A maximizing dominance degree model based on trust relationships and TODIM method to reach group consensus

To obtain the suitable alternative(s) for the organization, this paper proposes a more practical method to solve the decision-making problems in society. That is combined with the TODIM (TOmada de decisão interativa multicrit e ´ rio). The maximizing dominance degree model to reach consensus is proposed with two following components: (1) constructing the complete trust relationships network; (2) the maximizing dominance degree feedback mechanism to reach group consensus. Therefore, firstly owing to the complexity of the trust relationships network, judging the direct and indirect trust propagation paths among the decision makers (DMs) to construct the complete trust relationships network and identifying the highest value of Trust Score (TS) as the leader is possible. Then identify the inconsistent DM based on the established consensus index. During the feedback process, inconsistent DMs adopt the feedback mechanism based on the dominance degree of the leader until the group consensus is reached. Later, the corresponding ranking result is calculated by the TODIM method. Finally, a numerical example is applied to illustrate the effectiveness and feasibility of the optimal model.

Daily Tackling Heath Inequalities: French Child Individual-Level Deprivation Index Development and Validation

The purpose of this study was to develop and validate a pediatric individual-level index for deprivation, usable in clinical practice and in public health. The index had a 4 phases development: items generation with literature review and experts interviews, items reduction with steering committee consensus, index derivation with multivariate analysis, and index validation with psychometric and Pearson analysis. French Child Individual-Level Deprivation Index (FrenChILD-Index) was addressed by untrained healthcare professionals in a cross-sectional multicentric study. The deprivation burden was blindly evaluated in every domain of lifestyle by an expert. Children in need of one specific type of healthcare for deprived children were: moderately deprived. Children in need of referral to a socio-medical unit for access to healthcare were: severely deprived. The main outcome measure was the agreement between FrenChILD-Index results and expert evaluation.Development phases produced a 12-item instrument. Validation phases were carried out in a 986 children sample. FrenChILD-Index fulfilled the Terwee validity criterion for screening instruments. For moderate deprivation, sensitivity was 96.0% [92.6; 98.7] and specificity 68.3% [65.2; 71.4]. For severe deprivation, sensitivity was 96.3% [92.7; 100] and specificity 91.1% [89.2; 92.9]. It correlated with the number of lifestyle deprived domains 0.80 [0.77; 0.83] and the amount of specific healthcare for children 0.86 [0.83; 0.88].Conclusions: FrenChILD-Index is the first pediatric individual-level index of deprivation, methodologically validated in Europe. FrenChILD-Index enables individual appropriate referral for deprived children. It enables considering social determinants of health into account in epidemiological adjustment, patient sample stratification and program impact measurement. (NCT03640715, 21/08/2018)

On Consensus Index of Triplex Star-Like Networks: A Graph Spectra Approach

In this article, the consensus-related performances of the triplex multi-agent systems with star-related structures, which can be measured by the algebraic connectivity and network coherence, have been studied by the characterization of Laplacian spectra. Some notions of graph operations are introduced to construct several triplex networks with star substructures. The methods of graph spectra are applied to derive the network coherence, and some asymptotic behaviors of the indices have been derived. It is found that the operations of adhering star topologies will make the first-order coherence increase a constant value under the triplex structures as parameters tend to infinity, and the second-order coherence have some equality relations as the node related parameters tend to infinity. Finally, the consensus related indices of the triplex systems with the same number of nodes but non-isomorphic graph structures have been compared and simulated to verify the results.

Developing a Descriptive Sensory Characterization of Flour Tortilla Applying Flash Profile

For any food, it is important to know consumption, preference, and the characteristics as quality parameters that are important to consumers of a product. The descriptive methodologies are an important tool to know the quality attributes of the products. Within these methodologies is the flash profile (FP), which is based on the generation of the distinctive attributes of the products without any expensive and time-consuming training sessions. The aim of this research was to study the consumption and preference of flour tortillas by consumers and to develop the descriptive characterization of the tortillas by using the flash profile method. The wheat flour tortillas used were two commercial and two handcrafted samples. Ten experienced panelists participated as the FP panel. The panelists generated 22 descriptors, six for texture, seven for appearance, five for odor, and four for flavor. These descriptors differentiate the samples of the flour tortillas. The panelists’ performance was assessed using the consensus index (Rc = 0.508). The first two dimensions of the Generalized Procrustes Analysis represent 83.78% of the data variability. Flash profile proved to be an easy and rapid technique that allowed the distinctive attributes of flour tortillas to be obtained.

Large-scale group decision-making based on Pythagorean linguistic preference relations using experts clustering and consensus measure with non-cooperative behavior analysis of clusters

To represent qualitative aspect of uncertainty and imprecise information, linguistic preference relation (LPR) is a powerful tool for experts expressing their opinions in group decision-making (GDM) according to linguistic variables (LVs). Since for an LV, it generally means that membership degree is one, and non-membership and hesitation degrees of the experts cannot be expressed. Pythagorean linguistic numbers/values (PLNs/PLVs) are novel choice to address this issue. The aim of this paper which we propose a GDM problem involved a large number of the experts is called large-scale GDM (LSGDM) based on Pythagorean linguistic preference relation (PLPR) with a consensus model. Sometimes, the experts do not modify their opinions to achieve consensus. Therefore, the experts’ proper opinions’ management with their non-cooperative behaviors (NCBs) is necessary to establish a consensus model. At the same time, it is essential to ensure the proper adjustment of the credibility information. The proposed model using grey clustering method is divided with the experts’ similar evaluations into a subgroup. Then, we aggregate the experts’ evaluations in each cluster. A cluster consensus index (CCI) and a group consensus index (GCI) are presented to measure consensus level among the clusters. Then, we provide a mechanism for managing the NCBs of the clusters, which contain two parts: (1) NCB degree is defined using CCI and GCI for identifying the NCBs of the clusters; (2) implemented the weight punishment mechanism of the NCBs clusters to consensus improvement. Finally, an example is offered for usefulness of the proposed approach.

Consensus Analysis for the Panel Responses and the Psychometrics Properties in Script Concordance Testing

Background: Clinical reasoning is an essential attribute in the teaching, learning, and assessment part of medical education for undergraduates. In using the Script Concordance Test (SCT) to foster clinical reasoning, expert panel members’ responses are initially created. There is no agreement in optimizing the panel members’ responses. Our study aimed to develop and validate an SCT and test the utility of the consensus index and panel response pattern. Methods: The methodology was an evolving pattern of constructing SCTs, administering them to the panel members, optimizing the panel with response pattern and consensus index. The SCT’s final items were chosen to be administered to the students. Item-total correlation and Cronbach’s alpha were calculated from the students’ scores. Results: Our study developed an SCT with 98 items and was administered to 20-panel members. The mean score of the panel members for these 98 items was 79.5 (+/- 4.4 SD). On optimizing with the panel responses, 14 items had a uniform response pattern, and 2 had bimodal response patterns. The consensus index calculated for the 98 item SCT ranged from 25.81 to 100. When the 16 items of bimodal and uniform response pattern were eliminated, the consensus index ranged from 58.65 to 100. We administered this 82 items SCT to 30 undergraduate and ten postgraduate students. The mean score of undergraduate students was 61.1 (+/-7.5 SD), and that of postgraduate students was 67.7 (+/- 6.3 SD), which was statistically significant using an independent t-test. Cronbach’s alpha for this 82 item SCT was 0.74. On analysing the item-total correlation, 22 items had a correlation of less than 0.05. Excluding these 22 poor items, the final SCT instrument of 60 items had a Cronbach’s alpha of 0.82. Conclusion: The consensus index can also be used to optimize the items for panel responses in SCT. Our study revealed that a consensus index of above 60 had a good item-total correlation with good internal consistency. Our study also revealed that the panel response clustering pattern could also be used to categorize the items though bimodal and uniform distribution patterns need further differentiation.

Fuzzy Nonbalanced Hedonic Scale (F-NBHS): A New Method for Treatments of Food Preference Data Collected with Hedonic Scales of Points

Hedonic point scales are widely used in food preference studies. However, in this type of scale, the symmetrical distribution of categories and inaccuracy of the responses may interfere with the results of the research. This paper proposes the fuzzy nonbalanced hedonic scale (F-NBHS) as a new method for treatments of food preference data collected with hedonic scales of 9 points and can be generalized to scales with a different number of points. Data analysis from F-NBHS aims to improve the limitations presented by a traditional treatment, especially regarding the distribution of numerical values between the categories and the inaccuracy of the responses. The validation of the proposed scale was carried out through a food preference research done within a Portuguese university. A set of 64 foods, divided into 8 food groups, was evaluated by 119 students in two experiments. The frequency and variability of the data were studied according to the categories in different areas of the scale. Findings showed that the structure of the proposed scale is observed in the behavior of experimental data and intermediate areas, which indicated the intensity of perception and variability of different responses from other areas of the scale. The data used with F-NBHS were more satisfactory in relation to standard deviations and consensus index measurements compared with a traditional treatment. Thus, it is concluded that the F-NBHS scale is a more efficient and robust method for the treatment of dietary preference information compared to a traditional treatment.

A Consensus Reaching Model with Minimum Adjustments in Interval-Valued Intuitionistic MAGDM

This paper focuses on multiattribute group decision-making problems with interval-valued intuitionistic fuzzy values (IVIFVs) and develops a consensus reaching model with minimum adjustments to improve the consensus among decision-makers (DMs). To check the consensus, a consensus index is introduced by measuring the distance between each decision matrix and the collective one. For the group decision-making with unacceptable consensus, Consensus Rule 1 and Consensus Rule 2 are, respectively, proposed by minimizing adjustment amounts of individual decision matrices. According to these two consensus rules, two algorithms are devised to help DMs reach acceptable consensus. Moreover, the convergences of algorithms are proved. To determine weights of attributes, an interval-valued intuitionistic fuzzy program is constructed by maximizing comprehensive values of alternatives. Finally, alternatives are ranked based on their comprehensive values. Thereby, a novel method is proposed to solve MAGDM with IVIFVs. At length, a numerical example is examined to illustrate the effectiveness of the proposed method.

PROBABILITY-HESITANT FUZZY SETS AND THE REPRESENTATION OF PREFERENCE RELATIONS

Probability interpretations play an important role in understanding decision makers’ (DMs) behaviour in decision making. In this paper, we extend hesitant fuzzy sets to probability-hesitant fuzzy sets (P-HFSs) to enhance their modeling ability by taking DMs’ probabilistic preferences into consideration. Based on P-HFSs, we propose the concept of probability-hesitant fuzzy preference relation (P-HFPR) to collect the preferences. We then develop a consensus index to measure the consensus degrees of P-HFPR, and a stochastic method to improve the consensus degrees. All these results are essential for further research on P-HFSs.