An adaptive consensus model with hybrid feedback mechanism: Exploring interference effects under evidence theory

Abstract

The consensus reaching process (CRP) is crucial for achieving broad agreement in group decision-making (GDM). In the CRP, factors such as epistemic uncertainty and opinion interference of experts may cause cognitive biases and irrational behaviors. Therefore, this paper proposes a new adaptive consensus model based on quantum probability theory (QPT) in the context of evidence theory and develops a hybrid feedback mechanism to select the optimal alternative accepted by a majority of experts. To improve the level of precision when dealing with epistemic uncertainty, the design of parameters in evidence theory is optimized, jointly considering the experts’ harmony degree and reliability, to reduce decision biases. Moreover, expert relationships are classified into three cases—mutual support, mutual conflict, and mutual independence—while considering the interference effects within the group. To mitigate conflicts and promote consensus, the quantum Bayesian network (QBN) is employed to model expert opinion interference, and a hybrid feedback mechanism, that uses individual or group opinions as a reference, is designed for adjusting opinions tailored to the specific relationships among experts. Finally, an illustrative example regarding the risk assessment of medical waste disposal is presented to verify the feasibility and effectiveness of the proposed method.