Group decision-making in heterogeneous multi-scale information fusion: Integrating overconfident and non-cooperative behaviors

Abstract

In the field of group decision-making (GDM), the complex heterogeneous data continuously challenges the traditional single decision-making model, highlighting the limitations of traditional methods in handling multi-dimensional data and dynamic scenarios. Although information fusion is of great significance for GDM, there are still significant deficiencies in existing research within the framework of multi-scale information systems (MSIS). In particular, there is an urgent need to address the challenges of dealing with multi-structural data and managing the complex behaviors of decision-makers (DMs). Firstly, a novel concept, the heterogeneous multi-scale information system (HMSIS), is put forward. This system innovatively integrates utility value quantification analysis, fuzzy preference relation modeling, preference ranking algorithms, and equivalence class partitioning techniques, thereby constructing a highly realistic simulation framework for real-world data. Through this cross-paradigm data integration approach, the HMSIS provides a more adaptable and scalable theoretical foundation for GDM, effectively resolving the limitations of traditional models in handling complex data structures. Building on this foundation, this paper further develops the consensus-trust multi-network opinion interaction mechanism. This mechanism shatters the constraints of one-way information transmission in traditional decision-making processes. By devising an adaptive opinion exchange protocol, it enables multi-round and multi-dimensional information interactions among decision-makers. Additionally, innovative behavior monitoring and intervention rules are introduced, which can detect irrational behaviors of DMs, such as overconfidence and non-cooperation, in real time. Through dynamic weight adjustment, intelligent guidance strategies, and other means, targeted management is implemented to ensure the stability and effectiveness of the group decision-making process. Moreover, this paper constructs an optimized consensus reaching process (CRP). By embedding an optimization model and under the intelligent regulation of a virtual decision-making coordinator, it optimizes both the efficiency of decision-making information transmission and the accuracy of opinion convergence simultaneously. With the core objectives of minimizing the decision adjustment distance and shortening the consensus-reaching time, and combined with a dynamic weight allocation algorithm, this model achieves efficient and fair consensus building in complex decision-making environments. Finally, empirical studies conducted on a real-world dataset demonstrate the remarkable superiority of the proposed method. The experimental results further validate the method’s robust performance in handling heterogeneous data and complex decision-making scenarios.