梳理模糊决策方法体系，帮人选对决策工具。
Survey of Various Fuzzy and Uncertain Decision-Making Methods
- 按任务场景分类，整理模糊决策的各类问题设置。
- 对比不同决策方法的优劣，明确适用条件。
- 适合需要解释性与鲁棒性的复杂决策场景。
现实决策常受模糊性、信息不全、数据异构和专家意见冲突影响。本文综述了面向不确定性的多准则决策（MCDM）方法，构建了一个任务导向的简洁分类体系。总结了问题层面的设定（离散、群组/共识、动态、多阶段、多层级、多智能体、多情景）、权重获取方式（模糊/语言输入下的主观与客观方案），以及准则间结构与因果建模方法。在求解策略上，对比了补偿型评分法、参考点距离与折衷方法、非补偿型排序框架在排序或分类中的应用；还概述了基于规则/证据与序列化决策模型，可生成可解释规则或策略。文章突出典型输入、核心计算步骤与主要输出，并针对鲁棒性、可解释性与数据可用性提供方法选择建议。最后指出可解释不确定性融合、稳定性及大规模动态环境下的可扩展性等开放方向。
原文摘要 · Abstract (English)
Decision-making in real applications is often affected by vagueness, incomplete information, heterogeneous data, and conflicting expert opinions. This survey reviews uncertainty-aware multi-criteria decision-making (MCDM) and organizes the field into a concise, task-oriented taxonomy. We summarize problem-level settings (discrete, group/consensus, dynamic, multi-stage, multi-level, multiagent, and multi-scenario), weight elicitation (subjective and objective schemes under fuzzy/linguistic inputs), and inter-criteria structure and causality modelling. For solution procedures, we contrast compensatory scoring methods, distance-to-reference and compromise approaches, and non-compensatory outranking frameworks for ranking or sorting. We also outline rule/evidence-based and sequential decision models that produce interpretable rules or policies. The survey highlights typical inputs, core computational steps, and primary outputs, and provides guidance on choosing methods according to robustness, interpretability, and data availability. It concludes with open directions on explainable uncertainty integration, stability, and scalability in large-scale and dynamic decision environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。