned relations. Three kinds of scoring functions are developed with the help of prospect theory, based on which, despondent, expectant, and balanced tactics are developed to illustrate three mindsets of decision maker. Ultimately, three sizeable datasets gathered from the KEEL and UCI databases are used to demonstrate the legitimacy, resiliency, and supremacy of the constructed model. [ABSTRACT FROM AUTHOR]

Background: One of several logistics contexts in which pricing decisions are made involves truckload carriers using reverse auctions to bid for prices they want for their transportation services while operating under uncertainty about factors such as their (i) operations costs and (ii) rivals’ bids. This study’s main purpose is to explore humans’ use of fast and frugal heuristics (FFHs) to navigate those uncertainties. In particular, the study clarifies the logic, theoretical underpinnings, and performance of human FFHs. Methods: The study uses behavior experiments as its core research method. Results: The study’s key findings are that humans use rational FFHs, yet, despite the rationality, human decisions yield average profits that are 35% below profits from price optimization models. The study also found that human FFHs yield very unstable outcomes: the FFH coefficient of variation in profit is twice as large as price optimization. Novel contributions inherent in these findings include (a) clarifying connections between spot market auction pricing and behavioral theories and (b) adding truckload spot markets to the literature’s contexts for measuring performance gaps between human FFHs and optimization models. Conclusions: The contributions have implications for practical purposes that include gauging spot pricing decisions made under constraints such as limited access to price optimization tools.

This study investigates the distinctive modeling of regret utility when compared with common utility. I also introduce the interplay between common utility and regret utility. Using this model, I examine the differences in decision making, which encompasses issues such as risk sharing and principal–agent dilemmas. Regret utility is set so that its risk aversion shows common utility’s prudence (i.e., downside risk aversion). This paper reveals, both qualitatively and quantitively and with a concrete model, that regret utility leads to a more balanced and optimal ratio of agent payouts to outputs compared with common utility, meaning when major outputs are kept by principal, there are relatively larger agent payouts, and when major outputs are kept by the agent, there are relatively smaller agent payouts. This means that regret makes a more balanced distribution, and regret utility is more conservative (not biased). In addition, preliminary empirical research was performed in which people were asked risk preference or averseness questions, and their risk averseness was calculated by using the CRRA (Constant Relative Risk Aversion) utility function. The regret condition leads to a more conservative attitude. Furthermore, the regret model can be used in other areas, like in conservative investment portfolio optimization.

The objective of this paper is to present an extended approach to address the stochastic multi-attribute decision-making problem. The novelty of this study is to consider the regret behavior of decision makers under a Pythagorean hesitant fuzzy environment. First, the group satisfaction degree of decision-making matrices is used to consider the different preferences of decision-makers. Second, the nonlinear programming model under different statues is provided to compute the weights of attributes. Then, based on the regret theory, a regret value matrix and a rejoice value matrix are constructed. Furthermore, the feasibility and superiority of the developed approach is proven by an illustrative example of selecting an air fighter. Eventually, a comparative analysis with other methods shows the advantages of the proposed methods.

Given that most current linguistic multiattribute decision-making methods do not consider the optimal efficiency of decision-making units and the psychological behavior of decision makers, a linguistic multiattribute decision-making method based on regret theory, data envelopment analysis, and the minimax reference point method is proposed. First, based on the decision-maker psychology of regret and avoidance, the perceived utility value of each decision-making unit was calculated using the language regret–joy value function. The subjective and optimal efficiency values of each decision-making unit were obtained using the subjective weighting and data envelopment analysis methods, respectively. Next, we considered the best efficiency as the reference point and the subjective efficiency as the decision preference to establish a minimax reference point model. By solving the model, a set of public weights that minimizes the difference between the efficiency values of all decision-making units and their optimal efficiency values can be obtained to sort the decision-making units and select the best. The feasibility of the method was verified using an example of employee evaluation and selection; the effectiveness of the method was demonstrated through comparative analysis with other methods. [ABSTRACT FROM AUTHOR]

Cross-efficiency in data envelopment analysis is widely used in production as an evaluation method that includes input and output indicators and allows for self-evaluation and mutual evaluation of decision making units (DMUs). However, as the application scenarios continue to expand, the traditional methods gradually fail to meet the needs. Many researchers have proposed improved methods and made great progress in weight determination, but the existing studies still have shortcomings in considering the psychological behavior of decision makers (DMs) and there is still relatively little research on cross-efficiency in fuzzy environments. In this paper, we proposed a method to apply CRITIC to determine weights and introduce both prospect theory and regret theory into the evaluation method of cross-efficiency to obtain the prospect cross-efficiency matrix and regret cross-efficiency matrix respectively, and then applied the Pythagorean hesitant fuzzy operator to aggregate them to achieve the ranking of DMUs through the fraction function. This largely takes into account the subjective preference and regret avoidance psychology of DMs. The applicability of this paper's method is also verified through an example of shopping for a new energy vehicle. Finally, the effectiveness of this paper's method is verified by comparing three traditional methods with this paper's method, which provides an effective method for considering risk preferences in the decision-making process. [ABSTRACT FROM AUTHOR]

Hydrogen is gaining prominence as a sustainable energy source in the UK, aligning with the country's commitment to advancing sustainable development across diverse sectors. However, a rigorous examination of the interplay between the hydrogen economy and the Sustainable Development Goals (SDGs) is imperative. This study addresses this imperative by comprehensively assessing the risks associated with hydrogen production, storage, transportation, and utilization. The overarching aim is to establish a robust framework that ensures the secure deployment and operation of hydrogen-based technologies within the UK's sustainable development trajectory. Considering the unique characteristics of the UK's energy landscape, infrastructure, and policy framework, this paper presents practical and viable recommendations to facilitate the safe and effective integration of hydrogen energy into the UK's SDGs. To facilitate sophisticated decision making, it proposes using an advanced Decision-Making Trial and Evaluation Laboratory (DEMATEL) tool, incorporating regret theory and a 2-tuple spherical linguistic environment. This tool enables a nuanced decision-making process, yielding actionable insights. The analysis reveals that Incident Reporting and Learning, Robust Regulatory Framework, Safety Standards, and Codes are pivotal safety factors. At the same time, Clean Energy Access, Climate Action, and Industry, Innovation, and Infrastructure are identified as the most influential SDGs. This information provides valuable guidance for policymakers, industry stakeholders, and regulators. It empowers them to make well-informed strategic decisions and prioritize actions that bolster safety and sustainable development as the UK transitions towards a hydrogen-based energy system. Moreover, the findings underscore the varying degrees of prominence among different SDGs. Notably, SDG 13 (Climate Action) exhibits relatively lower overall distinction at 0.0066 and a Relation value of 0.0512, albeit with a substantial impact. In contrast, SDG 7 (Clean Energy Access) and SDG 9 (Industry, Innovation, and Infrastructure) demonstrate moderate prominence levels (0.0559 and 0.0498, respectively), each with its unique influence, emphasizing their critical roles in the UK's pursuit of a sustainable hydrogen-based energy future. [ABSTRACT FROM AUTHOR]

Technique for order preference by similarity to ideal solution (TOPSIS) is a famous technique for solving multicriteria decision-making problems. However, the classical TOPSIS is invalid to distinguish the alternatives when the distances to the PIS and NIS are the same, and the current researchers about TOPSIS seldom consider the psychological characteristics of loss aversion and regret aversion which affect the decision quality in real world. This paper proposes a behavioral TOPSIS with prospect theory and regret theory considering risk attitudes. First, the defect of classical TOPSIS is illustrated as motivation. Next, we introduce a behavioral TOPSIS with prospect theory and show how it overcomes the defect of classical TOPSIS. Then, regret theory is applied to extend behavioral TOPSIS to show the regret attitude. Finally, a numerical example is used to demonstrate the feasibility and comparative analysis is presented to show the novelty and validity of the proposed method. [ABSTRACT FROM AUTHOR]

The present study introduces a q-rung orthopair hesitant fuzzy stochastic method based on regret theory to capture the psychological behavior of decision makers (DMs) in decision making. For this, first, according to the score and variance function of q-rung orthopair hesitant fuzzy number (q-ROHFN), a novel group satisfaction degree is defined, which can precisely mirror the overall level and group divergence. And then, on this basis, an optimization model of criteria weights is constructed, and the Lagrange function is formulated to cope with the situation where the information about criteria weights is entirely unknown. Next, the regret value and the rejoice value are obtained by the provided regret-rejoice function, and the alternatives are ranked according to the total psychological perception values of DMs under multi-state situations. Lastly, a case study concerning stock investment is addressed to demonstrate the implantation of the provided method. Besides this, detailed sensitivity analysis and comparison with relevant literature are performed to discuss the stability and superiority of the presented method. [ABSTRACT FROM AUTHOR]

Blockchain technology is a technology that can effectively support supply chain transparency. An important initial managerial activity is for organisations in supply chains to evaluate and select the most suitable blockchain technology. However, uncertainty and emphasis on sustainable transparency has made this appraisal more complex. This paper: (1) introduces blockchain technology performance measures incorporating various sustainable supply chain transparency and technical attributes; and (2) introduces a new hybrid group decision method, integrated hesitant fuzzy set and regret theory, for blockchain technology evaluation and selection. This method emphasises decision maker psychological characteristics and variation in decision maker opinions. An illustrative application and sensitivity analysis is introduced to aid supply chain managers and researchers understand the blockchain technology selection decision. Methodological and managerial implications associated with the decision tool and application are introduced. This research sets the foundation for significant future research in blockchain technologies evaluation in a supply chain environment. [ABSTRACT FROM AUTHOR]

In view of the complexity, vagueness, and systematization of metal mine emergency rescue system evaluation, regret theory was introduced, and a comprehensive evaluation model of the metal mine emergency rescue system was established. Firstly, from four perspectives, including pre-emergency prevention, pre-emergency preparation, emergency rescue during an event, and post-disaster recovery and reconstruction, 26 influencing factors were selected to build a comprehensive evaluation index system of metal mine emergency rescue systems. Secondly, the G1 method and the anti-entropy weight method were used to determine the subjective weight and objective weight of the evaluation indicators, respectively, and the comprehensive weight of the indicator was determined based on game theory. The final evaluation level was determined by calculating the total evaluation value of the object to be evaluated. Finally, the established comprehensive evaluation model of a metal mine emergency rescue system based on game theory and regret theory was applied to Chengchao Iron Mine in Ezhou City, Hubei Province, and the evaluation results of this model were compared with those of fuzzy comprehensive evaluation and set pair analysis. The results showed that the calculation results of the evaluation model are reasonable and reliable, which can provide a new means of evaluating emergency rescue systems in metal mines. [ABSTRACT FROM AUTHOR]

For multi-attribute group decision-making (MAGDM) problems, this paper proposes a three-way consensus model based on regret theory (RT) under the framework of probabilistic linguistic term sets (PLTSs), i.e., the PL-RT-GTWD model. Specifically, the PL-RT-GTWD model mainly consists of the following two components: (1) The consensus measurement considering the relativity among decision-makers (DMs); (2) A three-way consensus feedback mechanism with the minimum adjustment. First, the relative relationship between DMs is supplemented, and the consensus degree of the DM evaluation matrix is measured by a newly developed distance measure. Second, an optimization model with the minimum adjustment for all DMs to modify and reach the final desired goal (group consensus opinions) is constructed, so that DMs can achieve a balance between reaching consensus and maintaining individual independence. Then, taking the minimum adjustment before and after the adjustment as the objective function, the increase of consensus degrees and the initial range of adjustment parameters as constraints, the goal programming on adjustment parameters is constructed. It is noteworthy that the initial range of adjustment parameters is divided via three-way decision (TWD) tools that consider the regret generated during the adjustment, so as to avoid the subjectivity of the adjustment and reduce adjustment costs. Finally, comparative and sensitivity analyses are carried out to verify the feasibility and superiority of the constructed model. • A three-way consensus model with regret theory is designed. • A new distance measure in PLTSs is defined. • A PL-RT-GTWD model is established. • A feedback adjustment mechanism is considered. • An optimization model with minimum cost is explored. [ABSTRACT FROM AUTHOR]

Considerable number of economic and financial postulations conclude that individuals act rationally in the process of decision-making by considering all available information. But there is corroboration to show recurrent patterns of irrationality in the way humans make decisions and choices when faced with uncertainty. The traditional finance paradigm seeks to understand the financial market using models in which agents are rational. Unfortunately, after years of effort, it has become clear that basic facts about the aggregate stock market, a cross-section of average returns and individual trading behaviour are not easily understood in this framework. Behavioural finance proposes psychology-based theories to explain stock market anomalies, such as severe rise and fall in stock price. The purpose is to identify and understand why people make certain financial choices. Within behavioural finance, it is assumed the information structure and the characteristics of the market participant systematically influence an individual's investment decision as well as market outcomes. The fact that even the most prominent and well-educated investors were affected by the collapse of the speculative bubble in the 2008 subprime crisis proved that something was fundamentally missing in the traditional models of rational market behaviour. In this paper, behavioural finance theories such as Herding theory, Prospect theory, Regret theory, and Anchoring theory are related to different investment strategies like Buy and hold, Fundamental analysis and technical analysis. In order to achieve this aim, the research on the subject was divided into two stages. The first stage examined the main issues of individual investors and studied their relation to behavioural finance theories. The aim of the final stage was to form a pattern between the most common investment strategies and the four behavioural finance theories. The most significant finding was the correlation between Buy and hold strategy and regret theory. [ABSTRACT FROM AUTHOR]

To study the bilateral matching problem of new R&D institution-talent teams based on uncertain linguistic assessment information and multiple indicators-multiple talents, a cloud model regret theory-based information gathering method is proposed, and a bi-objective bilateral matching model based on single-indicator utility maximization and overall indicator utility maximization is constructed.. The method firstly constructs the demand indicators of new R&D institutions for talent teams, uses cloud data to characterize uncertain group linguistic assessment information, and converts cloud data into cloud perceived utility based on power function; secondly, calculates the indicator weights of each expert based on entropy power method, and secondly uses entropy power method to calculate comprehensive indicator weights, optimally solves objective expert weights based on the minimum variance of assessment information among experts, and integrates with subjective expert Again, based on regret theory, the cloud perceived utility of each talent under each index is converted into regret cloud perceived utility, and set with the index weights and expert weights into comprehensive cloud perceived utility; finally, a local-whole dual-objective bilateral matching model is constructed to obtain the matched talent team, and example analysis and method comparison are used to show that the method has feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

Globally, industries are focusing on green habits, with world leaders demanding net zero carbon; clean energy is considered an attractive and viable option. The Internet of things (IoT) is an emerging technology with potential opportunities in the clean energy domain for quality improvement in production and management. Earlier studies on IoTs show evidence that direct adoption of such digital technology is an ordeal and incurs adoption barriers that must be prioritized for effective management. Motivated by the claim, in this paper, the authors attempt to prioritize the diverse adoption barriers with the support of the newly proposed Fermatean fuzzy-based decision framework. Initially, qualitative rating information is collected via questionnaires on barriers and criteria from the circular economy (CE). Later, these are converted to Fermatean fuzzy numbers used by integrated approaches for decision processes. A regret scheme is put forward for determining CE criteria importance, and the barriers are prioritized by using a novel ranking algorithm that incorporates the WASPAS formulation and experts' personal choices during rank estimation. The applicability of the developed framework is testified via a case example. Sensitivity analysis and comparison reveal the merits and limitations of the developed decision model. Results show that labor/workforce skill insufficiency, an ineffective framework for performance, a technology divide, insufficient legislation and control, and lack of time for training and skill practice are the top five barriers that hinder IoT adoption, based on the rating data. Additionally, the criteria such as cost cutting via a reuse scheme, resource circularity, emission control, and scaling profit with green habits are the top four criteria for their relative importance values. From these inferences, the respective authorities in the clean energy sector could effectively plan their strategies for addressing these barriers to promote IoT adoption in the clean energy sector. [ABSTRACT FROM AUTHOR]

We examined changes in personal life insurance purchase decisions after a public health event by incorporating perceived health risk and regret into the expected utility function. The model predicts that the epidemic will create incremental insurance demand. Based on the 2003 severe acute respiratory syndrome outbreak in China, we used a panel dataset of 30 provinces from 2000 to 2007 and applied the difference‐in‐differences method to confirm the prediction empirically. The results showed that the epidemic did not significantly impact the demand for life insurance in the short term but played a role in the long term. People increased their health‐care expenditure and premiums for new policies after the severe acute respiratory syndrome event, suggesting that the epidemic changed people's perceived risk and triggered anticipated regret, which increased life insurance demand. Some robustness checks also supported our findings. [ABSTRACT FROM AUTHOR]