A novel consensus model integrating asynchronous opinion evolution and dynamic trust-driven three-way decision for incomplete multi-scale information systems

Abstract

[EN] In the era of big data, the integration of data from multiple sources to enhance decision-making processes has become a crucial challenge. Traditional information fusion methods often struggle with incomplete multi-scale information systems, resulting in biased conclusions and an inability to fully leverage the potential connections within the data. This study addresses these limitations by proposing a novel consensus model that integrates asynchronous opinion evolution in the DeGroot model with dynamic trust-driven three-way decision. The method employs adaptive multi-scale clustering partitioning and mutual information to uncover potential associations among the original data, introduces an interval-based neighborhood representation and a weighted aggregation approach to more reasonably impute missing values, and determines the optimal scale through the analysis of the information entropy structure curve. Furthermore, considering the asynchronous behaviors and interactive influences among decision-makers in social networks, the proposed model incorporates a differentiated group consensus regulation method to achieve more flexible and robust group decision-making. The experimental results demonstrate the effectiveness of the method proposed in this paper, with significant improvements in key indicators such as the number of modifications and iterations required to reach a consensus, while maintaining a relatively low adjustment cost. Specifically, the number of modifications and the number of iterations achieved by the proposed method are the lowest among the compared methods, indicating its superior performance in consensus-reaching processes. These findings highlight the model’s ability to efficiently handle incomplete information and enhance the rationality and dynamic adaptability of group decision-making, making it a valuable tool for information fusion.