Multiobjective Optimization-Based Collective Opinion Generation With Fairness Concern

The generation of collective opinion based on probability distribution function (PDF) aggregation is gradually becoming a critical approach for tackling immense and delicate assessment evaluation tasks in decision analysis. However, the existing approaches fail to model behavioral characteristics associated with individuals, thus, cannot reflect fairness concerns among them when they consciously or unconsciously incorporate their judgments level into formulations individual opinions. In this study, we propose multiobjective optimization-driven that generalizes bi-objective optimization-based PDF paradigm. doing so, adapt notion concern utility characterize influence inclusion take its maximization as an additional objective, together criteria consensus confidence levels, achieve generating opinion. formulation then transformed congregation utilities use functions. We regard generalized extended Bonferroni mean (BM) elaborated framework aggregating utilities. such way, establish concept BM-type empower capacity modeling different structures expert group concern. application proposed fairness-aware maturity building information demonstrates effectiveness efficiency accomplishing complicated data scarcity.