Professor Enrique Herrera-Viedma

Job: Professor of Soft Computing and Intelligent Information Systems

Faculty: Computing, Engineering and Media

School/department: School of Computer Science and Informatics

Address: De Montfort University, The Gateway, Leicester, LE1 9BH

T: N/A

E: enrique.herrera-viedma@dmu.ac.uk

W: www.dmu.ac.uk/

 

Research group affiliations

  1. DIGITS – De Montfort University Interdisciplinary Group in Intelligent Transport Systems
  2. Centre for Computational Intelligence

Publications and outputs

  • A bilateral negotiation mechanism by dynamic harmony threshold for group consensus decision making
    dc.title: A bilateral negotiation mechanism by dynamic harmony threshold for group consensus decision making dc.contributor.author: Cao, Mingshuo; Chiclana, Francisco; Liu, Yujia; Wu, Jian; Herrera-Viedma, Enrique dc.description.abstract: This article proposes a framework for bilateral negotiation mechanism to deal the case the concordant decision-makers (DMs) coalition cannot be constructed, which resolves the limitations of the existing group decision making methods. Bilateral negotiation means a process in which any two involved DMs change their own opinions based on each other’s opinions, avoiding the formation of group coalitions and the coercion of individual DMs. It can not only improve group consensus by interaction between individual DMs, but also considers the limited compromise behavior of DMs in the consensus bargaining process. The key contributions of this article contain: (1) It investigates the concept of ‘harmony threshold’ by combining the consensus levels of individual DMs and the number of group members to explain the limited compromise behavior of DMs. (2) it proposes a novel bilateral negotiation consensus mechanism with personalized compromise behavior with the group consensus threshold as the objective function and personalized harmony thresholds as constraints to help any two discording DMs partly to adopt each other’s opinions. And (3) It develops the ranking difference level (RDL) to measure the deviation degree between the final ranking of alternatives and all the DMs’ original rankings of alternatives. The research found that the proposed mechanism can reduce consensus cost by 40% and ranking difference by 5%. dc.description: 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.
  • Evolution of Credit Scores of Enterprises in a Social Network: A Perspective Based on Opinion Dynamics
    dc.title: Evolution of Credit Scores of Enterprises in a Social Network: A Perspective Based on Opinion Dynamics dc.contributor.author: Liang, Haiming; Xu, Weijun; Chiclana, Francisco; Yu, Shui; Dong, Yucheng; Herrera-Viedma, Enrique dc.description.abstract: The use of social network to model the evolution of credit scores of networked enterprises is still a challenging task. This article develops an opinion dynamics model of the evolution of credit scores of enterprises in a social network. Firstly, based on the number of potential cooperated enterprises and the initial credit scores, the leader and follower enterprises are identified. Then, taking into consideration the cooperated benefit and discrimination cost, the cooperated utility between any two enterprises is calculated, which is used to compute the weights that one enterprise assigns to other enterprises. An opinion dynamics model on the evolution of credit scores of enterprises, inspired on the classical Friedkin–Johnsen’s social network model, is developed. Some desirable properties of the proposed opinion dynamics model are theoretically stated and proved. Finally, a numerical example is provided to illustrate the feasibility of the proposed opinion dynamics model, while a simulation analysis to investigate the joint influences of the connection probabilities and the network structure on the evolution of credit scores of enterprises is reported. dc.description: 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.
  • Ordering vs. AHP. Does the intensity used in the decision support techniques compensate?
    dc.title: Ordering vs. AHP. Does the intensity used in the decision support techniques compensate? dc.contributor.author: Sáenz-Royo, Carlos; Chiclana, Francisco; Herrera-Viedma, Enrique dc.description.abstract: The manifestation of the intensity in the judgment of one alternative versus another in the peer comparison processes is a central element in some decision support techniques, such as the Analytical Hierarchy Process (AHP). However, his contribution in terms of quality (expected performance) with respect to the priority vector has not been evaluated so far. In this work, through the Intentional Bounded Rationality Methodology (IBRM) of Sáenz-Royo, Chiclana, and Herrera-Viedma (2023), the gains obtained from requiring the decision-maker to report an intensity judgment in pairs (AHP) are analyzed with respect to a technique that only requires expressing a preference (Ordering). The results show that when decision-makers have low levels of expertise, it is possible that a less informative and expensive technique (Ordering) performs better than a more informative and expensive one (AHP). When decision-makers have medium and high levels of expertise, AHP obtains meager gains about Ordering. This study proposes a cost-benefit analysis of decision support techniques contrasting the gains of a technique that requires more (AHP) resources with other less expensive (Ordering). Our results can change the way of managing the information obtained from experts’ judgments. dc.description: open access article
  • Steering committee management. Expertise, diversity, and decision-making structures
    dc.title: Steering committee management. Expertise, diversity, and decision-making structures dc.contributor.author: Sáenz Royo, Carlos; Chiclana, Francisco; Herrera-Viedma, Enrique dc.description.abstract: This paper proposes to analyze how the differences in expertise, diversity, and group decision procedures affect the quality of the strategic decision of steering committees. Strategic decisions are difficult to anticipate, and performances of the alternatives are often not observable in their entirety, which prevents researchers from obtaining controlled empirical studies. This paper proposes to analyze the performance of steering committees where managers can err in their decisions using the Intentional Bounded Rationality (IBR). The majority procedure improves the committee's performance concerning authority when the level of diversity and expertise increases. However, in situations of low expertise, the gains over authority narrow. This work provides guidance in terms of trade-offs between the mentality of managers, their expertise, group decision procedures, and diversity, which in the empirical works are contradictory. This study contributes to current theorizations of committee management using the IBR methodology, which is new and allows quantifying the contribution of the distinct characteristics of the committee. dc.description: open access article
  • A Local Adjustment Method to Improve Multiplicative Consistency of Fuzzy Reciprocal Preference Relations
    dc.title: A Local Adjustment Method to Improve Multiplicative Consistency of Fuzzy Reciprocal Preference Relations dc.contributor.author: Xu, Yejun; Wang, Qianqian; Chiclana, Francisco; Herrera-Viedma, Enrique dc.description.abstract: Preferences that verify the transitivity property are usually referred to as rational or consistent preferences. Existent methods to improve the consistency of inconsistent fuzzy reciprocal preference relations (FPRs) fail to retain the original preference values because they always derive a new FPR. This article presents a new inconsistency identification and modification (IIM) method to detect and rectify only the most inconsistent elements of an inconsistent FPR. As such, the proposed IIM can be considered a local adjustment method to improve multiplicative consistency (MC) of FPRs. The case of inconsistent FPRs with missing values, i.e., incomplete FPRs, is addressed with the estimation of the missing preferences with a constrained nonlinear optimization model by the application of the IIM method. The implementation process of the proposed algorithms is illustrated with numerical examples. Simulation experiments and comparisons with existent methods are also included to show that the new method requires fewer iterations than existent methods to improve the MC of FPRs and achieves better MC level, while preserving the original preference information as much as possible than the existent methods. Thus, the results presented in this article demonstrate the correctness, effectiveness, and robustness of the proposed method. dc.description: 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.
  • Multi-objective optimization-based collective opinion generation with fairness concern
    dc.title: Multi-objective optimization-based collective opinion generation with fairness concern dc.contributor.author: Chen, Zhen-Song; Zhu, Zhengze; Herrera-Viedma, Enrique; Wang, Xian-Jia; Chiclana, Francisco; Skibniewski, Mirosław J. dc.description.abstract: The generation of collective opinion based on probability distribution function (PDF) aggregation is gradually becoming a critical approach for tackling immense and delicate assessment and evaluation tasks in decision analysis. However, the existing collective opinion generation approaches fail to model the behavioral characteristics associated with individuals, and thus, cannot reflect the fairness concerns among them when they consciously or unconsciously incorporate their judgments on the fairness level of distribution into the formulations of individual opinions. In this study, we propose a multiobjective optimization-driven collective opinion generation approach that generalizes the bi-objective optimization-based PDF aggregation paradigm. In doing so, we adapt the notion of fairness concern utility function to characterize the influence of fairness inclusion and take its maximization as an additional objective, together with the criteria of consensus and confidence levels, to achieve in generating collective opinion. The formulation of fairness concern is then transformed into the congregation of individual fairness concern utilities in the use of aggregation functions. We regard the generalized extended Bonferroni mean (BM) as an elaborated framework for aggregating individual fairness concern utilities. In such way, we establish the concept of BM-type collective fairness concern utility to empower multiobjective optimization-driven collective opinion generation approach with the capacity of modeling different structures associated with the expert group with fairness concern. The application of the proposed fairness-aware framework in the maturity assessment of building information modeling demonstrates the effectiveness and efficiency of multiobjective optimization-driven approach for generating collective opinion when accomplishing complicated assessment and evaluation tasks with data scarcity. dc.description: open access article
  • Weight Penalty Mechanism for Noncooperative Behavior in Large-Scale Group Decision Making With Unbalanced Linguistic Term Sets
    dc.title: Weight Penalty Mechanism for Noncooperative Behavior in Large-Scale Group Decision Making With Unbalanced Linguistic Term Sets dc.contributor.author: Sun, Qi; Chiclana, Francisco; Wu, Jian; Liu, Yujia; Liang, Changyong; Herrera-Viedma, Enrique dc.description.abstract: This paper proposes a novel framework to manage subgroups’ non-cooperative behavior by weight penalty in large scale group decision making (LSGDM). To do that, a trust consensus index (TCI) is defined by combining trust score and consensus degree among experts expressed by unbalanced linguistic term sets. A Louvain algorithm clustering process based on undirected graph composed of TCI is introduced to detect the subgroups in large network. Hence, a weight penalty feedback model is established to manage the subgroups detected as discordant and non-cooperative. The proposed method novelty resides in that the minimum adjustment cost can be obtained with respect to the penalty parameter. A detail analysis regarding the computation of the optimal penalty parameter to prevent excessive penalization is reported. Finally, a detailed numerical and comparative analyses are provided to verify the validity of the proposed method. dc.description: 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.
  • The overlapping community driven feedback mechanism to support consensus in social network group decision making
    dc.title: The overlapping community driven feedback mechanism to support consensus in social network group decision making dc.contributor.author: Ji, Feixia; Wu, Jian; Chiclana, Francisco; Wang, Sha; Fujita, Hamido; Herrera-Viedma, Enrique dc.description.abstract: Abstract—Social network group decision-making (SN-GDM) provides valuable support for obtaining agreed decision results by effectively utilizing the connected social trust relationships among individuals. However, the impact of intricately overlapped social trust relationships within overlapping communities on evaluation modifications in the SN-GDM consensus reaching process is seldom considered. To alleviate this issue, this study attempts to construct an overlapping community driven feedback mechanism for improving consensus in SN-GDM. The Lancichinetti-Fortunato method (LFM) is used to detect the overlapping community structures under social trust networks. Subsequently, the trusted recommendation advice is conducted within overlapping communities, which guides the inconsistent subgroups to make an interaction with each other to reach higher consensus level. Then, an associated feedback mechanism for SNGDM with overlapping communities is proposed, which enables the inconsistent subgroups to minimize the consensus cost by selecting personalized feedback parameters. Moreover, it shows that the overlapping communities based feedback mechanism is superior to the feedback mechanisms with non-overlapping communities. Finally, an illustrative example is included, which is also used to testify the efficacy of proposal by comparing the consensus cost under different representative recommendation advice in overlapping social trust networks. dc.description: 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
  • A bargaining game based feedback mechanism to support consensus in dynamic social network group decision making
    dc.title: A bargaining game based feedback mechanism to support consensus in dynamic social network group decision making dc.contributor.author: Xing, Yumei; Wu, Jian; Chiclana, Francisco; Yu, Gaofeng; Cao, Mingshuo; Herrera-Viedma, Enrique dc.description.abstract: A bargaining game is used to develop feedback mechanism for dynamic social networks group decision making (SN-GDM). The dynamic trust relationships between experts are updated by the change of their consensus state after each round of interaction. Then, a maximum entropy model based on individual interactive relationship and fairness is established to determine the comprehensive weight of each expert, which considers: (1) the individual weight by influence of expert; (2) the interaction weight by social relationships of experts. Hence, 2-tuple linguistic collective evaluation matrix of the 2-additive Choquet integral under Möbius transform is put forward. Further, the equilibrium solution of two experts in the bargaining game is established, and then this equilibrium recommendation will be accepted by both experts. Consequently, a bargaining game based feedback mechanism driven by trust relationship is proposed to reflect the interaction behaviors between the inconsistent expert and her/his most trusted consistent one, and therefore the recommendation advices are generated for them to promote consensus in SN-GDM. Finally, a sustainable supplier selection example demonstrates the effectiveness of the proposed approach. dc.description: 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
  • An approach to prevent weight manipulation by minimum adjustment and maximum entropy method in social network group decision making
    dc.title: An approach to prevent weight manipulation by minimum adjustment and maximum entropy method in social network group decision making dc.contributor.author: Sun, Qi; Wu, Jian; Chiclana, Francisco; Wang, Sha; Herrera-Viedma, Enrique; Yager, Ronald R. dc.description.abstract: In social network group decision making (SN-GDM) problem, subgroup weights are mostly unknown, many approaches have been proposed to determine the subgroup weights. However, most of these methods ignore the weight manipulation behavior of subgroups. Some studies indicated that weight manipulation behavior hinders consensus efficiency. To deal with this issue, this paper proposes a theoretical framework to prevent weight manipulation in SN-GDM. Firstly, a community detection based method is used to cluster the large group. The power relations of subgroups are measured by the power index (PI), which depends on the subgroups size and cohesion. Then, a minimum adjustment feedback model with maximum entropy is proposed to prevent subgroups’ manipulation behavior. The minimum adjustment rule aims for ‘efficiency’ while the maximum entropy rule aims for ‘justice’. The experimental results show that the proposed model can guarantee the rationality of weight distribution to reach consensus efficiently, which is achieved by maintaining a balance between ‘efficiency’ and ‘justice’ in the mechanism of assigning weights. Finally, the detailed numerical and simulation analyses are carried out to verify the validity of the proposed method. dc.description: 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

Click here to view a full listing of Enrique Herrera-Viedma's publications and outputs.

Research interests/expertise

  • Fuzzy preference modelling
  • Decision making problems with heterogeneous fuzzy information
  • Decision support systems, the consensus reaching process
  • Recommender systems, social networks
  • Modelling situations with missing/incomplete information
  • Rationality/consistency
  • Intelligent mobility and aggregation of information
  • Information retrieval
  • Bibliometrics analysis
  • Digital libraries.

Honours and awards

  • Best conference paper Awards:

S. Alonso, I.J. Pérez, E. Herrera-Viedma, F.J. Cabrerizo, Consensus with Linguistic Preferences in Web 2.0 Communities. 9th International Conference on Intelligent Systems Design and Applications (ISDA09), Pisa (Italy), 809-814, November 30 - December 2, 2009.

  • 2011 IEEE CIS TFS Outstanding Paper Award

F. Herrera, E. Herrera-Viedma, L. Martínez. A Fuzzy Linguistic Methodology To Deal With Unbalanced Linguistic Term Sets. IEEE Transactions on Fuzzy Systems 16:2 (2008) 354-370.

Membership of professional associations and societies

  •     Member of the European Society for Fuzzy Logic and Technology (EUSFLAT)
  •     IEEE member from 2012.

Conference attendance

PC membership

IFSA2009 / EUSFLAT09
FLINS2010 
FCTA 2013
AGOP2013
IFSA-NAFIPS 2013
ISDA'09 
ISKE2009 
ISKE2012
ITQM 2013
Multiconference CAEPIA 2013
UKCI2013
ICNC-FSKD 2013 

  • 28th International Conference on Artificial Intelligence. Application Stream (2008)
  • IEEE International Conference on Fuzzy Systems (2008)
  • IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (2009, 2011, 2013)
  • International Conference on Electronic Commerce and Web Technologies (2008)
  • Fuzzy Logic and Intelligent Technologies in Nuclear Science FLINS (2008, 2010)
  • IEEE Globecom Computer and Communications Network Security Symposium (2008,2009)
  • UK Workshop on Computational Intelligence (2008)
  • SGAI International Conference on Artificial Intelligence (2008)
  • EUROFUSE Workshop on Preference Modelling and Applications (2009, 2011)
  • Global Congress on Intelligent Systems (2009, 2010)
  • International Conference on Intelligent Systems and Knowledge Engineering (2009,2011)
  • International Conference on Fuzzy Computation (2009)
  • International Conference on Fuzzy Systems and Knowledge Discovery (2010)
  • International Symposium on Intelligent Decision Technologies (2010)
  • 2nd KES International Symposium on Intelligent Decision Technologies IDT'10 (2010)
  • 2011 International Conference onIntelligent Systems and Knowledge Engineering (2011)
  • 2009 International Fuzzy Systems Association WORLD CONGRESS (2009)
  • 2009 European Society for Fuzzy Logic and Technology CONFERENCE   (2009)
  • International Conference on Intelligent Systems Design and Applications (ISDA’09).

Externally funded research grants information

Intelligent systems for decisión making in heterogeneous contexts
Reference: Andalusian Excellence Project.TIC-5991
Period: November 2010 – June 2014

FUZZY-LING II: Fuzzy linguistic modelling of preferences: Applications in Information Retrieval and Group Decision Making.
Reference: Education Ministery. TIN2010-17876
Period: January 2011 – December 2013

Developing the fuzzy linguistic modelling and its use in web applications.
Reference: TIC-05299
Period: November 2009 – June 2013

FUZZY-LING: New fuzzy linguistic approaches for preference modelling:Applications on Information Retrieval and Decision Making
Project: Education Ministery. TIN2007-61079
Period: June 2007 - June 2011

Professional esteem indicators

Associate Editor

  • KNOWLEDGE BASED SYSTEMS
    From 2010
  • INFORMATION SCIENCES
    From 2010
  • SOFT COMPUTING 
    From 2012
  • JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 
    From 2012
  • FUZZY OPTIMIZATION & DECISION MAKING
    From 2013
  • IEEE Transactions on Systems, Man, and Cybernetics: Systems
    From 2013

Member of the Editorial Board:

  • FUZZY SETS AND SYSTEMS
    From 2007
  • SOFT COMPUTING 
    From 2007
  • INT. J. OF INFORMATION TECHNOLOGY AND DECISION MAKING
    From 2008
  • INT. J. OF COMPUTATIONAL INTELLIGENCE SYSTEMS
    From 2010

Journal Refereeing information

  • Annals of Operations Research
  • Applied Mathematical Modelling
  • Computers & Industrial Engineering
  • Data & Knowledge Engineering (DKE) Journal
  • European Journal of Operational Research
  • Fuzzy Sets and Systems
  • Group Decision and Negotiation
  • IEEE Transactions on Fuzzy Systems
  • IEEE Transactions on Industrial Informatics
  • IEEE Transactions on Systems, Man and Cybernetics, Part A
  • IEEE Transactions on Systems, Man and Cybernetics, Part B
  • IEEE Transactions on Systems, Man and Cybernetics, Part C
  • IMA Journal of Management Mathematics
  • Information Fusion
  • Information Sciences
  • International Journal of Approximate Reasoning
  • International Journal Artificial Intelligence in Medicine
  • International Journal of Computational Intelligence Research
  • International Journal of Information Technology & Decision Making
  • Journal of Intelligent and Fuzzy Systems
  • International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems(IJUFKS)
  • Journal of Environmental Management
  • Journal of Intelligent and Fuzzy Systems
  • Journal of Multiple-Valued Logic and Soft Computing
  • Journal of Operational Research Society
  • Journal of Systems Science and Systems Engineering
  • Knowledge-Based Systems 
  • Omega
  • Soft Computing
  • The Open Cybernetics and Systemics Journal.

Other Reviewing Activities

  • Switzerland National Council for Development and Innovation (2012)
  • Portuguese Foundation for Science and Technology (FCT) (2012 and 2013).