Probabilistic linguistic three-way multi-attibute decision making for hidden property evaluation of judgment debtor (Q2036104)

Summary: Most law enforcement cases executed by the courts in China have behaviours of evading, evading, or even violently resisting execution or passively waiting for enforcement, which seriously affects the authority of legal judgments and the judiciary's credibility. Therefore, we develop a hidden property evaluation model based on the probabilistic linguistic three-way multiattribute decision-making (PL3W-MADM) method. Considering the advantages of probabilistic linguistic term sets (PLTSs) expressing the evaluation information and their probabilities on judgment debtor given by expert judges, we extend the three-way decision method to a probabilistic linguistic environment and develop the strict PL3W-MADM model and flexible PL3W-MADM model. Then, the PL3W-MADM models are used to construct the hidden property evaluation model of judgment debtors. Finally, the developed hidden property evaluation model can quickly and effectively classify the judgment debtors into three categories: hidden behaviour, no hidden behaviour or lack of information, and temporary inability to judge. The results show that the developed model is more suitable for hidden property evaluation than the strict PL3W-MADM model and the flexible PL3W-MADM model.