Enrique Herrera Viedma - Selected Publications#

[1] C.C., Li, Y. Dong, F. Herrera, E. Herrera-Viedma, and L. Martínez. Personalized individual semantics in Computing with Words for supporting linguistic Group Decision Making. An Application on Consensus reaching. Information Fusion 33:1 (2017) 29-40.

Significance: In this paper the first personalized individual semantics (PIS) model is proposed to personalize individual semantics by means of an interval numerical scale and the 2-tuple linguistic model. Specifically, a consistency-driven optimization-based model to obtain and represent the PIS is introduced. A new fuzzy linguistic framework based on the 2-tuple linguistic model is then defined, such a framework allows us to deal with PIS to facilitate on keeping the idea that words mean different things to different people. In order to justify the feasibility and validity of the PIS model, it is applied to solve linguistic group decision making problems with a consensus reaching process.

Impact: The journal is a Top Q1 journal in Computer Science and AI with IF(2020)= 12.975 (ranked 3/139) . According to Clarivate Analytics Web of Science Core Collection, as of 26 March 2022, this paper received 254 citations and it is a Highly Cited Paper in the field of "Computer Science”.

[2] J. Wu, L Dai, F. Chiclana, H. Fujita, E. Herrera-Viedma. A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust. Information Fusion, 41 (2018) 232-242.

Significance: The first theoretical feedback mechanism framework to model consensus in social network group decision making (SN-GDM) is proposed with following two main components: (1) the modelling of trust relationship with linguistic information; and (2) the minimum adjustment cost feedback mechanism. To do so, a distributed linguistic trust decision making space is defined, which includes the novel concepts of distributed linguistic trust functions, expectation degree, uncertainty degrees and ranking method. Then, a social network analysis (SNA) methodology is developed to represent and model trust relationship between a networked group, and the trust in-degree centrality indexes are calculated to assign an importance degree to the associated user. To identify the inconsistent users, three levels of consensus degree with distributed linguistic trust functions are calculated. Then, a novel feedback mechanism is activated to generate recommendation advices for the inconsistent users to increase the group consensus degree. Its novelty is that it produces the boundary feedback parameter based on the minimum adjustment cost optimisation model. Therefore, the inconsistent users are able to reach the threshold value of group consensus incurring a minimum modification of their opinions or adjustment cost, which provides the optimum balance between group consensus and individual independence. Finally, after consensus has been achieved, a ranking order relation for distributed linguistic trust functions is constructed to select the most appropriate alternative of consensus.

Impact: The journal is a Top Q1 journal in Computer Science and AI with IF(2020)= 12.975 (ranked 3/139) . According to Clarivate Analytics Web of Science Core Collection, as of 26 March 2022, this paper received 174 citations and it is a Highly Cited Paper in the field of "Computer Science”.

[3] H. Zhang, Y. Dong, and E. Herrera-Viedma. Consensus building for the heterogeneous large-scale gdm with the individual concerns and satisfactions. IEEE Trans. On Fuzzy Systems, 26(2):884-898, 2018.

Significance: This study proposes the first novel consensus reaching model for the heterogeneous large-scale group decision making (GDM) with the individual concerns and satisfactions. In this consensus reaching model, a selection process is proposed to obtain the individual preference vectors, to divide decision makers into different clusters, and to yield the preference vector of the large group. Following this, a consensus measure method that considers the individual concerns on alternatives is defined for measuring the consensus degree, and a linguistic approach is developed to measure the individual and collective satisfactions regarding the consensus degree. Finally, a feedback adjustment process is proposed and utilized to help decision makers adjust their preferences.

Impact: The journal is a Top Q1 journal in Computer Science and AI with IF (2020)=12.029 (ranked 4/139) . According to Clarivate Analytics Web of Science Core Collection, as of 26 March 2022, this paper received 319 citations and it is a Highly Cited Paper in the field of “Engineering”.

[4] Nicola Capuano, F. Chiclana, H. Fujita, E. Herrera-Viedma, Vincenzo Loia. Fuzzy Group Decision Making with Incomplete Information Guided by Social Influence. IEEE Trans. On Fuzzy Systems. Volume: 26, Issue: 3, June 2018, pp. 1704-1718.

Significance: In this papers we propose a new influence-guided group decision making (GDM) model based on the following assumptions: experts influence each other and the more an expert trusts in another expert, the more his opinion is influenced by that expert. The effects of social influence are especially relevant to cases when, due to domain complexity, limited expertise or pressure to make a decision, an expert is unable to express preferences on some alternatives, i.e., in presence of incomplete information. The proposed model adopts fuzzy rankings to collect both experts' preferences on available alternatives and trust statements on other experts. Starting from collected information, possibly incomplete, the configuration and the strengths of interpersonal influences are evaluated and represented through a social influence network (SIN). The SIN, in its turn, is used to estimate missing preferences and evolve them by simulating the effects of experts' interpersonal influence before aggregating them for the selection of the best alternative.

Impact: The journal is a Top Q1 journal in Computer Science and AI with IF (2020)=12.029 (ranked 4/139) . According to Clarivate Analytics Web of Science Core Collection, as of 26 March 2022, this paper received 195 citations and it is a Highly Cited Paper in the field of “Engineering”.

[5] Yucheng Dong, Sihai Zhao, Hengjie Zhang, Francisco Chiclana, and E. Herrera-Viedma. A self-management mechanism for non-cooperative behaviors in large-scale group consensus reaching processes. IEEE Trans. On Fuzzy Systems, Volume: 26 , Issue: 6 , Dec. 2018, pp. 3276 – 3288.

Significance: In this paper, we propose a novel framework based on a self-management mechanism for noncooperative behaviors in large-scale CRPs (LCRPs). In the proposed consensus reaching framework, experts are classified into different subgroups using a clustering method, and experts provide their evaluation information, i.e., the multicriteria mutual evaluation matrices (MCMEMs), regarding the subgroups based on subgroups' performance (e.g., professional skills, cooperation, and fairness). The subgroups' weights are dynamically generated from the MCMEMs, which are in turn employed to update the individual experts' weights. This self-management mechanism in the LCRP allows penalizing the weights of the experts with noncooperative behaviors.

Impact: The journal is a Top Q1 journal in Computer Science and AI with IF (2020)=12.029 (ranked 4/139) . According to Clarivate Analytics Web of Science Core Collection, as of 26 March 2022, this paper received 145 citations and it is a Highly Cited Paper in the field of “Engineering”.

[6] Y. Liu, Y. Dong, H.Liang, F. Chiclana, E. Herrera-Viedma. Multiple attribute strategic weight manipulation with minimum cost in a group decision making context with interval attribute weights information. IEEE Trans. Syst. Man and Cyb: Systems, (2019) 49 Número: 10 Páginas: 1981-1992

Significance: In this paper, we study the strategic weight manipulation in a group decision making (GDM) context with interval attribute weight information. In G,DM, the revision of the decision makers' original attribute weight information implies a cost. Driven by a desire to minimize the cost, we propose the minimum cost strategic weight manipulation model, which is achieved via optimization approach, with the mixed 0-1 linear programming model being proved appropriate in this context. Meanwhile, some desired properties to manipulate a strategic attribute weight based on the ranking range under interval attribute weight information are propixsed.

Impact: The journal is a Top Q1 journal in Computer Science-Cybernetics with IF=13.451 (ranked 1/23). According to Clarivate Analytics Web of Science Core Collection, as of 26 March 2022, this paper received 73 citations and it is a Highly Cited Paper in the field of “Engineering”.

[7] Quanbo Zha, Y Dong, H Zhang, F Chiclana, and E Herrera-Viedma. A Personalized Feedback Mechanism Based on Bounded Confidence Learning to Support Consensus Reaching in Group Decision Making. IEEE Trans. Syst. Man and Cyb: Systems, (2021) 51:6, Pages 3900-3910.

Significance: In this article we propose the first consensus reaching model with a personalized feedback mechanism to help decision makers with bounded confidences in achieving consensus. Specifically, the personalized feedback mechanism produces more acceptable advices in the two cases where bounded confidences are known or unknown, and the unknown ones are estimated by a learning algorithm.

Impact: The journal is a Top Q1 journal in Computer Science-Cybernetics with IF=13.451 (ranked 1/23). According to Clarivate Analytics Web of Science Core Collection, as of 26 March 2022, this paper received 37 citations and it is a Highly Cited Paper in the field of “Engineering”.

[8] Y. Xu, M. Li, F. J. Cabrerizo, F. Chiclana and E. Herrera-Viedma. Algorithms to Detect and Rectify Multiplicative and Ordinal Inconsistencies of Fuzzy Preference Relations. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(6), pp. 3498-3511, June 2021.

Impact: The journal is a Top Q1 journal in Computer Science-Cybernetics with IF=13.451 (ranked 1/23). According to Clarivate Analytics Web of Science Core Collection, as of 26 March 2022, this paper received 261 citations and it is a Highly Cited Paper in the field of “Engineering”.

[9] J. Wu, M.S. Cao, F. Chiclana, E. Herrera-Viedma. An optimal feedback model to prevent manipulation behaviour in consensus under social network group decision making. IEEE Trans. on Fuzzy Systems, 2021, Volume29, Issue7, Page1750-1763.

Significance: In this article, a novel framework to prevent manipulation behavior in consensus reaching process under social network group decision making is proposed, which is based on a theoretically sound optimal feedback model. The manipulation behavior classification is twofold: first, "individual manipulation" where each expert manipulates his/her own behavior to achieve higher importance degree (weight); and second, "group manipulation" where a group of experts force inconsistent experts to adopt specific recommendation advices obtained via the use of a fixed feedback parameter. To counteract "individual manipulation," a behavioral weights assignment method modeling sequential attitude ranging from "dictatorship" to "democracy" is developed, and then a reasonable policy for group minimum adjustment cost is established to assign appropriate weights to experts. To prevent "group manipulation," an optimal feedback model is investigated where objective function is the individual adjustments cost and constraints related to the group threshold of consensus. This approach allows the inconsistent experts to balance group consensus and adjustment cost, which enhances their willingness to adopt the recommendation advices and consequently the group reaching consensus on the decision-making problem at hand.

Impact: The journal is a Top Q1 journal in Computer Science and AI with IF (2020)=12.029 (ranked 4/139) . According to Clarivate Analytics Web of Science Core Collection, as of 26 March 2022, this paper received 33 citations and it is a Highly Cited Paper in the field of “Engineering”.

[10] Ignacio Javier Perez; Francisco Javier Cabrerizo; Sergio Alonso; E. Herrera-Viedma. A new consensus model for group decision making problems with non-homogeneous experts IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS Volumen: 44 Número: 4 Páginas: 494-498 Fecha de publicación: APR 2014.

Significance: In this paper we present a new consensus model based on the heterogeneity existing among experts to guide the consensus process. The main goal of this paper is to present a new consensus model for heterogeneous group decision making problems guided also by the heterogeneity criterion. It is also based on consensus degrees and similarity measures, but it presents a new feedback mechanism that adjusts the amount of advice required by each expert depending on his/her own relevance or importance level.

Impact: This paper received The 2016 IEEE Systems, Man, and Cybernetics (SMC) Society Andrew P. Sage Best Transactions Paper Award. The journal is a Top Q1 journal in Computer Science-Cybernetics with IF=13.451 (ranked 1/23). According to Clarivate Analytics Web of Science Core Collection, as of 26 March 2022, this paper received 261 citations and it is a Highly Cited Paper in the field of “Engineering”.