Managing Strategic Manipulation Behaviors Based on Historical Data of Preferences and Trust Relationships in Large-Scale Group Decision-Making

In the large-scale group decision-making (LSGDM) problems, some experts may adopt strategic manipulation behaviors which can be reflected in trust and preference values. These behaviors can bias or hinder the large-scale consensus reaching process. This article proposes a novel consensus framework to deal with these strategic manipulation behaviors from the perspective of historical data of trust and preference values in LSGDM. In the proposed consensus framework, the experts are classified into several clusters using the historical data of trust and preference values. Next, the strategic manipulation behaviors of trust and preference values of clusters are identified, respectively. Then, we take the penalty strategy against experts in clusters with two kinds of strategic manipulation behaviors by updating experts’ weights. Simulation and comparison studies are employed to show the validity of the proposed consensus framework against traditional frameworks for managing strategic manipulation behaviors in LSGDM.