A Group Decision-Making Model Integrating Information Consensus and Polarity

Abstract

In opinion dynamics (OODs), the DeGroot and Hegselmann–Krause (HK) bounded confidence models are foundational tools for studying information evolution. However, both models have unavoidable limitations, particularly in group decision-making scenarios. This article proposes a novel OODs model that integrates the strengths of both the DeGroot and HK models within a unified framework. The proposed model balances ultimate consensus and diversity without requiring a subjectively chosen threshold by introducing an improved hyperbolic tangent function. Adjusting the function’s parameter enables a smooth transition between the DeGroot and HK models, enhancing adaptability across various scenarios. To determine the weights of agents during information evolution, we develop a calculation method based on a distance measure. Furthermore, the model’s properties are thoroughly analyzed through theoretical derivations. The model is extended to the linguistic environment, aligning with natural expression habits in real-world contexts. Comprehensive examples and comparisons validate the proposed model’s effectiveness, demonstrating its superiority and robustness.