Methods for Uncertainty Representation in Risk Management: A Comparative Review and Decision-Oriented Framework

Abstract

The consideration of uncertainty is a central but frequently inadequately addressed component of risk management. A systematic treatment of uncertainty is essential for ensuring the quality and traceability of decision-making processes, particularly in complex and safety-critical environments. This review systematically analyzes how established risk management approaches conceptualize and represent uncertainty in both their theoretical foundations and practical applications. Based on a systematic literature review of 370 publications, the identified approaches are classified into five methodological families. These include probabilistic methods, evidence-based and fuzzy-logic approaches, qualitative elicitation techniques, graphical and visual representations and hybrid frameworks. The analysis shows that probabilistic methods remain predominant due to their quantitative rigor, whereas fuzzy and evidence-based approaches are particularly suited to addressing vagueness and epistemic uncertainty. Qualitative and graphical approaches are found to enhance interpretive understanding and support the transparent communication of uncertainty. Despite these developments, the analysis indicates that the practical integration of these approaches into operational risk management remains limited in many domains. The findings highlight the need for more structured guidance in method selection and suggest that future research would benefit from further development of hybrid approaches and visualization techniques.