A Scalable Elicitation and Parameterization of Expert Uncertainty in Project Risk Management: A Mode-Precision Decoupling Framework

Abstract Early-stage risk assessments for large-scale projects require intuitive and scalable methods for representing expert uncertainty. This study introduces a decision-support framework that translates expert judgment into analytically tractable probability distributions. The mode-precision (MP) framework addresses a key limitation of the classical beta distribution by replacing its nonintuitive shape parameters with two interpretable inputs: the mode; and a dimensionless measure of confidence (precision, κ ). This one-to-one reparameterization preserves distributional flexibility while improving practical usability. The central contribution is the decoupling of location and dispersion, enabling experts to independently specify expected outcomes and associated uncertainty. This structure supports anchoring-based and iterative elicitation. The framework also provides simplified moment approximations, making it well suited to large-scale applications. Within this framework, the program evaluation and review technique emerges as a special case. We conduct a comparative evaluation against benchmark models and apply the framework to two illustrative cases: contingency planning and dependence modeling with Bayesian risk drivers. The results highlight the MP framework’s distributional and structural flexibility, demonstrating that the framework enhances modeling flexibility and the scalability of risk-informed decision-making.