Decision Maker's Preferences Research Articles

Correlation coefficients between normal wiggly hesitant fuzzy sets and their applications

The multi-criteria decision-making (MCDM) field has long sought tools capable of adeptly capturing the intricacies of human decision-making amidst uncertainty. Hesitant fuzzy sets (HFS) have become a cornerstone in the MCDM field due to their ability to capture the intricacies of human decision-making under uncertainty. Nonetheless, we identified a significant gap in traditional HFS formulations, which often fail to fully harness the nuanced and implicit preferences of decision-makers (DMs). This shortcoming can lead to suboptimal decision outcomes in complex and uncertain environments. We introduce the normal wiggly hesitant fuzzy set (NWHFS), a novel construct that encapsulates both explicit and implicit preferences within a more representative framework. This study pioneers the development of new correlation coefficients for NWHFSs, offering a robust quantitative measure to elucidate the intricate relationships between variables. Our findings demonstrate that NWHFSs significantly enhance the MCDM process, providing a nuanced perspective that traditional HFS models cannot match. The proposed correlation coefficients not only reveal the concealed preferences of DMs but also broaden the decision-making spectrum, offering a more profound understanding of the relationships between alternatives and criteria. We illustrate the superiority of our approach through comparative analysis with existing methods, highlighting its ability to discern subtleties that other models overlook. Moreover, we integrate NWHFSs into clustering analysis, showcasing their potential to classify data sources with shared attributes effectively. This integration is particularly noteworthy for its ability to navigate complex datasets, offering a new dimension in data mining and resource retrieval. In essence, our study redefines the MCDM paradigm by introducing NWHFSs and their correlation coefficients, setting a new standard for decision-making accuracy and insight. The implications of our work extend beyond theory, offering practical solutions to real-world decision-making challenges.

The impact of entrepreneurs’ wives participating in corporate management on company risk-taking

To investigate the influence of spouses of business leaders on corporate decision-making behavior, we use data from 2003 to 2019 from Chinese listed family-controlled companies to examine the impact of entrepreneurs’ wives participating in corporate management on the risk-taking of family businesses. We found that entrepreneurs’ wives participating in company management significantly reduce the risk taking of family businesses, particularly when the wife is a shareholder in the company. Additionally, the wife’s age, level of education, and professional experience also affect her attitude and behavior toward participating in company management. When the wife is older and has a higher degree of education, the risk taking of the enterprise decreases. However, the wife’s rich professional experience can mitigate female conservatism and increase the company’s risk taking. Our research has implications for understanding gender differences in the context of the extension of marital power of Chinese entrepreneurs’ wives from the family to corporate decision-making and risk preferences. This helps to deepen our understanding of the role and status of women in China’s family business management.

Antibiotic Use at the End of Life: Current Practice and Ways to Optimize.

Infections are common complications in end of life (EOL). However, clinicians have minimal guidance regarding antibiotic decision-making in EOL care, leading to the overuse of antibiotics. While symptom relief is frequently cited as a major reason for antibiotic use in EOL, antibiotics have not been shown to provide significant improvement in symptoms outside of urinary tract infections. In addition, when prognosis is expected to be in the range of days to weeks, antibiotics have not been shown to provide significant survival benefit. Antibiotics can be beneficial in EOL care in appropriate scenarios, but the current widespread use of antibiotics in EOL requires reevaluation. There needs to be broader efforts to think about antibiotics like other invasive medical procedures in which benefits and risks are weighed, recognizing that not all patients in EOL who receive antibiotics will benefit. In addition, during care planning process, discussing and documenting antibiotic preferences will be beneficial. Non-antibiotic symptom management should be incorporated to plan of care when infection is suspected. Ultimately, the use of antibiotics at EOL should be for the clear benefit for the recipient and should be guided by the type of infection and its clinical course, patients' primary disease and its prognosis, and the preferences of patients or surrogate decision makers.

Research on Risk-Averse Procurement Optimization of Emergency Supplies for Mine Thermodynamic Disasters

Reducing procurement risks to ensure the supply of emergency supplies is crucial for mitigating the losses caused by mine thermodynamic disasters. The risk preference of decision-makers and supply chain collaboration are the important aspects for this reductiom. In this study, a novel P-CVaR (Piecewise conditional risk value) distributionally robust optimization model is proposed to accurately assist the decision-makers’ decision of risk preference for reducing procurement risks. Meanwhile, the role of cooperation between procurement and reserves are considered for the weakening procurement risks. A risk-averse bi-level optimization model is proposed to obtain the optimal procurement strategy. Furthermore, by applying the Lagrange duality theorem, the complexity of the bi-level optimization model is simplified then solved using a PSO algorithm. Using empirical analysis, it has been verified that the model presented in this paper serves as a valuable guideline for mine thermodynamic pre-disaster emergency material procurement strategies for the prevention of thermodynamic disasters.

A GIS-based multi-criteria approach for identifying areas vulnerable to subsidence in the world's largest ongoing urban socio-environmental mining disaster

This study proposes a GIS-based multi-criteria approach to identify areas vulnerable to subsidence in the world's largest ongoing urban socio-environmental disaster, caused by salt-gem urban mining in Maceió, Alagoas State, Brazil. The analysis revealed critical areas concentrated in regions with salt-gem mines and soil composed of sedimentary rocks near water bodies, where the local population faces significant socio-economic challenges. In addition to addressing environmental vulnerabilities, the developed methodology considers social and economic risks, aiming for integrated actions between the population and the environment. The FITradeoff multi-criteria method used in the geospatial model showed benefits, including a significant reduction in the number of inconsistencies required for geospatial decision-making, ensuring efficiency in the development of prevention and mitigation strategies. The Spatial Inference model showed a high level of accuracy of 88.89 % in classifying the vulnerability of Maceió’s neighborhoods to subsidence, although it faced challenges when assessing neighborhoods with distinctive characteristics. The proposed preference-driven multi-criteria model incorporates decision-maker preferences, opinions, and knowledge of this disaster. Under specific conditions, this model can offer tailored solutions that align with stakeholders’ objectives. The results provide valuable insights for the development of public policies and risk management strategies in affected areas and areas vulnerable to subsidence due to urban mining in Maceió, and may be applicable to other regions of Brazil and the world facing similar challenges.

Risk-Adaptive Local Decision Rules

The decision rules developed in this paper help managers recognize relationships between data and decisions and prescribe courses of actions that are guaranteed near-optimal as well as transparent and interpretable. Transparency and interpretability are prerequisites for any decision support tool that needs to be conveyed, understood, and justified broadly. An understanding of the relationship between data and decisions is important for managers as they identify the critical factors, prioritize their focus, guide the resource allocation, and develop mitigation strategies for handling data uncertainty. The relationship between data and decision can be especially convoluted for combinatorial optimization problems. We consider simple decisions rules that ensure transparency and interpretability, but also allow nearly any continuous decision rule within a unified framework of analysis. Our preferred adaptive minimum decision rule prescribes decisions with 1% relative suboptimality gap, on average, even when facing distributional shifts in out-of-sample testing.

Preferences for ethical intermediaries and sustainable investment decisions in micro-firms: The role of financial literacy and digital financial capability

This study analyses the effects of financial literacy and digital financial capabilities in shaping entrepreneurs’ preferences for ethical financial intermediaries and sustainable investment decisions. Exploiting novel survey data on Italian micro-enterprises, we find that more financially literate business owners have higher preferences for intermediaries with a strong ethical stance, are more likely to make investments inspired by environmental and social criteria and have a higher awareness of sustainable financial products. Moreover, digital financial competencies are found to play a significant role in enhancing preferences for ethical intermediaries and sustainable investment decisions. Finally, we show that the beneficial effect of financial literacy is mostly direct, while the indirect effect operating through higher digital financial abilities, despite being statistically significant, impacts firms’ sustainable financing and investing decisions only to a lower extent.

Decision-making preferences and regret in rectal cancer patients undergoing restorative proctectomy: A prospective cohort study

BackgroundHow patients make treatment choices in rectal cancer is poorly understood and may affect long-term regret and satisfaction. The objective of this study is to characterize decision-making preferences and their effect on decisional regret in patients undergoing restorative proctectomy for rectal cancer. MethodsA prospective cohort study was conducted in a single academic specialist rectal cancer center from October 2018 to June 2022. Adult patients who underwent restorative proctectomy at least one year prior were recruited. Health literacy was assessed using the BRIEF instrument. Decision-making preferences regarding cancer treatment were assessed using the Control Preferences Scale. Decisional regret regarding their choice of restorative proctectomy was assessed using the Decision Regret Score. Bowel dysfunction was measured using the low anterior resection syndrome score. ResultsOverall, 123 patients were included. Health literacy was categorized as adequate in 63%, marginal in 25%, and limited in 12%. Patients with adequate health literacy were more likely to prefer a collaborative decision-making role compared with those with low health literacy (86% vs 65%, P = .016). Patients with incongruence between preferred and actual decision-making roles were more likely to report high regret (56% vs 25%, P = .003). Patients with major low anterior resection syndrome were also more likely to experience high regret compared with patients with no/minor low anterior resection syndrome (44% vs 25%, P = .036). ConclusionA significant proportion of patients with rectal cancer undergoing restorative proctectomy do not have a decision-making role that is congruent with their preferences, and these patients experience a high degree of regret.

Exploring communication preferences and risk thresholds of clinicians and parents of febrile infants under 90 days presenting to the emergency department: a qualitative study

BackgroundFebrile infants under 3 months of age are at higher risk of invasive bacterial illness (IBI) when compared with older children. Increasingly sequential assessment based on age, clinical appearance and...

Stakeholder involvement and preferences in landscape protection decision-making: a systematic literature review

Stakeholder involvement and preferences are pivotal in the decision-making process for landscape protection within a specific context. However, many decisions regarding landscape protection options still rely on management choices with little or no consideration of all stakeholders. Previous scholars emphasized the importance of establishing an integrated framework to gain an adequate understanding of the process of stakeholders’ decision-making in landscape protection. Therefore, a systematic literature review was conducted on the topic of stakeholders’ involvement and preferences in landscape protection decision-making. This review included research articles published from 2013 to 2023 using two databases and registers, namely, Science Direct and Google Scholar. A total of 110 research articles were identified and qualified for review based on the screening requirements, with an additional 15 documents for theories and backgrounds to provide a better understanding and outcomes for the study. The results of this study were organized based on concepts from the resulting research articles and were integrated to propose a conceptual framework for Stakeholders’ involvement and preferences in landscape protection. Additionally, this study’s findings indicate the significance of incorporating diverse stakeholders and their preferences in landscape protection processes to ensure awareness of inclusivity in decision-making and secure long-term support.

The Absurdity of Rational Choice: Time Travel, Foreknowledge, and the Aesthetic Dimension of Newcomb Problems

Nikk Effingham and Huw Price argue that in certain cases of Newcomb problems involving time travel and foreknowledge, being given information about the future makes it rational to choose as an evidential decision theorist would choose. Although the cases they consider have some intuitive pull, and so appear to aid in answering the question of what it is rational to do, we argue that their respective positions are not compelling. Newcomb problems are structured such that whichever way one chooses, one might be led by one’s preferred decision theory to miss out on some riches (riches which others obtain whilst employing their preferred decision theory). According to the novel aesthetic diagnosis we shall offer of the Newcomb dialectic, missing out in this way does not render one irrational but, rather, subject to being seen as absurd. This is a different kind of cost but not one that undermines one’s rationality.

A Multi-attribute Decision-making Method for Interval Rough Number Information System Considering Distribution Types

This paper proposes a novel multi-attribute decision-making (MADM) method for interval rough numbers (IRNs) considering different distribution types, namely uniform, exponential, and normal distributions. Upper and lower approximate interval dominance degrees are defined and aggregated using dynamic weights to obtain pairwise comparisons of IRNs. The properties of dominance are verified, and an attribute weight determination method based on the dominance balance degree is introduced. The proposed MADM method is data-driven and does not rely on the subjective preferences of decision-makers. Case analysis demonstrates the effectiveness and rationality of the proposed method, revealing that the distribution type of IRNs significantly impacts decision results, potentially leading to reversed ranking outcomes. The proposed method offers a comprehensive framework for handling MADM problems with IRNs under different distributions.

Organ Offer Review Cards: Improving Transparency on the Kidney Transplant Waitlist.

The 2022 National Academy of Sciences, Engineering, and Medicine report on equity in organ transplantation highlighted limited transparency and accountability for organ offer declines and recommended prioritizing patient engagement in decisions regarding organ offers. Yet, there is no guidance on how to incorporate patients in organ offers. We elected to study the experiences of patients on the waitlist and their perception of a novel Organ Offer Review Card (OORC). A prototype OORC was created using Donornet refusal codes. Sixty randomly selected kidney waitlist patients at a single center were asked to participate in a web-based survey focusing on current medical decision-making preferences and perceptions of the prototype OORC. Among the 43 patients reached, 17 (39.5%) completed the survey. Most participants (88.2%) expressed it was important to be involved in the decision-making about organ offers, with 100.0% of respondents wanting to know why an organ was declined. Regarding the prototype OORC, 94.1% thought it helped them understand the factors and priorities considered when selecting an organ, and 88.2% said it increased their belief that their team was acting in their best interest. An OORC could increase transparency and communication during the waitlist process while enhancing trust in the transplant team.

A novel preference-driven evolutionary algorithm for dynamic multi-objective problems

Most studies in dynamic multi-objective optimization have predominantly focused on rapidly and accurately tracking changes in the Pareto optimal front (POF) and Pareto optimal set (POS) when the environment undergoes changes. However, there are real-world scenarios where it is necessary to simultaneously solve changing objective functions and satisfy the preference of Decision Makers (DMs). In particular, the DMs may be only interested in a partial region of the POF, known as the region of interest (ROI), rather than requiring the entire POF. To meet the challenge of simultaneously predicting a changing POF and/or POS and dynamic ROI, this paper proposes a new dynamic multi-objective evolutionary algorithm (DMOEAs) based on the preference. The proposed algorithm consists of three key components: an evolutionary direction adjustment strategy based on changing reference points to accommodate shifts in preferences, an angle-based search strategy for tracking the varying ROI, and a hybrid prediction strategy that combines linear prediction models and population manifold estimation within the ROI to ensure convergence and distribution in scenarios where preferences remain unchanged. Experimental studies conducted on 30 widely used benchmark problems in which it outperforms contrasting algorithms on 71% of test suits. Empirical results demonstrate the significant advantages of the proposed algorithm over existing state-of-the-art DMOEAs.

The Influence of Social Media on the Consumer Decision-Making Process and Preferences in the Digital Era

The Influence of Social Media on the Consumer Decision-Making Process and Preferences in the Digital Era

Consensus-oriented linguistic multi-criteria group sorting method incorporating dynamic trust management

Multi-criteria group sorting (MCGS) concerns the multi-person process of evaluating alternatives and assigning them to predetermined ordered categories under certain criteria. Identifying class thresholds and coordinating conflicting opinions are two primary challenges within this process. Taking into account the 2-tuple linguistic preferences of decision-makers (DMs), we propose a consensus-oriented sorting method that incorporates dynamic trust management for linguistic MCGS. The main contributions and innovations are as follows. (a) A group consensus maximization model is constructed to optimize class thresholds and initial individual sorting results. (b) Integrating familiarity-based trust and similarity-based trust, the dynamic comprehensive trust among DMs is established. On this basis, a feedback mechanism is designed, in which the bi-objective programming is utilized to generate adjustment recommendations. (c) The final consensual group sorting result is obtained by maximizing the similarity of group views. (d) The feasibility of the proposed method is demonstrated using a case of sorting fresh agricultural products. Using sensitivity analysis and comparative analysis, the proposed method is verified to be rational and effective. A comparison based on real datasets and a qualitative comparison further demonstrate the validity and characteristics of the proposed method.

Three-way decision based on three-way preference measures and three-level dominance relations in interval-valued systems

Interval-valued systems facilitate data decision, and their preference measures and dominance relations become pivotal for three-way decision (3WD). However, the recent D-type preference measure and partial-overall dominance relation between intervals respectively exhibit the weak representation and incomplete hierarchy, so corresponding results need further development. Aiming at interval-valued systems, an S-type measure is proposed to constitute three-way preference measures, and three-level dominance relations are established, so criss-cross knowledge granulations motivate systematic 3WD models and applications. First, the S-type preference measure is proposed from the sigmoid function, it exhibits good learning semantics and mathematical properties, and its supplementation and improvement induce three-way preference measures. Then, by three-level constructions, three-way preference measures are applied for sorting and classification, and the S-type measure exhibits decision effectiveness and recognition superiority. Furthermore, hierarchical three-way preference measures induce three-level dominance relations, and vertical-horizontal condition granulations generate multiple 3WD models on preference decision classes. Finally, all 3WD strategies from criss-cross dominance relations are comprehensively compared and selected via classification error rates; by data experiments, the S-type measure and its hierarchical relations become effective and optimal for 3WD, and the corresponding 3WD approaches outperform the existing methods from the D-type measure and partial-overall relation.

Intelligent decision modeling for optimizing railway cold chain service networks under uncertainty

Railway cold chain service network design (RCC-SND) aims to optimize the utilization of stations and lines as well as train allocations in a manner that minimizes costs while satisfying the service requirements of shippers. Furthermore, the uncertainties associated with freight demand, transportation costs, quality loss, station handling capacity, and arc capacity make the RCC-SND a complex decision-making problem. To tackle this challenge, we first formulate a Mixed-Integer Nonlinear Programming (MINLP) model to determine hub locations, freight wagon flows, and service frequency. To cope with uncertain parameters with varying degrees of uncertainty incorporated in the model, we extend the problem using fuzzy programming and further convert it to its crisp counterpart. A real-world cases study in Southwest China is performed to validate the proposed model, whose results provide different strategies for decision-makers with varying preferences. There are some main findings: As the number of hubs increases from 5 to 6, a maximum total cost savings of 1.99% can be achieved. Railway operators may opt for different decision preferences, for decisions prioritizing economic efficiency, the cost can decrease by 2.69% compared to deterministic optimization.

Participants' Utilitarian Choice Is Influenced by Gamble Presentation and Age.

No prior behavioral science research has delved into the impact of gamble presentation (horizontal or vertical) on individuals' utilitarian behavior, despite evidence suggesting that such choices can be influenced by comparing attributes like probability and money in gambles. This article addresses this gap by exploring the influence of gamble presentation on utilitarian behavior. A two-factor independent measures design was employed to explore the influence of the type of gamble presentation and age on participants' utilitarian decision-making preferences. The findings showed a reduced likelihood of participants choosing the non-utilitarian gamble with vertically presented gambles compared to horizontal ones. Consequently, participants' utilitarian behavior was influenced by between-gamble comparisons of available attributes, with utilitarian choices (e.g., choosing Gamble A) being more prevalent in vertical presentations due to a straightforward comparison on the probability attribute. Furthermore, the results also revealed that older participants take more time than their younger counterparts when making utilitarian errors. We attribute this to their abundant knowledge and experience. Future research should explore the comparative psychological processing used by participants in risky decision-making tasks.

Poikiloderma vasculare atrophicans: report of a case of long duration.

We present the case of a 70-year-old woman with poikiloderma vasculare atrophicans (PVA), which demonstrated a benign course over 35 years, raising questions about its classification as early-stage mycosis fungoides (MF) or as a distinct entity. Despite receiving no treatment, the patient’s condition remained stable, with characteristic erythematous papules observed on clinical examination. Histopathological and immunohistochemical analyses supported the diagnosis, emphasizing the importance of longitudinal studies and patient preferences in treatment decisions for PVA.