Expert Positivity Mining and Minimum Cost Consensus for Emergency Decision-Making with Probabilistic Linguistic Evaluation

Due to the urgency and complexity of emergency, achieving consensus in emergency decision-making (EDM) is more challenging than traditional group decision-making. Faced with the complexity of EDM, experts may exhibit inactive behaviors during the participation process. Furthermore, the existing minimum cost consensus models (MCCM) cannot solve the EDM problems that consider expert positivity, and they do not ensure the positivity levels of experts during the consensus reaching process (CRP). Therefore, this paper proposes a positivity measurement method to quantify the positivity levels of experts, and constructs an improved MCCM based on experts’ positivity management. Firstly, five identification rules and quantitative methods have been presented to measure the positivity levels of experts in EDM. Subsequently, considering the positivity levels and decision speeds of experts, a multi-objective optimization model is constructed to obtain the weights of experts. Moreover, the influence of positivity management on opinion modification is considered in the CRP, and an improved MCCM based on positivity mechanism is developed to promote viewpoint convergence. Ultimately, case study and further analysis are discussed to validate the effectiveness of the proposed positivity-based MCCM in EDM.