Group Of Decision Makers Research Articles

Weighted consensus clustering and its application to Big data

The aim of this study is the development of a weighted consensus clustering that assigns weights to single clustering methods using the purity utility function. In the case of Big data that does not contain labels, the utility function based on the Davies-Bouldin index is proposed in this paper. The Banknote authentication, Phishing, Diabetic, Magic04, Credit card clients, Covertype, Phone accelerometer, and NSL-KDD datasets are used to assess the efficiency of the proposed consensus approach. The proposed approach is evaluated using the Euclidean, Minkowski, squared Euclidean, cosine, and Chebychev distance metrics. It is compared with single clustering algorithms (DBSCAN, OPTICS, CLARANS, k-means, and shared nearby neighbor clustering). The experimental results show the effectiveness of the proposed approach to the Big data clustering in comparison to single clustering methods. The proposed weighted consensus clustering using the squared Euclidean distance metric achieves the highest accuracy, which is a very promising result for Big data clustering. It can be applied to expert systems to help experts make group decisions based on several alternatives. The paper also provides directions for future research on consensus clustering in this area.

Comparing Groups of Decision-Making Units in Efficiency Based on Semiparametric Regression

We consider a stochastic frontier model in which a deviation of output from the production frontier consists of two components, a one-sided technical inefficiency and a two-sided random noise. In such a situation, we develop a semiparametric regression-based test and compare the technical efficiencies of the different decision-making unit groups, assuming that the production frontier function is the same for all the groups. Our test performs better than the previously proposed ones for the same purpose in numerical studies, and also has the theoretical advantage of working under more general assumptions. To illustrate our method, we apply the proposed test to Program for International Student Assessment (PISA) 2015 data and investigate whether an efficiency difference exists between male and female student groups at a specific age in terms of learning time and achievement in mathematics.

РОЗПОДІЛЕНА СИСТЕМА ПІДТРИМКИ ПРИЙНЯТТЯ ГРУПОВИХ РІШЕНЬ

The decision-making process is an integral part of modern society. Research shows that over the last decades, the use of groups within organizations has increased dramatically­, and each organization operates based on decisions made by groups of individuals within the organization. Almost every aspect of social, political, legislative, and economic life functions through the decision making of groups of individuals. Systems theory views group decision making as interdependent forces capable of being analyzed from the perspective of other forces. The behavior of individuals in groups is due to many reasons, and the systematic method emphasizes multiple cause and effect relationships and the complex interplay of forces. Effective decision-making is seen as a natural consequence of the ability of decision-makers to analyze and understand the group decision-making process, but no one can acquire this ability without actively participating in the group decision-making process. A group decision, in general, is the choice of group members from among the alternatives available to them. Very rarely does a group of individuals make the decision in isolation from the rest of the group. There are currently no simple tools and systems available to make the best decisions, which raises the need for research in this field. Group decision support systems are interactive computerized systems that help a group of decision-makers uses data and models to identify and solve tasks. In this article, the use of Bayesian network analytics is proposed as an approach to implement a distributed group decision support system and resolving conflict situations in the decision-making process. For this purpose, the information technology of the distributed group decision support system was developed, the information model and dynamic data structures were proposed, and the software implementation of the group decision support system was developed.

A decision support system that incorporates price volatility in risk classifying regional food security

Consider a cooperative group of decision-makers striving to strategise against failing to attain a goal. The risk of failure depends on external factors and the decision-makers' assessment and acceptance of risk. This setting is complex due to multiple objectives and criteria, making it difficult for the individual and group of decision-maker(s) to assess the risk coherently. To aid humans in this situation formal risk frameworks such as utilities additives discriminants (UTADIS) have been developed. The contribution of this paper is to extend such framework with objective measures of volatility preserving its features. The solution is demonstrated and implemented on the problem of UN's policy-makers monitoring the sustainable development goal of food security. The solution provides both global risk scores and the utility threshold in an efficient manner such that misclassification between the decision-makers' preferred classification and their authentic classification is mimimised and obtained within one iteration.

Shannon entropy-based approach for calculating values of WABL parameters

ABSTRACT In the application phase of the fuzzy theory, it is an obvious advantage to have a valuable defuzzification. The defuzzification method that we deal with in this work is a flexible, adaptable and multi-purpose method. In this study, we will introduce a new concept to obtain the parameter values of the defuzzification method called WABL. The new concept is based on maximizing the entropy of the level sets weights of the method. We develop two versions for the concept. In the first one, we suppose that we have one decision-maker to supervise a fuzzy process. In the second version, we assume that we have a group of decision-makers to collectively administrate a fuzzy process. For each version, we construct a constrained optimization problem and we solve each problem analytically. The working results of the versions are demonstrated by numerical examples.

A generic brain connectome map linked to different types of everyday decision-making in old age.

Making reasonable decisions related to financial and health scenarios is a crucial capacity that can be difficult for older adults to maintain as they age, yet few studies examine neurocognitive factors that are generalizable to different types of everyday decision-making capacity. Here we propose an innovative approach, based on individual risk-taking preference, to identify neural profiles that may help predict older adults' everyday decision-making capacity. Using performance and cognitive arousal information from two gambling tasks, we identified three decision-making preference groups: ambiguity problem-solvers (A), risk-seekers (R), and a control group without strong risk-taking preferences (C). Comparisons of the number of connections within white matter tracts between A vs. C and R vs. C groups resulted in features consistent with the theory of dual neural functional systems involved in decision-making. Unique tracts from the A vs. C contrast were primarily centered in dorsal frontal regions/reflective system; unique tracts from the R vs. C contrast were centered in the ventral frontal regions/impulsive system; and shared tracts from both contrasts were centered in the basal ganglia, coordinating the switch between the two types of decision-making preference. Number of connections from the tracts differentiating A vs. C significantly predicted financial and health/safety decision-making capacity, and the association remained significant after controlling for multiple socioeconomic and cognitive factors. The connectome identified may provide insight into a generic white matter mechanism related to everyday decision-making capacity in older age.

Comparison of Evidential Reasoning Algorithm with Linear Combination in Decision Making

Evidential reasoning (ER) approach is a representative method for analyzing uncertain multi-criteria decision-making (MCDM) and multi-criteria group decision-making (MCGDM) problems. Its core is ER algorithm used to combine belief distributions on criteria, which is developed based on Dempster’s rule of combination and probability theory. The ER algorithm is nonlinear and more computationally complex than linear combination of belief distributions. To address the necessity of the ER algorithm in MCDM and MCGDM, it is compared with linear combination from three perspectives by simulation. The first is to examine differences between the aggregated assessments derived from the ER algorithm and linear combination. The second is to examine error rates of best alternatives derived from two combination ways. The third is to examine alternative ranking differences derived from two combination ways. To facilitate the comparison, difference between aggregated assessments is designed and score function of alternative is developed from the expected utilities of alternative. Simulation experiments show that differences between the aggregated assessments are influenced by the number of assessment grades, and error rates of best alternatives and alternative ranking differences are influenced by the numbers of criteria and alternatives.

Technical specification of expert consensus statement in developing clinical practice guidelines of traditional Chinese medicine

The formation of expert consensus statement is an indispensable part in the process of developing clinical practice guidelines. The Guidelines International Network believes that experts make group decisions for different stages and issues,and the process of gathering expert opinions is the process of reaching the consensus. GRADE system also requires that recommendations should be formed based on expert consensus in consideration of the risk and bias,patients' preferences and values,resources and other factors. At present,the main method for reaching consensus is the formal consensus method. According to the published clinical guidelines,most of them failed to report the specific methods and process of reaching expert consensus. Therefore,it is impossible to obtain an objective evaluation. This phenomenon is more common in the field of clinical practice guidelines of traditional Chinese medicine( TCM). There are two main reasons for this phenomenon. For one thing,the developers of the guidelines neglect the importance of transparency and objectivity in the implementation of expert consensus. For another,they know little about the methods and technical specifications for the formation of expert consensus. To solve them,based on the internationally recognized consensus-building methods,as well as the specific stages in the process of developing clinical practice guidelines of traditional Chinese medicine,it is of great significance to put forward the technical norms for TCM researchers to develop the expert consensus. This guide will provide detailed guidance for forming the expert consensus for TCM clinical practice guideline. This guideline has been approved and published by the Chinese Association of Traditional Chinese Medicine( No. T/CACM 1049-2017).

Gen-Y men and ‘Fast Fashion’: Who values it?

Fast fashion refers to the clothing that follows the latest fashion trends at affordable prices. Although fast fashion could cause environmental, social and ethical issues, it has been widely consumed by young consumers because of its various styles with inexpensive prices. Thus, the current study investigated how Gen-Y males’ varied decision-making styles influence their perceptions towards fast fashion differently. The results of an online survey collected from 335 Gen-Y males showed that there are two different decision-making groups: indifferent realists and conscious fashionistas, which were dissimilar in their ethnic distribution. Further, their varied decision-making dimensions influenced their perceptions towards fast fashion differently. These results contribute towards the existing literature related to fast fashion and provide insightful perceptions for fast fashion marketers in targeting Gen-Y male consumers.

Food taboos, health beliefs, and gender: understanding household food choice and nutrition in rural Tajikistan

Household nutrition is influenced by interactions between food security and local knowledge negotiated along multiple axes of power. Such processes are situated within political and economic systems from which structural inequalities are reproduced at local, national, and global scales. Health beliefs and food taboos are two manifestations that emerge within these processes that may contribute beneficial, benign, or detrimental health outcomes. This study explores the social dimensions of food taboos and health beliefs in rural Khatlon province, Tajikistan and their potential impact on household-level nutrition. Our analysis considers the current and historical and political context of Tajikistan, with particular attention directed towards evolving gender roles in the wake of mass out-migration of men from 1990 to the present. Considering the patrilieneal, patrilocal social system typical to Khatlon, focus group discussions were conducted with the primary decision-making groups of the household: in-married women, mothers-in-law, and men. During focus groups, participants discussed age- and gender-differentiated taboos that call for avoidance of several foods central to the Tajik diet during sensitive periods in the life cycle when micronutrient and energy requirements peak: infancy and early childhood (under 2 years of age), pregnancy, and lactation. Participants described dynamic and complex processes of knowledge sharing and food practices that challenge essentialist depictions of local knowledges. Our findings are useful for exploring entaglements of gender and health that play out across multiple spatial and temporal scales. While this study is situated in the context of nutrition and agriculture extension, we hope researchers and practitioners of diverse epistemologies will draw connections to diverse areas of inquiry and applications.

Large group emergency decision-making method with linguistic risk appetites based on criteria mining

In linguistic large group emergency decision-making (LGEDM), the linguistic information of large numbers of decision makers (DMs) needs to be fused to reach a collective decision. During the decision process, individual linguistic risk appetites and the provision of incomplete information under time pressure need to be considered. In this paper, we define the generalized asymmetric linguistic D number (GALDN) and propose a corresponding fusion method to effectively express and fuse the incomplete linguistic information of a large group of DMs. In this novel method, a GALDN-extended linguistic preference relation that satisfies additive consistency is designed. Moreover, the public opinions related to emergency events on social media are taken into consideration in the LGEDM. The themes of public concern are regarded as a fundamental basis for criteria selection. Finally, a case study about a fire and explosion accident is employed to justify the validity of the proposed method.

Women’s Household Decision-Making and Intimate Partner Violence in Ethiopia

Abstract Women’s household decision-making, a reflection of interpersonal power dynamics in intimate relationships is assumed to play a central role in eliminating violence against women. Thus we sought to examine the association between women’s household decision-making autonomy and the occurrence of intimate partner violence (IPV) among Ethiopian women. We used data gathered in the 2016 Ethiopia Demographic and Health Survey (EDHS-2016). We limited our study to ever-married women (aged: 15 - 49 years) who responded to the domestic violence questions (n = 4,469). Sampling weights were applied and effects associated with complex survey design were accounted for. Overall, 24%, 23.1%, and 10.1% of women have experienced emotional abuse, physical violence, or sexual violence, respectively in their lifetimes. The relationship between demographic variables and IPV were inconsistent and mostly non-significant. We found significant association between decision-making autonomy and IPV variables. Women who made decisions jointly with their husbands/partners had lower risk of domestic violence as compared to women with low level of household decision-making autonomy. No significant difference between women in the low and high level of decision-making groups. Egalitarian family power structures may be beneficial toward reducing IPV and achieving gender equality in Ethiopia.

Shared Decision-Making in Chronic Patients with Polypharmacy: An Interventional Study for Assessing Medication Appropriateness.

Potentially inappropriate medications are associated with polypharmacy and polypathology. Some interventions such as pharmacotherapy reviews have been designed to reduce the prescribing of inappropriate medications. The objective of this study is to evaluate how effective a decision-making support tool is for determining medication appropriateness in patients with one or more chronic diseases (hypertension, dyslipidaemia, and/or diabetes) and polypharmacy in the primary care setting. For this, a quasi-experimental study (randomised, controlled and multicentre) has been developed. The study compares an intervention group, which assesses medication appropriateness by applying a decision support tool, with a control group that follows the usual clinical practice. The intervention included a decision support tool in paper format, where participants were informed about polypharmacy, inappropriate medications, associated problems and available alternatives, as well as shared decision-making. This is an informative guide aimed at helping patients with decision-making by providing them with information about the secondary risks associated with inappropriate medications in their treatment, according to the Beers and START/STOPP criteria. The outcome measure was the proportion of medication appropriateness. The proportion of patients who confirmed medication appropriateness after six months of follow-up is greater in the intervention group (32.5%) than in the control group (27.9%) p = 0.008. The probability of medication appropriateness, which was calculated by the proportion of drugs withdrawn or replaced according to the STOPP/Beers criteria and those initiated according to the START criteria, was 2.8 times higher in the intervention group than in the control group (OR = 2.8; 95% CI 1.3–6.1) p = 0.008. In patients with good adherence to the treatment, the percentage of appropriateness was 62.1% in the shared decision-making group versus 37.9% in the control group (p = 0.005). The use of a decision-making support tool in patients with potentially inappropriate medications increases the percentage of medication appropriateness when compared to the usual clinical practice.

Developing GAHP concepts for measurement of travel agency organizational performance

A model for organizational performance evaluation is proposed. Organizational performance support can reduce barriers to applications of multi-criteria decision-making procedures. The relevant criteria of the proposed model were derived from an expert group interview to include the analytic hierarchy process (AHP), which is performed to determine the weights of evaluation, and grey relational analysis (GRA), which is then performed to rank the organizational performance of the travel agency. The AHP is a measurement theory that prioritizes the hierarchy and consistency of judgmental data provided by a group of decision-makers. It incorporates the evaluations of all decision-makers into a final decision through pair-wise comparisons of the alternatives, without eliciting their utility functions on subjective and objective criteria. GRA examines the extent of connections between two digits through departing and scattering measurement methods. The combined AHP and GRA decision-making method can provide decision-makers with a valuable data analysis when evaluating organizational performance. Because the proposed model can inform organizational performance assessments, it is highly applicable to both academic and commercial organizational evaluation and development.

The Social Psychology of Financial Regulatory Governance

This paper contributes to addressing a fundamental question: how do institutions, in general, and financial regulators, in particular, “think”? To this end, the analytical tools of social psychology are applied to the regulatory framework for financial services in the European Union. The paper reveals a relationship between the constitutional status of EU regulators and the dominant group dynamics typified in the literature of social psychology. Such a relationship indicates that institutional structures might favour the emergence of specific behavioural patterns and modus operandi within regulatory bodies. Furthermore, the identification of dominant group dynamics paves the way to a more profound understanding of conflictual dynamics within groups of decision-makers. Such a novel analytical map is, then, applied to the context of the ongoing debate as to whether, following Brexit, the decision-making process of EU regulators is poised to be marked by a divide separating eurozone and non-eurozone Member States.

Bayesian best-worst method: A probabilistic group decision making model

The best-worst method (BWM) is a multi-criteria decision-making method which finds the optimal weights of a set of criteria based on the preferences of only one decision-maker (DM) (or evaluator). However, it cannot amalgamate the preferences of multiple decision-makers/evaluators in the so-called group decision-making problem. A typical way of aggregating the preferences of multiple DMs is to use the average operator, e.g., arithmetic or geometric mean. However, averages are sensitive to outliers and provide restricted information regarding the overall preferences of all DMs. In this paper, a Bayesian BWM is introduced to find the aggregated final weights of criteria for a group of DMs at once. To this end, the BWM framework is meaningfully viewed from a probabilistic angle, and a Bayesian hierarchical model is tailored to compute the weights in the presence of a group of DMs. We further introduce a new ranking scheme for decision criteria, called credal ranking, where a confidence level is assigned to measure the extent to which a group of DMs prefers one criterion over one another. A weighted directed graph visualizes the credal ranking based on which the interrelation of criteria and confidences are merely understood. The numerical example validates the results obtained by the Bayesian BWM while it yields much more information in comparison to that of the original BWM.

Precision medicine for lung cancer decision-making: Evaluation of an -omics based FFT approach to personalized medicine.

e20713 Background: The prevalence of next-generation sequencing and the availability of a large number of targeted therapies in the clinics, has complicated treatment decision-making in lung cancer. While national guidelines and commercial pathways offer a method to improve the oncologists’ adherence to appropriate testing and treatment modalities available, more effort is required to solidify this as a standard of care model at academic and community sites. A better understanding of the improved durable survival of targeted therapy assignment compared with non-targeted therapy outside of the clinical trial setting is needed to understand the efficacy and accuracy of precision medicine. Methods: We perform an in-depth analysis of a series of lung AD patients (n = 798) with genomic and clinical data in a recently created thoracic patient registry, who were treated at COH between 2009-2018 period. Results: 798 individuals with lung AD were identified in the Thoracic Oncology Registry who were treated or were intended to be treated at COH; 662 (83%) of the patients had genomic testing performed at the request of their treating oncologist and 460 (58%) of whom received a 1st-line targeted therapy decision (including clinical trial assignment based on bio-marker). Oncogenic alterations were detected in 653 (82%) patients with the majority presenting with EGFR (47%), who were mostly treated with erlotinib (78%). 462/653 (70%) patients had an alteration detected with an available FDA approved therapy and 90% (416/462) of the patients were appropriately matched to a targeted therapy based on the oncologist’s decision. Several decision-making algorithms were tested and fast-and-frugal trees (FFTs) proved superior with a positive predictive value (PPV) of 90% and only required two important cues in informing the decision of the type of treatment to give to the patient. Furthermore, a targeted therapy treatment decision showed a significant benefit with a median OS of 38 months as compared to 22 months in the non-targeted therapy decision-making group (p < 0.00001). This was also evident in the PFS analysis where targeted therapy decision-making had a median survival of 9 months as compared with 5 months in the other groups (p < 0.00001). Conclusions: FFTs are a novel tool to test the efficacy of precision medicine in a real-world setting and can provide a more streamlined method for clinical guidance and decision-making. FFTs were able to predict with 90% PPV a precision medicine decision that was correlated with improved PFS (9 vs 5 months) and OS (38 vs 22 months).

Evaluation and selecting the contractor in bidding with incomplete information using MCGDM method

The main concern of construction projects is to select the right contractor in uncertain environment. According to the complex nature of today’s projects, classical approaches are not successful since they have considered only the proposed price and completion time of the project. The selection of a successful contractor depends on multi-criteria such as managerial and financial ability, technical capability, and organizational performance criteria in the presence of a decision-making group. Due to the uncertainties, ambiguities, and lack of information in this multi-criteria decision-making problem, this paper proposes an outranking method based on intuitionistic fuzzy theory. This approach involves reciprocal preference relation (RPR) and credibility function. The RPR is used to complete information, and credibility function is employed to select the best contractor. Choosing appropriate contractor to work in power plant projects is essentially owing to the fact that high-sensitive power plant construction projects are handed over to the contractor through tenders. Finally, a case study for selecting contractor in a power plant project is presented to demonstrate the effectiveness of the proposed method.

Corporate Ownership and Governance Reforms in Japan: Influence of Globalization and U.S. Practice

This paper is a brief summary and discussion on the recent reforms of Japanese corporations in regards to ownership and board structure. The paper will examine the dissolving of traditional ownership models and legal reform of board structure through reviewing its influence such as recent shareholder activism, hostile takeover, the latest corporate scandal, and further reforms in the regulative environment of Japanese corporate governance in the past few years. Above all, the paper will delve into the conflicts and tensions created by the adoption of US-style shareholder activism or board structures in the corporate world among the Japanese business and legal practices in both country and firm level. The paper provides an analysis from both legal and social perspective, with a view of company as a decision-making group, and focuses on group cohesiveness of Japanese company derived from its ownership and employment structure. The analysis of recent ownership and board reform suggests that while ownership and board structure certainly converge toward Anglo-American style through the reform, the influence of the reforms on corporate governance is still limited or varies by firms, due to the high cohesiveness and conformity of Japanese companies derived from strong ties among their constituencies.

Novel m—Polar Fuzzy Linguistic ELECTRE-I Method for Group Decision-Making

Linguistic variables play a vital role in several qualitative decision environments, in which decision-makers assume several feasible linguistic values or criteria instead of a single term for an alternative or variable. The motivation for the use of words or sentences instead of numbers is that linguistic classification and characterizations are generally less precise than numerical ones. In this research article, we encourage the fuzzy linguistic approach and introduce the novel concept known as m-polar fuzzy linguistic variable (mFLV) to increase the affluence of linguistic variables based on m-polar fuzzy (mF) approach. An mF set is an effective concept for interpreting uncertainty and fuzziness. The concept of mFLV is more versatile and sensible for dealing with real-life problems, when data comes from qualitative and multipolar information. We also introduce an mF linguistic ELECTRE-I approach to solve multiple-criteria decision-making (MCDM) and multiple-criteria group decision-making (MCGDM) problems, where the evaluation of the alternatives under suitable linguistic values are determined by the decision-makers. Furthermore, we validate the efficiency of our proposed technique by applying it to real-life examples, such as the salary analysis of companies and by selecting a corrupt country. Finally, we develop an algorithm of our proposed approach, present its flow chart, and generate computer programming code.