Survey of Various Fuzzy and Uncertain Decision-Making Methods

TL;DR

This survey comprehensively reviews uncertainty-aware multi-criteria decision-making methods, categorizing problem settings, solution techniques, and providing guidance on method selection, while highlighting open challenges in explainability, stability, and scalability.

Contribution

It offers a structured taxonomy of fuzzy and uncertain decision-making methods, summarizing problem settings, solution approaches, and providing practical guidance for method selection.

Findings

Categorizes decision-making under uncertainty into discrete, group, dynamic, multi-stage, multi-level, multiagent, and multi-scenario settings.

Contrasts various solution procedures including scoring, outranking, and rule-based methods.

Identifies open research directions in explainability, stability, and large-scale decision environments.

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.