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A large scale consensus reaching process managing group hesitation

(2018) KNOWLEDGE-BASED SYSTEMS. 159. p.86-97
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Abstract
Nowadays due to the social networks and the technological development, large-scale group decision making (LS-GDM) problems are fairly common and decisions that may affect to lots of people or even the society are better accepted and more appreciated if they agreed. For this reason, consensus reaching processes (CRPs) have attracted researchers attention. Although, CRPs have been usually applied to GDM problems with a few experts, they are even more important for LS-GDM, because differences among a big number of experts are higher and achieving agreed solutions is much more complex. Therefore, it is necessary to face some challenges in LS-GDM. This paper presents a new adaptive CRP model to deal with LS-GDM which includes: (i) a clustering process to weight experts' sub-groups taking into account their size and cohesion, (ii) it uses hesitant fuzzy sets to fuse expert's sub-group preferences to keep as much information as possible and (iii) it defines an adaptive feedback process that generates advice depending on the consensus level achieved to reduce the time and supervision costs of the CRP. Additionally, the proposed model is implemented and integrated in an intelligent CRP support system, so-called AFRYCA 2.0 to carry out this new CRP on a case study and compare it with existing models.
Keywords
Software, Information Systems and Management, Management Information Systems, Artificial Intelligence, Large-scale group decision making, Consensus reaching process, Clustering, Hesitant fuzzy sets, Sub-group weight, Intelligent consensus reaching process support system, GROUP DECISION-MAKING, FUZZY PREFERENCE RELATIONS, ANALYSIS FRAMEWORK, MODEL, INFORMATION, SETS, CLUSTERS, INITIALIZATION, ENVIRONMENT, RANKING

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Please use this url to cite or link to this publication:

MLA
Rodríguez, Rosa M., et al. “A Large Scale Consensus Reaching Process Managing Group Hesitation.” KNOWLEDGE-BASED SYSTEMS, vol. 159, 2018, pp. 86–97.
APA
Rodríguez, R. M., Labella, Á., De Tré, G., & Martínez, L. (2018). A large scale consensus reaching process managing group hesitation. KNOWLEDGE-BASED SYSTEMS, 159, 86–97.
Chicago author-date
Rodríguez, Rosa M., Álvaro Labella, Guy De Tré, and Luis Martínez. 2018. “A Large Scale Consensus Reaching Process Managing Group Hesitation.” KNOWLEDGE-BASED SYSTEMS 159: 86–97.
Chicago author-date (all authors)
Rodríguez, Rosa M., Álvaro Labella, Guy De Tré, and Luis Martínez. 2018. “A Large Scale Consensus Reaching Process Managing Group Hesitation.” KNOWLEDGE-BASED SYSTEMS 159: 86–97.
Vancouver
1.
Rodríguez RM, Labella Á, De Tré G, Martínez L. A large scale consensus reaching process managing group hesitation. KNOWLEDGE-BASED SYSTEMS. 2018;159:86–97.
IEEE
[1]
R. M. Rodríguez, Á. Labella, G. De Tré, and L. Martínez, “A large scale consensus reaching process managing group hesitation,” KNOWLEDGE-BASED SYSTEMS, vol. 159, pp. 86–97, 2018.
@article{8653221,
  abstract     = {Nowadays due to the social networks and the technological development, large-scale group decision making (LS-GDM) problems are fairly common and decisions that may affect to lots of people or even the society are better accepted and more appreciated if they agreed. For this reason, consensus reaching processes (CRPs) have attracted researchers attention. Although, CRPs have been usually applied to GDM problems with a few experts, they are even more important for LS-GDM, because differences among a big number of experts are higher and achieving agreed solutions is much more complex. Therefore, it is necessary to face some challenges in LS-GDM. This paper presents a new adaptive CRP model to deal with LS-GDM which includes: (i) a clustering process to weight experts' sub-groups taking into account their size and cohesion, (ii) it uses hesitant fuzzy sets to fuse expert's sub-group preferences to keep as much information as possible and (iii) it defines an adaptive feedback process that generates advice depending on the consensus level achieved to reduce the time and supervision costs of the CRP. Additionally, the proposed model is implemented and integrated in an intelligent CRP support system, so-called AFRYCA 2.0 to carry out this new CRP on a case study and compare it with existing models.},
  author       = {Rodríguez, Rosa M. and Labella, Álvaro and De Tré, Guy and Martínez, Luis},
  issn         = {0950-7051},
  journal      = {KNOWLEDGE-BASED SYSTEMS},
  keywords     = {Software,Information Systems and Management,Management Information Systems,Artificial Intelligence,Large-scale group decision making,Consensus reaching process,Clustering,Hesitant fuzzy sets,Sub-group weight,Intelligent consensus reaching process support system,GROUP DECISION-MAKING,FUZZY PREFERENCE RELATIONS,ANALYSIS FRAMEWORK,MODEL,INFORMATION,SETS,CLUSTERS,INITIALIZATION,ENVIRONMENT,RANKING},
  language     = {eng},
  pages        = {86--97},
  title        = {A large scale consensus reaching process managing group hesitation},
  url          = {http://dx.doi.org/10.1016/j.knosys.2018.06.009},
  volume       = {159},
  year         = {2018},
}

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