An expert classification consensus reaching model based on fuzzy trust relationship matrix in the application of steel industry

In the complex context of today's societal development, there is an increasing number of decision-making scenarios in a variety of fields, such as corporate strategic planning, public policy making, and healthcare choices. The numerous variables and uncertainties involved in these decision-making scenarios significantly increase the complexity of the decision-making environment. Traditional approaches that rely on a single expert opinion or simply aggregate the opinions of multiple experts can no longer adequately address this challenge. In this study, the concept of fuzzy trust matrix is proposed with the aim of depicting the intricate trust relationships among experts more accurately, and the computational formula for trust propagation is developed accordingly. For the multi-attribute decision-making (MADM) problem, this study further extends the fuzzy trust matrix to the level of each attribute, considering the differences in experts' familiarity on different attributes. Through the in-depth analysis of the fuzzy trust matrix, this paper divides experts into three categories and develops corresponding consensus strategies for each category. This study applies the above theories and methods to the decision-making of corporate carbon emission reduction measures in the iron and steel industry. By adopting the consensus strategy proposed in this paper, the most appropriate and efficient carbon reduction plan is finally identified. In addition, a series of conclusions on consensus reaching are drawn through comparative and sensitivity analyses, and the feasibility and validity of the model are effectively verified.