Study of Manhattan’s consensus degrees through an extension based on the Uniform distribution

The authors would like to acknowledge the Spanish State Research Agency support under the Project PID2019-103880RB-I00/ AEI/10.13039/501100011033.An important aspect to be considered in Group Decision Making problems is the study of consensus. Since in these problems it is desirable that the final decision is widely accepted, improving the consensus degree in a fair way is a very interesting task. This paper analyses the improvement in the consensus degrees -obtained by applying Manhattan distance-when the experts' preferences are slightly modified using one of the properties of the Uniform distribution. We carry out an experimental study that shows the enhancement in different cases to which Uniform extension has been applied, with different number of both, experts and alternatives.Spanish Government PID2019-103880RB-I00/ AEI/10.13039/50110001103

Distance-based consensus models for fuzzy and multiplicative 3 preference relations

This paper proposes a distance-based consensus model for fuzzy preference relations where the weights of fuzzy preference relations are automatically determined. Two indices, an individual to group consensus index (ICI) and a group consensus index (GCI), are introduced. An iterative consensus reaching algorithm is presented and the process terminates until both the ICI and GCI are controlled within predefined thresholds. The model and algorithm are then extended to handle multiplicative preference relations. Finally, two examples are illustrated and comparative analyses demonstrate the effectiveness of the proposed methods

Expertons and uncertain averaging operators versus correlational approaches: A case study on corporate social responsibility and effectiveness

The purpose of this paper is to explore the relationship between corporate social responsibility (CSR), work-life balance (WLB) and effectiveness by comparing a correlational approach, expertons method and uncertain averaging operators (uncertain average [UA], uncertain weighted average [UWA], uncertain probabilistic aggregation [UPA] and uncertain probabilistic weighted averaging [UPWA])

A fuzzy hierarchical multiple criteria group decision support system - Decider - and its applications

Decider is a Fuzzy Hierarchical Multiple Criteria Group Decision Support System (FHMC-GDSS) designed for dealing with subjective, in particular linguistic, information and objective information simultaneously to support group decision making particularly on evaluation. In this chapter, the fuzzy aggregation decision model, functions and structure of Decider are introduced. The ideas to resolve decision and evaluation problems we have faced in the development and application of Decider are presented. Two real applications of the Decider system are briefly illustrated. Finally, we discuss our further research in this area. © 2011 Springer-Verlag Berlin Heidelberg

A new decision making model based on Rank Centrality for GDM with fuzzy preference relations

The work of Enrique Herrera Viedma was supported by the Spanish State Research Agency under Project PID2019-103880RB-I00/AEI/10.13039/501100011033.Preference aggregation in Group Decision Making (GDM) is a substantial problem that has received a lot of research attention. Decision problems involving fuzzy preference relations constitute an important class within GDM. Legacy approaches dealing with the latter type of problems can be classified into indirect approaches, which involve deriving a group preference matrix as an intermediate step, and direct approaches, which deduce a group preference ranking based on individual preference rankings. Although the work on indirect approaches has been extensive in the literature, there is still a scarcity of research dealing with the direct approaches. In this paper we present a direct approach towards aggregating several fuzzy preference relations on a set of alternatives into a single weighted ranking of the alternatives. By mapping the pairwise preferences into transitions probabilities, we are able to derive a preference ranking from the stationary distribution of a stochastic matrix. Interestingly, the ranking of the alternatives obtained with our method corresponds to the optimizer of the Maximum Likelihood Estimation of a particular Bradley-Terry-Luce model. Furthermore, we perform a theoretical sensitivity analysis of the proposed method supported by experimental results and illustrate our approach towards GDM with a concrete numerical example. This work opens avenues for solving GDM problems using elements of probability theory, and thus, provides a sound theoretical fundament as well as plausible statistical interpretation for the aggregation of expert opinions in GDM.Spanish State Research Agency PID2019-103880RB-I00/AEI/10.13039/50110001103

An analysis of consensus approaches based on different concepts of coincidence

The file attached to this record is the author's final peer reviewed version.Soft consensus is a relevant topic in group decision making problems. Soft consensus measures are utilized to reflect the different agreement degrees between the experts leading the consensus reaching process. This may determine the final decision and the time needed to reach it. The concept of coincidence has led to two main approaches to calculating the soft consensus measures, namely, concordance among expert preferences and concordance among individual solutions. In the first approach the coincidence is obtained by evaluating the similarity among the expert preferences, while in the second one the concordance is derived from the measurement of the similarity among the solutions proposed by these experts. This paper performs a comparative study of consensus approaches based on both coincidence approaches. We obtain significant differences between both approaches by comparing several distance functions for measuring expert preferences and a consensus measure over the set of alternatives for measuring the solutions provided by experts. To do so, we use the nonparametric Wilcoxon signed-ranks test. Finally, these outcomes are analyzed using Friedman mean ranks in order to obtain a quantitative classification of the considered measurements according to the convergence criterion considered in the consensus reaching process

A dynamic feedback mechanism with attitudinal consensus threshold for minimum adjustment cost in group decision making

This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 71971135, Grant 71571166, Grant 72071056, and Grant 71910107002, in part by the Innovative Talent Training Project of Graduate Students in Shanghai Maritime University of China under Grant 2019YBR017, and in part by the Spanish State Research Agency under Project PID2019-103880RB-I00/AEI/10.13039/501100011033.This article presents a theoretical framework for a dynamic feedback mechanism in group decision making (GDM) by the implementation of an attitudinal consensus threshold (ACT) to generate recommendation advice for the identified inconsistent experts with the aim to increase consensus. The novelty of the approach resides in its ability to implement the ACT continuously, which allows the covering of all possible consensus states of the group from its minimum to maximum consensus degrees. Therefore, it can be flexibly applied to GDM problems with different consistency requirements. A sensitivity analysis method with visual simulation is proposed to support the checking of the numbers of experts involved in the feedback process and the minimum adjustment cost associated with the different ACT intervals. Experimental results show that an increase in the ACT value will lead to an increase in the number of experts and adjustment cost involved in the feedback process. Eventually, a numerical example is included to simulate the feedback process under various decision making scenarios with different ACT intervals.National Natural Science Foundation of China (NSFC) 71971135 71571166 72071056 71910107002Innovative Talent Training Project of Graduate Students in Shanghai Maritime University of China 2019YBR017Spanish Government PID2019-103880RB-I00/AEI/10.13039/50110001103

Web 2.0-based Collaborative Multicriteria Spatial Decision Support System: A Case Study of Human-Computer Interaction Patterns

The integration of GIS and Multicriteria Decision Analysis (MCDA) capabilities into the Web 2.0 platform offers an effective Multicriteria Spatial Decision Support System (MC-SDSS) with which to involve the public, or a particular group of individuals, in collaborative spatial decision making. Understanding how decision makers acquire and integrate decision-related information within the Web 2.0-based collaborative MC-SDSS has been one of the major concerns of MC-SDSS designers for a long time. This study focuses on examining human-computer interaction patterns (information acquisition behavior) within the Web 2.0-based MC-SDSS environment. It reports the results of an experimental study that investigated the effects of task complexity, information aids, and decision modes on information acquisition metrics and their relations. The research involved three major steps: (1) developing a Web 2.0-based analytic-deliberative MC-SDSS for parking site selection in Tehran, Iran to analyze human-computer interaction patterns, (2) conducting experiments using this system and collecting the human-computer interaction data, and (3) analyzing the log data to detect the human-computer interaction patterns (information acquisition metrics). Using task complexity, decision aid, and decision mode as the independent factors, and the information acquisition metrics as the dependent variables, the study adopted a repeated-measures experimental design (or within-subjects design) to test the relevant hypotheses. Task complexity was manipulated in terms of the number of alternatives and attributes at four levels. At each level of task complexity, the participants carried out the decision making process in two different GIS-MCDA modes: individual and group modes. The decision information was conveyed to participants through common map and decision table information structures. The map and table were used, respectively, for the exploration of the geographic (or decision) and criterion outcome spaces. The study employed a process-tracing method to directly monitor and record the decision makers’ activities during the experiments. The data on the decision makers’ activities were recorded as Web-based event logs using a database logging technique. Concerningiv task complexity effects, the results of the study suggest that an increase in task complexity results in a decrease in the proportion of information searched and proportion of attribute ranges searched, as well as an increase in the variability of information searched per attribute. This finding implies that as task complexity increases decision makers use a more non-compensatory strategy. Regarding the decision mode effects, it was found that the two decision modes are significantly different in terms of: (1) the proportion of information search, (2) the proportion of attribute ranges examined, (3) the variability of information search per attribute, (4) the total time spent acquiring the information in the decision table, and (5) the average time spent acquiring each piece of information. Regarding the effect of the information aids (map and decision table) on the information acquisition behavior, the findings suggest that, in both of the decision modes, there is a significant difference between information acquisition using the map and decision table. The results show that decision participants have a higher number of moves and spend more time on the decision table than map. The study presented in this dissertation has implications for formulating behavioral theories in the spatial decision context and practical implications for the development of MC-SDSS. Specifically, the findings provide a new perspective on the use of decision support aids, and important clues for designers to develop an appropriate user-centered Web-based collaborative MC-SDSS. The study’s implications can advance public participatory planning and allow for more informed and democratic land-use allocation decisions

Access network selection schemes for multiple calls in next generation wireless networks

There is an increasing demand for internet services by mobile subscribers over the wireless access networks, with limited radio resources and capacity constraints. A viable solution to this capacity crunch is the deployment of heterogeneous networks. However, in this wireless environment, the choice of the most appropriate Radio Access Technology (RAT) that can Tsustain or meet the quality of service (QoS) requirements of users' applications require careful planning and cost efficient radio resource management methods. Previous research works on access network selection have focused on selecting a suitable RAT for a user's single call request. With the present request for multiple calls over wireless access networks, where each call has different QoS requirements and the available networks exhibit dynamic channel conditions, the choice of a suitable RAT capable of providing the "Always Best Connected" (ABC) experience for the user becomes a challenge. In this thesis, the problem of selecting the suitable RAT that is capable of meeting the QoS requirements for multiple call requests by mobile users in access networks is investigated. In addressing this problem, we proposed the use of Complex PRoprtional ASsesment (COPRAS) and Consensus-based Multi-Attribute Group Decision Making (MAGDM) techniques as novel and viable RAT selection methods for a grouped-multiple call. The performance of the proposed COPRAS multi-attribute decision making approach to RAT selection for a grouped-call has been evaluated through simulations in different network scenarios. The results show that the COPRAS method, which is simple and flexible, is more efficient in the selection of appropriate RAT for group multiple calls. The COPRAS method reduces handoff frequency and is computationally inexpensive when compared with other methods such as the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Simple Additive Weighting (SAW) and Multiplicative Exponent Weighting (MEW). The application of the proposed consensus-based algorithm in the selection of a suitable RAT for group-multiple calls, comprising of voice, video-streaming, and file-downloading has been intensively investigated. This algorithm aggregates the QoS requirement of the individual application into a collective QoS for the group calls. This new and novel approach to RAT selection for a grouped-call measures and compares the consensus degree of the collective solution and individual solution against a predefined threshold value. Using the methods of coincidence among preferences and coincidence among solutions with a predefined consensus threshold of 0.9, we evaluated the performance of the consensus-based RAT selection scheme through simulations under different network scenarios. The obtained results show that both methods of coincidences have the capability to select the most suitable RAT for a group of multiple calls. However, the method of coincidence among solutions achieves better results in terms of accuracy, it is less complex and the number of iteration before achieving the predefined consensus threshold is reduced. A utility-based RAT selection method for parallel traffic-streaming in an overlapped heterogeneous wireless network has also been developed. The RAT selection method was modeled with constraints on terminal battery power, service cost and network congestion to select a specified number of RATs that optimizes the terminal interface utility. The results obtained show an optimum RAT selection strategy that maximizes the terminal utility and selects the best RAT combinations for user's parallel-streaming for voice, video and file-download