A Family of Aggregation Operators for Group Decision-Making from the Perspective of Incentive Management

Aggregating various decision information provided by a group of decision makers (DMs) into an integrated one is essential for seeking the optimal solution. This paper aims to propose an effective method for information aggregation in group decision-making (GDM) in an uncertain environment. The approach introduces a family of incentive-induced cluster-based uncertain ordered weighted averaging (II-CUOWA) operators from the perspective of incentive management. Specifically, the II-CUOWA operator is first introduced, involving the definition, the clustering method of judgment information, the calculation method of position weights, and several mathematical properties. Then, the study delves into the exploration of generalized formulas for the II-CUOWA operator, as well as discussing special cases achievable by adjusting internal parameters. Finally, this paper outlines the aggregation process of II-CUOWA operators when addressing GDM problems, accompanied by a practical example illustrating its application and validity in employees’ performance assessment. The results show that II-CUOWA operators not only highlight the distributed structure of decision information but also possess the capability to reward or penalize alternatives, thereby guiding their development by considering the manager’s incentive preference. The proposed method enriches the methodology of GDM theory from a novel research perspective and provides a solution to practical GDM problems.