Interval-valued information fusing via dynamic three-way decision model and regret theory

In the realm of practical decision-making, the challenge of aggregating information across multiple time periods is highlighted by the inadequacy of traditional, static methods to handle such dynamic complexities. This paper addresses this challenge by introducing a dynamic three-way decision model that integrates dynamic interval-valued fuzzy information with regret theory, emphasizing the critical role of information fusion in modern decision-making. First, an adaptive time-weight method is proposed to take into account for the varying importance of information over different periods, reflecting the evolving nature of data in real-world scenarios. Second, the paper employs a fuzzy c-means algorithm and an embedding degree to describe objects, and this description forms the basis for estimating conditional probabilities, which provides a more nuanced understanding than traditional models. Besides, the introduction of an interval additive generator pair of overlap functions further broadens the model’s application scope by fusing information grains. Meanwhile, the attribute-weight vector is determined through a multi-objective programming model, considering both embeddedness and deviation, which is essential for reflecting the relative importance of different attributes. Finally, the model aims to maximize utility by incorporating psychological behaviors, aligning with the realities of human decision-making processes. Its practicality, rationality, and superiority are demonstrated through its application to city air quality assessment problems, where comparative and experimental analyses verify the model’s effectiveness in handling complex, real-world decision-making challenges. In summary, this paper presents a dynamic decision-making model that underscores the importance of information fusion in addressing aggregated data across time periods, enhancing the human-centric decision-making process.