Quantum mechanics

Scale (ratio)

Process (computing)

Engineering

Group cohesiveness

Social psychology

Operating system

Computer science

Risk analysis (engineering)

Business

Cohesion (chemistry)

Physics

Operations research

Organic chemistry

Psychology

Political science

Group decision-making

Data science

Artificial intelligence

Medicine

Chemistry

Programming language

Knowledge management

Consensus algorithm

Knowledge-Based Systems

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.

A large scale consensus reaching process managing group hesitation