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

Battle Count: 50

The paper is primarily focused on general risk management and safety-critical systems rather than quantitative trading. However, several concepts are transferable: probabilistic risk assessment, Bayesian networks, Monte Carlo simulation, Value at Risk concepts, robust optimization, and uncertainty quantification methods are relevant to financial risk management. The distinction between aleatory and epistemic uncertainty is applicable to market risk modeling. The hybrid approaches combining probabilistic and fuzzy methods could inform trading strategy risk assessment. However, the paper does not address financial markets, portfolio optimization, or trading-specific applications directly.

The paper itself is a review and does not implement specific methods. However, the methods it reviews vary widely in complexity: probabilistic methods (PRA, Bayesian Networks, Monte Carlo) require substantial data and computational resources; evidence/fuzzy methods require expert parameterization; hybrid approaches are described as conceptually and computationally complex, resource-intensive, and difficult to implement in operational settings. The proposed decision pathway adds a meta-level of complexity for practitioners selecting among methods.

The review follows a structured multi-stage procedure for literature selection (database searches, deduplication, screening, full-text review) with defined inclusion/exclusion criteria. However, it does not follow a formal PRISMA protocol. The classification framework and decision pathway are conceptual and not empirically validated. The 370 publications are managed in a Zotero library but no public dataset or code is provided. The methodology is transparent but the subjective classification of methods into families introduces some reproducibility limitations.