A Consensus Support System for Group Decision Making Problems with Heterogeneous Information
01 Jan 2008-pp 229-257
TL;DR: A consensus support system model is proposed to automate the consensus reaching process in GDM problems defined in heterogeneous contexts where the experts express their preferences by means of numerical, linguistic and interval-valued assessments.
Abstract: Summary. A group decision making (GDM) problem is a decision process where several decision makers (experts, judges, etc.) participate and try to reach a common solution. In the literature these problems have been solved carrying out a selection process that returns the solution set of alternatives from the preferences given by the experts. In order to achieve an agreement on the solution set of alternatives among the experts, it would be adequate to carry out a consensus process before the selection process. In the consensus process the experts discuss and change their preferences in order to achieve a big agreement. Due to the fact that the experts may belong to different research areas, they may express their preferences in different information domains. In this contribution we focus on the consensus process in GDM problems defined in heterogeneous contexts where the experts express their preferences by means of numerical, linguistic and interval-valued assessments. We propose a consensus support system model to automate the consensus reaching process, which provides two main advantages: (1) firstly, its ability to cope with GDM problems with heterogeneous information by means of the Fuzzy Sets Theory, and, (2) secondly, it assumes the moderator’s tasks, figure traditionally presents in the consensus reaching process.
Citations
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TL;DR: An approach to tackle multiple criteria group decision making problems in the context of interval-valued intuitionistic fuzzy sets using an optimization model to obtain criterion weights in exact numbers rather than intervals and subsequently calculates an aggregated IVIFN for each alternative.
Abstract: Research highlights? We develop an approach to tackle multiple criteria group decision making problems in the context of interval-valued intuitionistic fuzzy sets. ? An interval-valued intuitionistic fuzzy preference relation matrix is employed to determine the criterion importance with pairwise comparisons. ? Three families of parametric fuzzy unions and intersections are applied in the aggregation operation with comparisons of alternative rankings. ? The parameters of the aggregation operators have an impact on the ranks of alternatives. ? The non-parametric fuzzy operations in the aggregation operators result in a consistent ranking of alternatives. This study develops an approach to tackle multiple criteria group decision-making problems in the context of interval-valued intuitionistic fuzzy sets. Due to conflicting evaluations and insufficient information about the criteria, an interval-valued intuitionistic fuzzy preference relation matrix is employed to determine the relative importance of criteria in terms of pairwise comparisons. The decision matrix, which indicates the degree of alternatives with respect to each criterion, is expressed by interval-valued intuitionistic fuzzy numbers (IVIFNs). In order to integrate interval-valued intuitionistic fuzzy information, some special aggregation operators are created by altering the aggregation operation of IVIFNs. The three families of parametric fuzzy unions and fuzzy intersections are applied in the aggregation operation with comparisons of the ranking results of alternatives. With a linear programming method, the proposed approach uses an optimization model to obtain criterion weights in exact numbers rather than intervals, and subsequently calculates an aggregated IVIFN for each alternative. The score function and accuracy function assist in discriminating between the aggregated IVIFNs, and in generating a final rank of alternatives. Finally, an illustrative supplier selection problem is used to demonstrate how to apply the proposed approach and to observe the computational consequences resulting from various aggregation operators. The results reveal that the parameters of the aggregation operators indeed have an impact on the ranks of alternatives.
169 citations
01 Oct 2015
TL;DR: The results indicate that the position-based consensus measure is able to overcome possible distortions of the results in large-scale GDM problems and is more effective and flexible in processing heterogeneous information.
Abstract: In group decision making (GDM) problems, it is natural for decision makers (DMs) to provide different preferences and evaluations owing to varying domain knowledge and cultural values. When the number of DMs is large, a higher degree of heterogeneity is expected, and it is difficult to translate heterogeneous information into one unified preference without loss of context. In this aspect, the current GDM models face two main challenges, i.e., handling the complexity pertaining to the unification of heterogeneous information from a large number of DMs, and providing optimal solutions based on unification methods. This paper presents a new consensus-based GDM model to manage heterogeneous information. In the new GDM model, an aggregation of individual priority (AIP)-based aggregation mechanism, which is able to employ flexible methods for deriving each DM's individual priority and to avoid information loss caused by unifying heterogeneous information, is utilized to aggregate the individual preferences. To reach a consensus more efficiently, different revision schemes are employed to reward/penalize the cooperative/non-cooperative DMs, respectively. The temporary collective opinion used to guide the revision process is derived by aggregating only those non-conflicting opinions at each round of revision. In order to measure the consensus in a robust manner, a position-based dissimilarity measure is developed. Compared with the existing GDM models, the proposed GDM model is more effective and flexible in processing heterogeneous information. It can be used to handle different types of information with different degrees of granularity. Six types of information are exemplified in this paper, i.e., ordinal, interval, fuzzy number, linguistic, intuitionistic fuzzy set, and real number. The results indicate that the position-based consensus measure is able to overcome possible distortions of the results in large-scale GDM problems.
27 citations
30 Nov 2009
TL;DR: In this contribution, the effects of different aggregation operators on the consensus processes are studied: arithmetic mean, OWA with the linguistic quantifier “most” and Dependent OWA.
Abstract: Searching for consensus in group decision making is a process in which experts change their preferences in order to achieve a minimum agreement before making a decision. Computing the consensus degree among experts and the group collective opinion by aggregating experts’ opinions are two main tasks in a consensus reaching process. In this contribution we have studied the effects of different aggregation operators on the consensus processes. In particular, we have analyzed the obtained outcomes by three different aggregation operators: arithmetic mean, OWA with the linguistic quantifier “most” and Dependent OWA. Finally, some preliminary conclusions about the obtained results and the influence of these aggregation operations on consensus processes are drawn
3 citations
02 May 2011
TL;DR: This work intends to provide a new GDM framework in which the agents are able to employ abductive reasoning and discuss the options towards consensus in situations where a group of agents need to pick one of possibly many options from a set and commit to it.
Abstract: In Multiagent Systems (MAS), various activities are related to decisions involving a group of agents such as negotiation, auctions and social choice. Group Decision Making (GDM) specializes in situations where a group of agents need to pick one of possibly many options from a set and commit to it. We intend to provide a new GDM framework in which the agents are able to employ abductive reasoning and discuss the options towards consensus.
2 citations
01 Jan 2011
TL;DR: In this article, a new GDM framework is proposed in which the agents are able to employ abductive reasoning and discuss the options towards consensus, which is called abductive group decision making.
Abstract: In Multiagent Systems (MAS), various activities are related to decisions involving a group of agents such as negotiation, auctions and social choice. Group Decision Making (GDM) specializes in situations where a group of agents need to pick one of possibly many options from a set and commit to it. We intend to provide a new GDM framework in which the agents are able to employ abductive reasoning and discuss the options towards consensus.
Cites background from "A Consensus Support System for Grou..."
...More recent approaches proposed different structures to represent preferences [2, 9, 13] and to aggregate them [2, 4, 5, 7, 10]....
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...Each agent ranks the given options and provides their preference relations by attributing to each pair of alternatives either a fuzzy value, fuzzy interval or linguistic term [9]....
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References
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"A Consensus Support System for Grou..." refers background in this paper
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03 Jan 1988
TL;DR: A type of operator for aggregation called an ordered weighted aggregation (OWA) operator is introduced and its performance is found to be between those obtained using the AND operator and the OR operator.
Abstract: The author is primarily concerned with the problem of aggregating multicriteria to form an overall decision function. He introduces a type of operator for aggregation called an ordered weighted aggregation (OWA) operator and investigates the properties of this operator. The OWA's performance is found to be between those obtained using the AND operator, which requires all criteria to be satisfied, and the OR operator, which requires at least one criteria to be satisfied. >
6,534 citations
"A Consensus Support System for Grou..." refers background in this paper
...The first ones can be assessed by means of precise values like crisp values (Kacprzyk 1986; Yager 1988)....
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TL;DR: The point of this note is that fuzzy logic plays a pivotal role in CW and vice-versa and, as an approximation, fuzzy logic may be equated to CW.
Abstract: As its name suggests, computing with words (CW) is a methodology in which words are used in place of numbers for computing and reasoning. The point of this note is that fuzzy logic plays a pivotal role in CW and vice-versa. Thus, as an approximation, fuzzy logic may be equated to CW. There are two major imperatives for computing with words. First, computing with words is a necessity when the available information is too imprecise to justify the use of numbers, and second, when there is a tolerance for imprecision which can be exploited to achieve tractability, robustness, low solution cost, and better rapport with reality. Exploitation of the tolerance for imprecision is an issue of central importance in CW. In CW, a word is viewed as a label of a granule; that is, a fuzzy set of points drawn together by similarity, with the fuzzy set playing the role of a fuzzy constraint on a variable. The premises are assumed to be expressed as propositions in a natural language. In coming years, computing with words is likely to evolve into a basic methodology in its own right with wide-ranging ramifications on both basic and applied levels.
3,093 citations
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...In other cases, according to (Zadeh 1996) there is a tolerance for imprecision which can be exploited to achieve tractability, robustness, low solution cost, and better rapport with reality (e.g., when evaluating the speed of a car, linguistic terms like “fast”, “very fast”, “slow” are used instead…...
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01 Jan 1988
3,030 citations