Special Issue "Group Decision Making Based on Artificial Intelligence"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Set Theory".

Deadline for manuscript submissions: closed (31 December 2020).

Special Issue Editors

Dr. Francisco Javier Cabrerizo-Lorite
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Guest Editor
Department of Computer Science and Artificial Intelligence University of Granada 18071, Granada, Spain
Interests: group decision making; consensus; fuzzy logic; linguistic modeling
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Prof. Dr. Ignacio Javier Perez Galvez
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Guest Editor
Department of Computer Sciences and Engineering, University of Cádiz, Puerto Real, 11519, Spain
Interests: group decision making; consensus; fuzzy logic; linguistic modeling
Prof. Dr. Jose María Merigo
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Guest Editor
School of Information, Systems and Modelling, University of Technology Sydney, Australia
Interests: computational intelligence; decision making; aggregation operators; business intelligence; bibliometrics; knowledge management
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Prof. Dr. Enrique Herrera-Viedma
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Guest Editor
Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain
Interests: intelligent decision-making; group decision-making; consensus models; fuzzy linguistic modeling; aggregation of information; information retrieval; recommender systems
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In the scope of decision support systems, which were at first designed as individual tools, the problem of group decision making has gained great importance. Individual tools have been quickly demonstrated to be limited, in the sense that in today’s organizations several decision makers are involved in most of the decision processes. To deal with such decision problems, a promising approach is to unify group decision support systems and artificial intelligence (AI). AI attempts to mimic human decision making in some capacity, and advances in AI have shown important promise in improving and assisting human decision making, in particular in real-time and complex environments. When AI techniques are used, the resulting systems are referred to as intelligent group decision support systems.

The purpose of this Special Issue is to gather a collection of articles reflecting the latest developments in the design of intelligent group decision support systems that use AI techniques, such as machine learning, Bayesian networks, neural networks, fuzzy logic, and others, to improve and enhance support for decision makers in solving difficult applied problems that involve large amounts of data, are often real-time, and benefit from complex reasoning.

Prof. Francisco Javier Cabrerizo
Prof. Ignacio Javier Pérez
Prof. Dr. Jose Maria Merigo
Dr. Enrique Herrera-Viedma
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Group decision making
  • Fuzzy group decision making
  • Soft consensus
  • Intelligent decision making based on computational intelligence
  • Aggregation operators
  • Social group decision making
  • Large-scale group decision making
  • Dynamic decision making
  • Intelligent decision making and applications.

Published Papers (9 papers)

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Research

Article
A Methodology for Redesigning Networks by Using Markov Random Fields
Mathematics 2021, 9(12), 1389; https://doi.org/10.3390/math9121389 - 15 Jun 2021
Viewed by 464
Abstract
Standard methodologies for redesigning physical networks rely on Geographic Information Systems (GIS), which strongly depend on local demographic specifications. The absence of a universal definition of demography makes its use for cross-border purposes much more difficult. This paper presents a Decision Making Model [...] Read more.
Standard methodologies for redesigning physical networks rely on Geographic Information Systems (GIS), which strongly depend on local demographic specifications. The absence of a universal definition of demography makes its use for cross-border purposes much more difficult. This paper presents a Decision Making Model (DMM) for redesigning networks that works without geographical constraints. There are multiple advantages of this approach: on one hand, it can be used in any country of the world; on the other hand, the absence of geographical constraints widens the application scope of our approach, meaning that it can be successfully implemented either in physical (ATM networks) or non-physical networks such as in group decision making, social networks, e-commerce, e-governance and all fields in which user groups make decisions collectively. Case studies involving both types of situations are conducted in order to illustrate the methodology. The model has been designed under a data reduction strategy in order to improve application performance. Full article
(This article belongs to the Special Issue Group Decision Making Based on Artificial Intelligence)
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Article
Assisting Users in Decisions Using Fuzzy Ontologies: Application in the Wine Market
Mathematics 2020, 8(10), 1724; https://doi.org/10.3390/math8101724 - 07 Oct 2020
Viewed by 459
Abstract
Nowadays, wine has become a very popular item to purchase. There are a lot of brands and a lot of different types of wines that have different prices and characteristics. Since there is a lot of options, it is easy for buyers to [...] Read more.
Nowadays, wine has become a very popular item to purchase. There are a lot of brands and a lot of different types of wines that have different prices and characteristics. Since there is a lot of options, it is easy for buyers to feel lost among the high number of possibilities. Therefore, there is a need for computational tools that help buyers to decide which is the wine that better fits their necessities. In this article, a decision support system built over a fuzzy ontology has been designed for helping people to select a wine. Two different possible architecture implementation designs are presented. Furthermore, imprecise information is used to design a comfortable way of providing information to the system. Users can use this comfortable communication system to express their preferences and provide their opinion about the selected products. Moreover, mechanisms to carry out a constant update of the fuzzy ontology are exposed. Full article
(This article belongs to the Special Issue Group Decision Making Based on Artificial Intelligence)
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Article
Group Decision-Making Based on Artificial Intelligence: A Bibliometric Analysis
Mathematics 2020, 8(9), 1566; https://doi.org/10.3390/math8091566 - 11 Sep 2020
Cited by 2 | Viewed by 846
Abstract
Decisions concerning crucial and complicated problems are seldom made by a single person. Instead, they require the cooperation of a group of experts in which each participant has their own individual opinions, motivations, background, and interests regarding the existing alternatives. In the last [...] Read more.
Decisions concerning crucial and complicated problems are seldom made by a single person. Instead, they require the cooperation of a group of experts in which each participant has their own individual opinions, motivations, background, and interests regarding the existing alternatives. In the last 30 years, much research has been undertaken to provide automated assistance to reach a consensual solution supported by most of the group members. Artificial intelligence techniques are commonly applied to tackle critical group decision-making difficulties. For instance, experts’ preferences are often vague and imprecise; hence, their opinions are combined using fuzzy linguistic approaches. This paper reports a bibliometric analysis of the ample literature published in this regard. In particular, our analysis: (i) shows the impact and upswing publication trend on this topic; (ii) identifies the most productive authors, institutions, and countries; (iii) discusses authors’ and journals’ productivity patterns; and (iv) recognizes the most relevant research topics and how the interest on them has evolved over the years. Full article
(This article belongs to the Special Issue Group Decision Making Based on Artificial Intelligence)
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Article
Bonferroni Probabilistic Ordered Weighted Averaging Operators Applied to Agricultural Commodities’ Price Analysis
Mathematics 2020, 8(8), 1350; https://doi.org/10.3390/math8081350 - 12 Aug 2020
Cited by 4 | Viewed by 809
Abstract
Financial markets have been characterized in recent years by their uncertainty and volatility. The price of assets is always changing so that the decisions made by consumers, producers, and governments about different products is not still accurate. In this situation, it is necessary [...] Read more.
Financial markets have been characterized in recent years by their uncertainty and volatility. The price of assets is always changing so that the decisions made by consumers, producers, and governments about different products is not still accurate. In this situation, it is necessary to generate models that allow the incorporation of the knowledge and expectations of the markets and thus include in the results obtained not only the historical information, but also the present and future information. The present article introduces a new extension of the ordered weighted averaging (OWA) operator called the Bonferroni probabilistic ordered weighted average (B-POWA) operator. This operator is designed to unify in a single formulation the interrelation of the values given in a data set by the Bonferroni means and a weighted and probabilistic vector that models the attitudinal character, expectations, and knowledge of the decision-maker of a problem. The paper also studies the main characteristics and some families of the B-POWA operator. An illustrative example is also proposed to analyze the mathematical process of the operator. Finally, an application to corn price estimation designed to calculate the error between the price of an agricultural commodity using the B-POWA operator and a leading global market company is presented. The results show that the proposed operator exhibits a better general performance than the traditional methods. Full article
(This article belongs to the Special Issue Group Decision Making Based on Artificial Intelligence)
Article
AHP-Like Matrices and Structures—Absolute and Relative Preferences
Mathematics 2020, 8(5), 813; https://doi.org/10.3390/math8050813 - 18 May 2020
Cited by 2 | Viewed by 876
Abstract
Aggregation functions are extensively used in decision making processes to combine available information. Arithmetic mean and weighted mean are some of the most used ones. In order to use a weighted mean, we need to define its weights. The Analytical Hierarchy Process (AHP) [...] Read more.
Aggregation functions are extensively used in decision making processes to combine available information. Arithmetic mean and weighted mean are some of the most used ones. In order to use a weighted mean, we need to define its weights. The Analytical Hierarchy Process (AHP) is a well known technique used to obtain weights based on interviews with experts. From the interviews we define a matrix of pairwise comparisons of the importance of the weights. We call these AHP-like matrices absolute preferences of weights. We propose another type of matrix that we call a relative preference matrix. We define this matrix with the same goal—to find the weights for weighted aggregators. We discuss how it can be used for eliciting the weights for the weighted mean and define a similar approach for the Choquet integral. Full article
(This article belongs to the Special Issue Group Decision Making Based on Artificial Intelligence)
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Article
Using Different Qualitative Scales in a Multi-Criteria Decision-Making Procedure
Mathematics 2020, 8(3), 458; https://doi.org/10.3390/math8030458 - 24 Mar 2020
Cited by 4 | Viewed by 878
Abstract
Many decision problems manage linguistic information assessed through several ordered qualitative scales. In these contexts, the main problem arising is how to aggregate this qualitative information. In this paper, we present a multi-criteria decision-making procedure that ranks a set of alternatives assessed by [...] Read more.
Many decision problems manage linguistic information assessed through several ordered qualitative scales. In these contexts, the main problem arising is how to aggregate this qualitative information. In this paper, we present a multi-criteria decision-making procedure that ranks a set of alternatives assessed by means of a specific ordered qualitative scale for each criterion. These ordered qualitative scales can be non-uniform and be formed by a different number of linguistic terms. The proposed procedure follows an ordinal approach by means of the notion of ordinal proximity measure that assigns an ordinal degree of proximity to each pair of linguistic terms of the qualitative scales. To manage the ordinal degree of proximity from different ordered qualitative scales, we provide a homogenization process. We also introduce a stochastic approach to assess the robustness of the conclusions. Full article
(This article belongs to the Special Issue Group Decision Making Based on Artificial Intelligence)
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Article
Decomposition and Arrow-Like Aggregation of Fuzzy Preferences
Mathematics 2020, 8(3), 436; https://doi.org/10.3390/math8030436 - 17 Mar 2020
Cited by 2 | Viewed by 766
Abstract
We analyze the concept of a fuzzy preference on a set of alternatives, and how it can be decomposed in a triplet of new fuzzy binary relations that represent strict preference, weak preference and indifference. In this setting, we analyze the problem of [...] Read more.
We analyze the concept of a fuzzy preference on a set of alternatives, and how it can be decomposed in a triplet of new fuzzy binary relations that represent strict preference, weak preference and indifference. In this setting, we analyze the problem of aggregation of individual fuzzy preferences in a society into a global one that represents the whole society and accomplishes a shortlist of common-sense properties in the spirit of the Arrovian model for crisp preferences. We introduce a new technique that allows us to control a fuzzy preference by means of five crisp binary relations. This leads to an Arrovian impossibility theorem in this particular fuzzy setting. Full article
(This article belongs to the Special Issue Group Decision Making Based on Artificial Intelligence)
Article
A Probabilistic Linguistic Multiple Attribute Decision Making Based on a New Correlation Coefficient Method and its Application in Hospital Assessment
Mathematics 2020, 8(3), 340; https://doi.org/10.3390/math8030340 - 04 Mar 2020
Cited by 13 | Viewed by 899
Abstract
The probabilistic linguistic term set (PLTS) is a newly emerging mathematical tool for handling uncertainties. It is considered a useful extension of linguistic term sets associated with probability information and can improve the effectiveness of multiple attribute decision making (MADM). This paper proposes [...] Read more.
The probabilistic linguistic term set (PLTS) is a newly emerging mathematical tool for handling uncertainties. It is considered a useful extension of linguistic term sets associated with probability information and can improve the effectiveness of multiple attribute decision making (MADM). This paper proposes a new PLTS correlation coefficient and addresses its usefulness in MADM problems. For achieving this aim, some new concepts of mean, variance, and covariance of the PLTS are first proposed. Moreover, a novel PLTS Pearson correlation coefficient is defined to overcome the shortcomings of the existing methods, whose significant feature is that it lies in the interval [−1,1], which makes it more effective in reflecting the negative and positive correlation between PLTSs. A weighted PLTS Pearson correlation coefficient is further defined to consider the importance of attribute weights and expand the scope of application. Then, a relative PLTS closeness coefficient is constructed based on the developed Pearson correlation coefficient, and based on which, a Pearson correlation-based TOPSIS (technique for order of preference by similarity to ideal solution) approach for MADM problems is developed. Finally, the effectiveness as well as the applicability of the developed method are illustrated through numerical examples and comparative analysis. Full article
(This article belongs to the Special Issue Group Decision Making Based on Artificial Intelligence)
Article
Novel Multiple Attribute Group Decision-Making Methods Based on Linguistic Intuitionistic Fuzzy Information
Mathematics 2020, 8(3), 322; https://doi.org/10.3390/math8030322 - 02 Mar 2020
Cited by 8 | Viewed by 677
Abstract
As an effective technique to qualitatively depict assessment information, a linguistic intuitionistic fuzzy number (LIFN) is more appropriate to portray vagueness and indeterminacy in actual situations than intuitionistic fuzzy number (IFN). The prominent feature of a Muirhead mean (MM) operator is that it [...] Read more.
As an effective technique to qualitatively depict assessment information, a linguistic intuitionistic fuzzy number (LIFN) is more appropriate to portray vagueness and indeterminacy in actual situations than intuitionistic fuzzy number (IFN). The prominent feature of a Muirhead mean (MM) operator is that it has the powerful ability to capture the correlations between any input-data and MM operator covers other common operators by assigning the different parameter vectors. In the article, we first analyze the limitations of the existing ranking approaches of LIFN and propose a novel ranking approach to surmount these limitations. Secondly, we propound several novel MM operators to fuse linguistic intuitionistic fuzzy (LIF) information, such as the LIF Muirhead mean (LIFMM) operator, the weighted LIF Muirhead mean (WLIFMM) operator and their dual operators, the LIFDMM operator and the WLIFDMM operator. Subsequently, we discuss several desirable properties along with exceptional cases of them. Moreover, two novel multiple attribute group decision-making approaches are developed based upon these operators. Ultimately, the effectuality and practicability of the propounded methods are validated through dealing with a global supplier selection issue, and the comparative analysis and the merits of the presented approaches are demonstrated by comparing them with existing approaches. Full article
(This article belongs to the Special Issue Group Decision Making Based on Artificial Intelligence)
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