Multiobjective Optimization-Based Collective Opinion Generation With Fairness Concern
Metadata
Show full item recordEditorial
IEEE
Materia
Collective opinion Generation Fairness concern Multiobjective optimization Probability Distribution function (PDF)
Date
2023-05-17Referencia bibliográfica
Z. -S. Chen, Z. Zhu, X. -J. Wang, F. Chiclana, E. Herrera-Viedma and M. J. Skibniewski, "Multiobjective Optimization-Based Collective Opinion Generation With Fairness Concern," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, [doi: 10.1109/TSMC.2023.3273715.]
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 BMtype
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.