A Multistage Fuzzy Decision-Making Framework for Dealing with Uncertainty in Civil Law

The process of legal decision-making, especially in complex cases such as mergers and acquisitions, involves many vague and uncertain factors. These factors can make the decision-making process complex and difficult to quantify; thus, a new approach is needed to address these issues. The goal of this paper is to develop a multistage fuzzy decision-making framework to help decision-makers identify, quantify, and manage ambiguity and uncertainty effectively in the legal decision-making process. The framework aims to improve the quality and efficiency of decision-making through systematic analysis. The framework is divided into four main stages: (1) problem definition and information collection, which involves defining decision objectives and collecting relevant information; (2) fuzzy processing and model building, which involves transforming non-quantitative information into fuzzy variables and constructing a fuzzy evaluation index system; (3) decision scheme generation and optimization, which involves generating and optimizing decision schemes via fuzzy logic and a genetic algorithm; and (4) a dynamic adjustment and feedback mechanism, which adjusts decisions according to new information and environmental changes. This paper compares the empirical performance of the Mamdani model and Sugeno model in addressing civil law decision-making problems. The results show that the Sugeno model performs better in terms of decision-making accuracy, computational efficiency, and interpretability. In addition, the effectiveness of the framework is verified by a case study, which focuses on contract disputes, which can provide more accurate and reasonable solutions. The multistage fuzzy decision-making framework proposed in this paper is a practicable method to address the complexity and uncertainty of legal decision-making. Specifically, it can help decision-makers better understand and address ambiguity and uncertainty through systematic analysis and optimization processes, thus improving the quality of decision-making. Future research will explore the potential application of the framework in other areas of law.