Generalizing and Integrating TOPSIS and Cook-Seiford Method for Multicriteria Group Decision-Making with Both Cardinal and Ordinal Data

The TOPSIS and Cook-Seiford social choice function are generalized and integrated for multicriteria group decision-making (MCGDM) with both cardinal evaluations and ordinal preferences of the alternatives. Unlike traditional TOPSIS, at first, the group’s positive ideal solution and negative ideal solution under cardinal and ordinal preferences are defined, respectively. Thus the group rankings of the alternatives with respect to each criterion are derived from the individual preferences by the modified group TOPSIS considering the weights of decision makers under each criterion. Then the weighted distance function representing the total inconsistency between the comprehensive rankings of all alternatives and the ones under all criteria is presented after the criteria weights are taken into account. Form the perspective of minimizing the criteria-weighted distance of the rankings, a nonlinear integer programming is developed and transformed into an assignment problem to obtain the final rankings of all alternatives. An illustrative case is presented and some comparisons on the results show that the developed approach is practical and effective. This study extends TOPSIS to group decision-making with ordinal preferences and generalizes Cook-Seiford social choice function to multicriteria decision-making considering the criteria weights and can be a novel benchmark for MCGDM with both cardinal and ordinal data

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

Multiple Criteria Decision Making and Multiattribute Utility Theory

T his paper is an update of a paper that five of us published in 1992. The areas of multiple criteria decision making (MCDM) and multiattribute utility theory (MAUT) continue to be active areas of management science research and application. This paper extends the history of these areas and discusses topics we believe to be important for the future of these fields

Modeling Influence In Group Decision Making

Group decision making has been widely studied since group decision making processes are very common in many fields. Formal representation of the experts’ opinions, aggregation of assessments or selection of the best alternatives have been some of main areas addressed by scientists and researchers. In this paper, we focus on another promising area, the study of group decision making processes from the concept of influence and social networks. In order to do so, we present a novel model that gathers the experts’ initial opinions and provides a framework to represent the influence of a given expert over the other(s). With this proposal it is feasible to estimate both the evolution of the group decision making process and the final solution before carrying out the group discussion process and consequently foreseeing possible actions

Intentional bounded rationality methodology to assess the quality of decision-making approaches with latent alternative performances

Expert’s judgments have been crucial in the development of decision theory; however, what criterion to use in the selection of experts remains an issue to address. Decision support techniques proposed to improve the quality of expert judgment decision making consider a demonstrated inconsistency of the judgments expressed by an expert as a criterion of exclusion in the decision-making process of such expert. Although consistency appears to be a desirable condition to qualify as “expert”, little is known about the quality of the decisions made imposing consistency as the expert qualifying condition. This paper proposes a simulation methodology, based on an automaton programmed to make decisions in an intended but bounded rational way, to assess the cost-benefit of different aspects of decision support techniques. Within this methodology, the imposition of the consistency condition in the selection of experts is studied. In particular, the paper shows with a case study example that the Analytical hierarchy process (AHP) decision support technique expected payoff is at most 5% higher when implementing Saaty’s consistency criterion of the expert’s judgments than when the consistency criterion is not considered.Spanish Government ECO2017-86305-C4-3-RGobierno de AragonEuropean Social Fund (ESF)Spanish Government PID2019-103880RB-I00/AEI/10.13039/50110001103

Managing Non-Homogeneous Information and Experts’ Psychological Behavior in Group Emergency Decision Making

After an emergency event (EE) happens, emergency decision making (EDM) is a common and effective way to deal with the emergency situation, which plays an important role in mitigating its level of harm. In the real world, it is a big challenge for an individual emergency manager (EM) to make a proper and comprehensive decision for coping with an EE. Consequently, many practical EDM problems drive group emergency decision making (GEDM) problems whose main limitations are related to the lack of flexibility in knowledge elicitation, disagreements in the group and the consideration of experts’ psychological behavior in the decision process. Hence, this paper proposes a novel GEDM approach that allows more flexibility for preference elicitation under uncertainty, provides a consensus process to avoid disagreements and considers experts’ psychological behavior by using the fuzzy TODIM method based on prospect theory. Eventually, a group decision support system (GDSS) is developed to support the whole GEDM process defined in the proposed method demonstrating its novelty, validity and feasibility.This work was partly supported by the Young Doctoral Dissertation Project of Social Science Planning Project of Fujian Province (Project No. FJ2016C202), National Natural Science Foundation of China (Project Nos. 71371053, 61773123), Spanish National Research Project (Project No. TIN2015-66524-P), and Spanish Ministry of Economy and Finance Postdoctoral Fellow (IJCI-2015-23715) and ERDF

A trust induced recommendation mechanism for reaching consensus in group decision making

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This article addresses the inconsistency problem in group decision making caused by disparate opinions of multiple experts. To do so, a trust induced recommendation mechanism is investigated to generate personalised advices for the inconsistent experts to reach higher consensus level. The concept of trust degree (TD) is defined to identify the trusted opinion from group experts, and then the visual trust relationship is built to help experts ‘see’ their own trust preferences within the group. Consequently, trust based personalised advices are generated for the inconsistent experts to revisit their opinions. To model the uncertainty of experts, an interval-valued trust decision making space is defined. It includes the novel concepts of interval-valued trust functions, interval-valued trust score (IVTS) and interval-valued knowledge degree (IVKD). The concepts of consensus degree (CD) between an expert and the rest of experts in the group as well as the harmony degree (HD) between the original opinion and the revised opinion are developed for interval-valued trust functions. Combining HD and CD, a more reasonable policy for group consensus is proposed as it should arrive at the threshold value with the maximum value of harmony and consensus degrees simultaneously. Furthermore, because the trust induced recommendation mechanism focuses on changing inconsistent opinions using only opinions from the trusted experts and not from the distrusted ones, the HD based changes cost to reach the threshold value of consensus is lower than previous mechanisms based on the average of the opinion of all experts. Finally, once consensus has been achieved, a ranking order relation for interval-valued trust functions is constructed to select the most appropriate alternative

Human factors aspects of control room design: Guidelines and annotated bibliography

A human factors analysis of the workstation design for the Earth Radiation Budget Satellite mission operation room is discussed. The relevance of anthropometry, design rules, environmental design goals, and the social-psychological environment are discussed

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