Preference Relations Research Articles

A social network analysis-based model for failure mode and effect analysis under linguistic preference relation environment

Failure mode and effect analysis (FMEA) is a proactive risk analysis technique widely used to improve the reliability and safety of complex systems in various industries. Generally, FMEA is a multidisciplinary team activity in which the trust relationships among experts have an influence on the risk assessment process and the risk priority of failure items. Furthermore, it is significant to obtain the risk ranking result of failure items with a high consistency degree in the group FMEA. Therefore, in this study, we aim to develop a new FMEA model based on double hierarchy hesitant linguistic preference relations to derive the risk priority of failure items considering experts' social network. First, the risk assessments of experts are described by using the double hierarchy hesitant linguistic preference relations via pairwise comparisons of failure items. Second, the relative weights of FMEA experts are derived based on their trust relationships in social network and the consistency of failure risk evaluations. Third, consistency checking and repairing algorithm are performed to address experts’ self-contradictory opinions, and the risk priority of failure items is determined in line with their risk degrees. Finally, a practical risk evaluation case is provided to demonstrate the applicability of the proposed FMEA model, and a comparative analysis is conducted to illustrate its effectiveness and advantages.

Assessing the impact of healthcare service risks on healthcare demand under evolving economic and social structures: An improved GLDS decision making method considering risk attitudes

Faced with backward and uneven economic conditions and an irrational and increasingly inequitable social structure, healthcare disparities are widening and the healthcare inequalities are expanding. This can lead to an increasing public demand for healthcare services, posing unprecedented risk, such as resource shortages, quality deterioration and rising healthcare costs. By identifying, managing, and optimizing the aforementioned risks within the healthcare system, it helps the healthcare system better address the challenges of expanding healthcare demands under evolving economic and social structure. To achieve this, this paper employs failure mode and effects analysis (FMEA) to identify severe failure modes in healthcare services. However, traditional FMEA methods possess limitations in assessing the dynamics of trust relationships and interaction effects among experts within social networks. Therefore, this paper proposes an improved gained and lost dominance score (GLDS) based social network decision-making method considering risk attitudes for FMEA to prioritize failure modes in healthcare services. First, transform linguistic risk assessment information from an interdisciplinary team of experts based on probabilistic linguistic term sets handle uncertain efficiently. Meanwhile, experts’ influence is reflected by an extended PageRank algorithm with trust degrees based on social networks analysis. Next, an improved GLDS method considering the differentiated risk attitudes of individuals and groups is developed to prioritize failure modes. Especially, to solve the problems of soft preference and incomparability relation among failure modes, which are ignored by the traditional GLDS method, a conflict analysis is established to determine the preference, indifference, and incomparability relations. Finally, the practicality of the proposed method is illustrated by the healthcare service risk case in the emergency department and the effectiveness of the developed method is validated by comparison and sensitivity experiments. The results suggest that failure to update labels showing patient severity promptly is the most dangerous. In addition, it would be more economically efficient to analyze the non-comparable failure modes facing different risk factors.

Organic Vegetable Chips: Exploring Romanian Consumers' Preferences in Relation to Socio-Demographic Factors.

In recent years, people have become increasingly interested in adopting a healthy diet, which also extends to healthy snacks, such as chips. Understanding the interplay of factors that influence the preference decisions concerning food products is very helpful in market segmentation for identifying specific groups of consumers with similar needs. This study aims to obtain a better understanding of Romanian consumers' preference for organic vegetable chips vs. classic potato chips. The research involved a transversal, cross-sectional, descriptive, exploratory, and correlational design. Data were collected based on a questionnaire (1060 participants) and processed with the SPSS 22 program, using the Pearson chi-square test and binary logistic regression as statistical procedures. Significant differences were found regarding the distribution of the respondents who prefer organic vegetable chips vs. classic potato chips based on socio-demographic characteristics. The results of the binary logistic regression analysis (χ2 = 102,906, df = 22, N = 909, p < 0.001) indicate that education level and frequency of consumption have a statistically significant impact on the preference for organic vegetable chips. The obtained results can contribute to a better understanding of Romanian consumers' preferences, acting as a knowledge stage in the adoption of a healthy eating style.

Advances of Probabilistic Linguistic Preference Relations: A Survey of Theory and Applications

Advances of Probabilistic Linguistic Preference Relations: A Survey of Theory and Applications

Improving consistency classification: An innovative benchmark‐based approach for the AHP

AbstractThe analytic hierarchy process (AHP) is a cornerstone of multi‐criteria decision analysis, enabling well‐informed choices across diverse contexts. This paper introduces an original benchmark‐based framework designed to enhance the precision of consistency classification for pairwise comparison matrices (PCMs) within the AHP methodology. This innovative approach quantifies the discrepancy between a given PCM and its benchmark matrix, comprising comparison ratios that faithfully reflect the relative preferences encapsulated within principal eigenvector values, thereby capturing the true degree of coherence. To ensure benchmark alignment with human perception, elements of the benchmark PCM are further rounded to the nearest values on the Fundamental Scale. The potency of our framework derives from two pivotal factors: the inherent Priority Preference Range within the principal eigenvector and the order of the PCM. Statistical thresholds for consistency are established using a technique based on simulated, logical PCMs, proposed by Bose [2022]. This rigorous method ensures an unbiased, objective and pragmatic evaluation of consistency, eliminating the subjectivity inherent in arbitrary thresholds based on random PCMs. Our approach rectifies the inconsistencies in the conventional CR method that yields false positives for PCMs of orders 3 and 4, and false negatives for higher orders. By harnessing customized benchmarks and eschewing random matrices, our framework systematically confronts the inherent consistency challenges within AHP, thus enhancing its decision‐making capability. The practical utility of our approach is aptly demonstrated through AHPtools, an R‐based library package designed to showcase our novel consistency evaluation method. The demonstration of the package in Appendix B will facilitate readers to easily apply our methodology to real‐world PCM classification scenarios within the AHP. In conclusion, our benchmark‐based framework heralds a transformative era in consistency classification within the AHP, empowering real‐world multi‐criteria decision‐making with unprecedented precision and reliability, and ushering in a new paradigm of informed and astute outcomes.

Information asymmetry in the graph model of conflict resolution and its application to the sustainable water resource utilization conflict in Niangziguan Springs Basin

With the increasing consumption of global water resources, research on sustainable water resource utilization has become a topic of urgent interest. Despite the promulgation of policies and laws on the protection of water resources, the sustainable utilization and development of water resources in China are still far from reaching their initially established targets. This is partly explained by real-life conflicts that arise, in which different decision makers (DMs) possess different levels of professional knowledge, social status, and even power in contexts where the parties’ ability to leverage information varies greatly. Sustainable water resource utilization conflicts between the government and factories exemplify the impact of this information asymmetry on the relationship between DMs for the purposes of conflict resolution. In this paper, it is assumed that if one DM in a conflict has more information, their information advantage will affect the policy formation of the other DM. Thus, to analyze and resolve relevant problems, the concept of information asymmetry between two DMs is integrated into the basic structure of the graph model for conflict resolution (GMCR). According to the mathematical expression of the information asymmetry preferences of DMs, a set of information asymmetry stability definitions can be introduced to determine the stable state for each DM. The sustainable water resource utilization conflict in Niangziguan Springs Basin is used as an exemplar to confirm the effectiveness and correctness of the proposed method. Stability analysis shows that when the government implements strict environmental policies by leveraging more information, factories should adjust their preference relations to follow the rules and regulations to protect water resource in practice. Finally, comprehensive sustainable utilization and development of natural resource mechanisms must be implemented not only for these factories but also for other industries in China.

A bias-controlled statistical procedure to highlight host-plant preferences and the resulting prevalences in phytophagous insects: illustrative case study with leaf-miners on Lamiaceae

Summary. Quantitative, rigorously derived and statistically validated information regarding the relative preferences/relative prevalences of phytophagous insects for/upon their different host plant species is too rarely made available in the literature, at least for wild host plant species, which are usually lacking economic interest. This seems likely to be due (i) in part to the usual lack of field samplings sufficiently wide (both numerically and spatially) to ensure the statistical significance of results; and (ii) to the current unavailability of a dedicated procedure for appropriately dealing with the unavoidable sources of biases affecting raw field data. Accordingly, a new methodological approach is proposed to overcome both kinds of difficulties, involving in particular: a procedure of evaluation of the relative preferences for hosts and relative prevalences on hosts, making them insensitive to the biases inevitably linked to the unavoidable large disparities among the respective abundances of the focused phytophagous insects on the one hand, and to the hardly controllable heterogeneities in sampling intensities of the different host plant species, on the other hand; an appropriately modified version of the standard Chi-2 testing procedure, applied to control the degree of statistical significance of the estimations of relative preferences and prevalences. The practical implementation of this newly derived approach is illustrated with a case study, dealing with leaf-mining insects upon host plant species belonging to Lamiaceae. From a purely descriptive point of view, more or less strong discrepancies turn out to single out the relative preference profiles, as well as the relative prevalence profiles of 10 leaf miners for four host-plant species (despite the relative phenotypic homogeneity of hosts within Lamiaceae). Highlighted, at a more speculative level, is the significant role played by phylogenetic affinities (between the exploiting species as well as between the exploited species) upon the arrangement of the food web that connects them.

A Local Adjustment Method to Improve Multiplicative Consistency of Fuzzy Reciprocal Preference Relations

Preferences that verify the transitivity property are usually referred to as rational or consistent preferences. Existent methods to improve the consistency of inconsistent fuzzy reciprocal preference relations (FPRs) fail to retain the original preference values because they always derive a new FPR. This article presents a new inconsistency identification and modification (IIM) method to detect and rectify only the most inconsistent elements of an inconsistent FPR. As such, the proposed IIM can be considered a local adjustment method to improve multiplicative consistency (MC) of FPRs. The case of inconsistent FPRs with missing values, i.e., incomplete FPRs, is addressed with the estimation of the missing preferences with a constrained nonlinear optimization model by the application of the IIM method. The implementation process of the proposed algorithms is illustrated with numerical examples. Simulation experiments and comparisons with existent methods are also included to show that the new method requires fewer iterations than existent methods to improve the MC of FPRs and achieves better MC level, while preserving the original preference information as much as possible than the existent methods. Thus, the results presented in this article demonstrate the correctness, effectiveness, and robustness of the proposed method.

Matrix representations of the inverse problem in the graph model for conflict resolution with fuzzy preference

The inverse analysis of the graph model for conflict resolution (GMCR) determines all the preference relationships required by each decision-maker (DM) according to a given stability definition to ensure that the given state is stable. In the paper, a matrix representation approach for the inverse graph model with fuzzy preference relations is proposed to obtain all the preference relations for each DM making the stability result based on the fuzzy stability definition, including fuzzy Nash, fuzzy general metarationality, fuzzy symmetric metarationality, and fuzzy sequential stability definition. For the two-DM graph model, the fuzzy preference relation over states, unilateral movements, and fuzzy unilateral improvements are refreshed by using the matrix forms. Furthermore, for the multiple DMs model, the joint movements and joint improvement for a collection including at least two DM are represented by using matrix representation. Finally, the matrix relationship for specifying the required preference relation in the framework of inverse graph model with fuzzy preferences is derived. To illustrate the usefulness of the matrix representation of inverse graph model with fuzzy preference relations, this paper demonstrates it using a real-life conflict of the Elmira groundwater contamination conflict.

Group decision making based on relative projection between fuzzy preference relations

Group decision making (GDM) has been widely focused on for coping with the complexity of a practical decision making problem. This paper proposes a relative projection-driven GDM model with fuzzy preference relations (FPRs). First, a novel additive consistency index of FPRs is constructed using the variance of relative projections between column/row vectors in two FPRs, which is called the relative projection variance consistency index (RPVCI). The thresholds of RPVCI for FPRs with acceptable additive consistency (AAC) are calculated using the simulation method. For improving consistency of FPRs, a convergent iteration algorithm is constructed by equipping subjective and objective adjustment parameters. Second, the group consensus level is captured using relative projections of individual FPRs on the collective one. An iteration model is reported to optimize the consensus degree by combining the weights of experts with a comprehensive adjustment coefficient that depends on both subjective and objective factors. Third, a novel GDM algorithm is proposed by integrating the two proposed iteration models. Comparisons with some existing models are offered by carrying out a case study to show the novelty and advantage of the proposed GDM model.

Adoption, Fostering, and Parental Absence in Vanuatu.

Alloparenting, wherein people provide care to children who are not their biological offspring, is a key aspect of human child-rearing. In the Pacific, many children are adopted or fostered by custodial alloparents even when both biological parents are still alive. From a behavioral ecology perspective, such behaviors are puzzling: why parent someone else's child at your expense? Furthermore, little is known about how these arrangements are made in Pacific Islander societies today, who provides care, and what kinds of outcomes fostered children experience. A better understanding of these proximate factors may help reveal the ultimate drivers behind custodial alloparenting. Here, we report findings from a survey carried out with the caregivers of 282 children in rural areas of Vanuatu, an island nation in Melanesia. Most fostered and adopted children lived with relatives such as aunts, uncles, and grandparents (87.5%) rather than unrelated caregivers, with a strong preference for maternal kin. The most common reasons for these arrangements were that the parents had separated (16.7%), were engaging in labor migration (27.1%), or a combination of both (27.1%). Results for investment in children's education and their educational outcomes were mixed, although children removed from crisis situations did more poorly than children removed for aspirational reasons. Our findings suggest that custodial alloparenting helps families adapt to socioeconomic transitions and changing marriage practices. Outcomes may depend on a range of factors, such as the reason children were transferred out of the natal home to begin with.

Average consistency index based consensus model for a group decision making problem dealing with ELICIT expressions

A group decision making (GDM) problem is often modeled by using a reciprocal preference relation (RPR) structure. Linguistic information is widely used in RPRs to depict its components because it facilitates its understanding and management of uncertainty. Among the existing linguistic representation models, the extended comparative linguistic expressions with symbolic translation (ELICIT) model improves the accuracy of computational processes and maintains the comprehensibility and interpretability of the results. Therefore, we introduce a new linguistic RPR (L-RPR) structure using ELICIT information, denoted as ELICIT-RPR, whose elements are uniformed as 2-tuple linguistic intervals to simplify and facilitate computations between ELICIT expressions. In addition, the average consistency index (ACI) of ELICIT-RPR is proposed together an algorithm for improving the ACI values of ELICIT-RPRs based on their best and worst consistency indices. To avoid conflicts and obtain agreed solutions, consensus reaching processes (CRPs) have been widely used in GDM. Thus, a novel CRP based on ACI values is developed using a new distance between linguistic intervals. Finally, a case study and a comparison analysis are presented to demonstrate the validity and applicability of the proposal.

A novel Group Decision Making method with the prediction selection rate

Methods on the basis of the consensus reaching process are prevalent in Group Decision Making (GDM), which typically forces some evaluators to revise initial opinions in order to reach group consensus without being able to precisely reflect original viewpoints. Furthermore, in case the correct opinion is embedded in the hand of the minority, existing methods may not reach the correct consensus. With the aim to tackle these observations, a novel approach of the Positive and Negative Prediction Selection Rate (PNPSR) is proposed on the basis of the Pythagorean Fuzzy Preference Relation (PFPR) which enables to present individuals’ opinions in a pairwise manner using the linguistic preference relation. The PFPR expressed opinions then serve as input for the computation of the proposed PNPSR, the minimum of which is subsequently selected as the correct one. Finally, the full ranking of the alternatives can be calculated through the proposed iterative algorithm. In the process, the evaluators’ original opinions are not required to modify, and the correct result can be achieved when the minority evaluators provide the correct opinions. Experimental results demonstrate the efficacy of the proposed approach in comparison with two state-of-the-art methods.

Accuracy and Preferences for Legal Error

Abstract Legal procedures used to determine liability trade-off type-1 errors (e.g., false convictions) against type-2 errors (e.g., false acquittals). After noting that people’s relative preferences for type-1 errors (compared to type-2 errors) appear to be negatively correlated with technological advancements, we study how the accuracy of evidence collection methods may affect the trade-off between these two errors. Counter-intuitively, we find that under some conditions, greater accuracy may result in a higher probability of type-1 error (or type-2 error) maximizing deterrence. Then, assuming both errors are decreasing in accuracy, we characterize the type-1 error that emerges under electoral pressures (when the median voter’s preferences are implemented): convictions occur more often than is socially optimal but less often than is necessary to maximize deterrence. Moreover, as the harm from crime increases, the median voter becomes less tolerant of type-1 errors as the legal system’s accuracy increases. We also show that, because the median voter is less averse toward type-1 errors than the average citizen, an increase in accuracy may reduce welfare.

Interval-valued intuitionistic fuzzy best-worst method with additive consistency

Best-worst method (BWM) has received extensive attention because of its fewer pairwise comparisons and maintenance of consistency. Extending it to various fuzzy environments has become a hot research spot. This paper initiates a new interval-valued intuitionistic fuzzy (IVIF) BWM with additive consistency. Firstly, an IVIF fuzzy preference relation (IVIFPR) is viewed as a directed graph with IVIF weights. An approach is offered to distinguish the best and worst criteria by the defined score out-degree and accuracy out-degree of criteria. The concept of IVIF reference comparisons is introduced. The additive consistency of IVIF reference comparisons is acquired by that of IVIFPR using its left and right intuitionistic fuzzy preference relations. We derive the relationship between the IVIF reference comparisons and the IVIF priority weight vector. Based on this relationship, a linear goal programming model is constructed to generate the IVIF priority weights. Two consistency indexes are analytically obtained and the consistency ratio is then defined to check the consistency of IVIF reference comparisons. For the unacceptably additive consistency, a new approach is put forward to enhance the consistency. Thereby, the proposed IVIF BWM is summarized step by step. Three examples of applications are furnished to demonstrate its effectiveness and validation.

Nonmonotonic Modulation of the Protein-Ligand Recognition Event by Inert Crowders.

The ubiquitous event of a protein recognizing small molecules or ligands at its native binding site is crucial for initiating major biological processes. However, how a crowded environment, as is typically represented by a cellular interior, would modulate the protein-ligand search process is largely debated. Excluded volume-based theory suggests that the presence of an inert crowder would reinforce a steady stabilization and enhancement of the protein-ligand recognition process. Here, we counter this long-held perspective via the molecular dynamics simulation and Markov state model of the protein-ligand recognition event in the presence of inert crowders. Specifically, we demonstrate that, depending on concentration, even purely inert crowders can exert a nonmonotonic effect via either stabilizing or destabilizing the protein-ligand binding event. Analysis of the kinetic network of binding pathways reveals that the crowders would either modulate precedent non-native on-pathway intermediates or would devise additional ones in a multistate recognition event across a wide range of concentrations. As an important insight, crowders gradually shift the relative transitional preference of these intermediates toward a native-bound state, with ligand residence time at the binding pocket dictating the trend of nonmonotonic concentration dependence by simple inert crowders.

Q-rung orthopair hesitant fuzzy preference relations and its group decision-making application

To express the opinions of decision-makers, q-rung orthopair hesitant fuzzy sets (q-ROHFSs) have been employed extensively. Therefore, it is necessary to construct q-rung orthopair hesitant fuzzy preference relations (q-ROHFPRs) as a crucial decision-making tool for decision-makers. The goal of this paper aims to define a new consistency and consensus approach for solving q-ROHFPR group decision-making (GDM) problems. To do this, we first state the definitions of q-ROHFPRs and additive consistent q-ROHFPRs based on q-ROHFSs, an additive consistency index and acceptable additive consistent q-ROHFPRs. Second, based on minimizing the deviation, we establish an acceptable goal programming model for unacceptable additive consistent q-ROHFPRs. Third, an iterative algorithm is created for achieving acceptable consistency and reaching a rational consensus. The degree of rational consensus among individual q-ROHFPRs is quantified by a distance-based consensus index. Afterward, a non-linear programming model is formulated to derive the priority vector of alternatives, which are q-rung orthopair hesitant fuzzy numbers (q-ROHFNs). Based on this model, a GDM model for q-ROHFPRs is then developed. To demonstrate the validity and utility of the proposed GDM model, a case study on the risk assessment of hypertension is provided. The finding of sensitivity and comparison analyses supports the feasibility and efficacy of the suggested approach.

An indirect multicriteria group decision-making method with heterogeneous preference relations and reliabilities of decision-makers

This study proposes a new indirect method to solve multicriteria group decision-making (MCGDM) problems with heterogeneous preference relations and decision-makers’ reliabilities. To unify the heterogeneous preference relations, the transformation of multiplicative preference relations (MPRs), fuzzy preference relations (FPRs), intuitionistic fuzzy preference relations (IFPRs), and linguistic preference relations (LPRs) into distributed preference relations (DPRs) is analyzed. As consistency among DPRs is constructed under the assumption that decision-makers are bounded rationality, the abstract transformation of MPR, FPR, IFPR, and LPR into DPR is defined, and its reality depends on the preferences of decision-makers. Two important properties of abstract transformation, namely, internal consistency and restricted max-max transitivity, were theoretically verified. The reliabilities of decision-makers are involved in MCGDM by estimating the reliability of each criterion to improve solution quality. Based on the four types of abstract transformations, a process for analyzing MCGDM problems with heterogeneous preference relations and decision-makers’ reliabilities is developed. The proposed method is used to analyze a new business selection problem for an enterprise located in Changzhou, Jiangsu, China to demonstrate its applicability and validity.

The interplay between perceived busyness and power distance belief on subjective social status: Consequences for consumer preferences for material versus experiential purchases

AbstractThe perception of a busy life as an indicator of self‐worth varies among individuals. Postulating that this perceptual difference can be attributed to one's attitude toward social stratification, the current research examines the interplay between perceived busyness and power distance belief (PDB). We propose that perceived busyness dictates a consumer's subjective social status under low PDB but not under high PDB. Furthermore, the interaction between perceived busyness and PDB determines an individual's relative preference for purchase types: material versus experiential purchases. Study 1 verifies that consumers' perceived busyness has a positive effect on their evaluation of their social status when they have low PDB, whereas this effect disappears when they have high PDB. Study 2 tests the asymmetric effect of perceived busyness and PDB on preference for purchase types, and Study 3 further provides evidence for the mediating role of subjective social status.

Contextuality in Collective Intelligence: Not There Yet.

Type I contextuality or inconsistent connectedness is a fundamental feature of both the classical as well as the quantum realms. Type II contextuality (true contextuality or CHSH-type contextuality) is frequently asserted to be specific to the quantum realm. Nevertheless, evidence for Type II contextuality in classical settings is slowly emerging (at least in the psychological realm). Sign intransitivity can be observed in preference relations in the setting of decision making and so intransitivity in decision making may also yield examples of Type II contextuality. Previously, it was suggested that a fruitful setting in which to search for such contextuality is that of decision making by collective intelligence systems. An experiment was conducted by using a detailed simulation of nest emigration by workers of the ant Temnothorax albipennis. In spite of the intransitivity, these simulated colonies came close to but failed to violate Dzhafarov's inequality for a 4-cyclic system. Further research using more sophisticated simulations and experimental paradigms is required.