A Minimum Cost Consensus Model Based on Two-Dimensional Linguistic Information in Social Network Environment

As social media becomes more prevalent, individuals are drawing closer together, and social networks are increasingly acting as a bridge for information exchange. Among these, the social trust network (STN), which relies on trust relationships between nodes, is commonly used in multi-attribute group decision-making. However, accurately expressing decision-maker opinions with only real numbers can be challenging in many decision-making scenarios. To analyze inter-individual relationships and facilitate consensus, this paper introduces a two-dimensional linguistic (2DL) information form and proposes a 2DL-based minimum cost consensus model in STN. This paper initially introduces a method for integrity analysis of STN from a path perspective, which constructs a complete STN matrix by considering trust aggregation and the stability of mediated path attenuation. Subsequently, a unit adjustment cost measure is proposed, combining individuals’ hesitancy with the impact of external trust relationships during the consensus process. Building on this, a minimum cost consensus model is proposed. Finally, the applicability of this decision-making process is demonstrated through an example of new energy vehicle option selection, and the effectiveness and advantages of the method and model are validated through comparative analysis.