A personalized consensus feedback mechanism based on maximum harmony degree
Identificadores
URI: http://hdl.handle.net/10481/71035Metadatos
Mostrar el registro completo del ítemEditorial
IEEE
Materia
Group decision making Consensus Personalized feedback mechanism Harmony degree
Fecha
2019-06Referencia bibliográfica
Published version: M. Cao... [et al.]. "A Personalized Consensus Feedback Mechanism Based on Maximum Harmony Degree," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 10, pp. 6134-6146, Oct. 2021, doi: [10.1109/TSMC.2019.2960052]
Patrocinador
National Natural Science Foundation of China (NSFC) 71971135 71571166; European Commission H2020-MSCA-IF-2016-DeciTrustNET-746398 TIN2016-75850-RResumen
This article proposes a framework of personalized
feedback mechanism to help multiple inconsistent experts to
reach consensus in group decision making by allowing to select
different feedback parameters according to individual consensus
degree. The general harmony degree (GHD) is defined
to determine the before/after feedback difference between the
original and revised opinions. It is proved that the GHD index is
monotonically decreasing with respect to the feedback parameter,
which means that higher parameters values will result in higher
changes of opinions. An optimisation model is built with the
GHD as the objective function and the consensus thresholds as
constraints, with solution being personalized feedback advices to
the inconsistent experts that keep a balance between consensus
(group aim) and independence (individual aim). This approach
is, therefore, more reasonable than the unpersonalized feedback
mechanisms in which the inconsistent experts are forced to
adopt feedback generated with only consensus target without
considering the extent of the changes acceptable by individual
experts. Furthermore, the following interesting theoretical results
are also proved: (1) the personalized feedback mechanism
guarantees that the increase of consensus level after feedback
advices are implemented; (2) the GHD by the personalized
feedback mechanism is higher than that of the unpersonalized
one; and (3) the personalized feedback mechanism generalises
the unpersonalized one as it is proved the latter is a particular
type of the former. Finally, a numerical example is provided to
model the feedback process and to corroborates these results
when comparing both feedback mechanism approaches.