A Biobjective Optimization Model for Expert Opinions Aggregation and Its Application in Group Decision Making

Abstract

Expert opinions aggregation is a generic part of the group decision making (GDM) problem. The challenge of expert opinions aggregation is to reduce the subjectivity in the process as much as possible and improve the reliability of the aggregated opinion. Most of the existing literature try to eliminate the subjectivity but seldom consider the reliability of the aggregation result. In this article, we propose a new criterion that contains consensus level and confidence level to improve both objectivity (i.e., consensus) and reliability (i.e., no absurd result) with the experts' opinions being represented as probability density functions. Subsequently, the expert opinion aggregation problem is formulated as a biobjective optimization model. The Survey of Professional Forecasters is used as an example to examine the feasibility and accuracy of the proposed approach and the result shows that the new approach can provide a better estimation than that of the single objective model in the literature. To our best knowledge, the proposed criterion is new in the literature of GDM along with relevant problems. The proposed criterion is actually a pilot work to probe the problem of the quality of a GDM process, which is largely ignored in the field of GDM.