The Determination and Elimination of Decision Makers' Hidden Inherent Preference in Probabilistic Linguistic Group Decision-Making

In an increasingly complex decision-making environment, the decision information we obtain contains the decision makers' professional cognition, psychological preference, risk attitude and other hidden uncertainty factors. These hidden inherent factors may cause evaluations being better or worse than their actual level. In order to reduce the bias brought by these factors, they should be identified and eliminated prior to decision making. Hence, a preference determination model is proposed and a risk model is established to eliminate these factors by analyzing the possible impact. Moreover, a more practical probabilistic linguistic model-2-tuple probabilistic linguistic term set is proposed and its related concepts are defined. Furthermore, an integrated decision-making method is provided. Finally, considering the application background of double carbon economy, an example by selecting the best design of electric vehicles charging station is conducted to illustrate the proposed method, and the feasibility and effectiveness are verified.