Survey of Various Fuzzy and Uncertain Decision-Making Methods

• URL: http://arxiv.org/abs/2603.15709v1
• Date: Mon, 16 Mar 2026 13:45:09 GMT
• Title: Survey of Various Fuzzy and Uncertain Decision-Making Methods
• Abstract summary: Decision-making in real applications is often affected by vagueness, incomplete information, heterogeneous data, and conflicting expert opinions.<n>This survey reviews uncertainty-aware multi-criteria decision-making (MCDM) and organizes the field into a concise, task-oriented taxonomy.
• Score: 2.2667044928324747
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Multi-Modal Anomaly Detection: A Survey [21.953084534543237]
  Multi-Modal Anomaly Detection (MMAD) detects rare abnormal events from heterogeneous data sources.<n>MMAD is increasingly used in safety- and reliability-critical applications such as industrial inspection and cybersecurity.<n>We formalize the problem, identify five intrinsic characteristics underlying its core challenges, and organize prior work into two complementary paradigms.
  arXiv  Detail & Related papers  (2026-08-24T01:53:22Z)
• CLAIM: Leading Open-domain Active Clarification of Large Language Models with Uncertainty Measurement [50.91045324738942]
  CLAIM is an uncertainty-driven framework for active clarification learning in open-domain settings.<n>We propose an entropy-driven synthetic data generation pipeline that integrates uncertainty estimation with semantic clustering and reasoning-based judgments.<n> Experimental results demonstrate that CLAIM can learn stable and generalizable clarification strategies without relying on manually labeled data.
  arXiv  Detail & Related papers  (2026-08-12T04:27:45Z)
• Dynamics Within Latent Chain-of-Thought: An Empirical Study of Causal Structure [58.89643769707751]
  We study latent chain-of-thought as a manipulable causal process in representation space.<n>We find that latent-step budgets behave less like homogeneous extra depth and more like staged functionality with non-local routing.<n>These results motivate mode-conditional and stability-aware analyses as more reliable tools for interpreting and improving latent reasoning systems.
  arXiv  Detail & Related papers  (2026-02-09T15:25:12Z)
• A New Approach for Multicriteria Assessment in the Ranking of Alternatives Using Cardinal and Ordinal Data [0.0]
  We propose a novel MCA approach that combines two Virtual Gap Analysis (VGA) models.<n>The VGA framework, rooted in linear programming, is pivotal in the MCA methodology.
  arXiv  Detail & Related papers  (2025-07-10T04:00:48Z)
• A Principled Approach to Randomized Selection under Uncertainty: Applications to Peer Review and Grant Funding [61.86327960322782]
  We propose a principled framework for randomized decision-making based on interval estimates of the quality of each item.<n>We introduce MERIT, an optimization-based method that maximizes the worst-case expected number of top candidates selected.<n>We prove that MERIT satisfies desirable axiomatic properties not guaranteed by existing approaches.
  arXiv  Detail & Related papers  (2025-06-23T19:59:30Z)
• Retrieval Augmented Decision-Making: A Requirements-Driven, Multi-Criteria Framework for Structured Decision Support [8.585671505840637]
  This paper proposes the RAD method, which integrates Multi-Criteria Decision Making with the semantic understanding capabilities of LLMs.<n>The method automatically extracts key criteria from industry documents, builds a weighted hierarchical decision model, and generates structured reports under model guidance.<n> Experiments show that in various decision-making tasks, the decision reports generated by RAD significantly outperform existing methods in terms of detail, rationality, and structure.
  arXiv  Detail & Related papers  (2025-05-24T03:13:29Z)
• Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy [59.64384863882473]
  We study the problem of interactive decision making in which the underlying environment changes over time subject to given constraints.<n>We propose a framework, which provides an complexity between the complexity and adversarial settings of decision making.
  arXiv  Detail & Related papers  (2025-01-24T21:31:50Z)
• Causal Fairness Analysis [68.12191782657437]
  We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings. The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms. Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
  arXiv  Detail & Related papers  (2022-07-23T01:06:34Z)
• On the Complexity of Adversarial Decision Making [101.14158787665252]
  We show that the Decision-Estimation Coefficient is necessary and sufficient to obtain low regret for adversarial decision making. We provide new structural results that connect the Decision-Estimation Coefficient to variants of other well-known complexity measures.
  arXiv  Detail & Related papers  (2022-06-27T06:20:37Z)
• Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles [33.87108569224395]
  Local decision rules are commonly understood to be more explainable, due to the local nature of the patterns involved.<n>We propose a new method to learn and ensemble local decision rules, that are robust both in the training and deployment environments.
  arXiv  Detail & Related papers  (2021-09-21T20:50:10Z)
• Inverse Active Sensing: Modeling and Understanding Timely Decision-Making [111.07204912245841]
  We develop a framework for the general setting of evidence-based decision-making under endogenous, context-dependent time pressure. We demonstrate how it enables modeling intuitive notions of surprise, suspense, and optimality in decision strategies.
  arXiv  Detail & Related papers  (2020-06-25T02:30:45Z)
• Application of independent component analysis and TOPSIS to deal with dependent criteria in multicriteria decision problems [8.637110868126546]
  We propose a novel approach whose aim is to estimate, from the observed data, a set of independent latent criteria. A central element of our approach is to formulate the decision problem as a blind source separation problem. We consider TOPSIS-based approaches to obtain the ranking of alternatives from the latent criteria.
  arXiv  Detail & Related papers  (2020-02-06T13:51:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.