Survey of Various Fuzzy and Uncertain Decision-Making Methods
arXiv:2603.15709v1 Announce Type: new Abstract: 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
arXiv:2603.15709v1 Announce Type: new Abstract: 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.
Executive Summary
The article provides a comprehensive survey of fuzzy and uncertain decision-making methods, organizing the field into a concise taxonomy. It covers problem-level settings, weight elicitation, inter-criteria structure, and solution procedures, including compensatory and non-compensatory approaches. The survey highlights key considerations for choosing methods, such as robustness, interpretability, and data availability, and concludes with open directions for future research.
Key Points
- ▸ Uncertainty-aware multi-criteria decision-making (MCDM) methods
- ▸ Task-oriented taxonomy for organizing the field
- ▸ Contrasting compensatory and non-compensatory solution procedures
Merits
Comprehensive coverage
The survey provides a thorough review of various fuzzy and uncertain decision-making methods, making it a valuable resource for researchers and practitioners.
Clear taxonomy
The proposed taxonomy helps to clarify the complex field of MCDM, facilitating easier navigation and understanding of the different methods and approaches.
Demerits
Limited focus on practical applications
The survey primarily focuses on theoretical aspects, with limited discussion on real-world applications and case studies, which may reduce its appeal to practitioners.
Oversimplification of complex issues
Some complex issues, such as inter-criteria structure and causality modelling, may be oversimplified in the survey, potentially leading to incomplete or inaccurate representations.
Expert Commentary
The survey provides a valuable contribution to the field of MCDM, offering a clear and comprehensive overview of fuzzy and uncertain decision-making methods. However, to fully realize the potential of these methods, future research should focus on developing more practical and applicable approaches, as well as addressing the challenges of explainability, stability, and scalability in large-scale and dynamic decision environments. By doing so, researchers and practitioners can work together to create more effective and trustworthy decision-making systems.
Recommendations
- ✓ Future research should prioritize the development of more practical and applicable uncertainty-aware MCDM methods
- ✓ Practitioners should consider leveraging the survey's taxonomy and methodology to inform their decision-making processes and improve outcomes
