Expert Judgment Elicitation in Uncertainty Analysis

Expert judgment elicitation involves the structured collection and quantification of specialists’ beliefs about uncertain parameters when empirical data are limited or absent. By translating subjective assessments into probability distributions, elicitation provides essential inputs for probabilistic modelling, risk assessment and decision support. Contemporary practice emphasises rigorous protocols to mitigate cognitive biases, combining individual and group sessions, calibrated scoring rules and iterative feedback. Methods range from classical aggregation techniques—where experts’ opinions are weighted according to performance metrics—to fully Bayesian approaches that treat elicited distributions as prior information, subsequently updated with empirical evidence. Applications span environmental impact assessments, health technology appraisals, engineering reliability studies and policy evaluations, each adapting methodological choices to context, available resources and decision‐makers’ needs. Recent advances have focused on remote and web‐based tools to broaden access, on refinem­ent of calibration metrics to improve statistical accuracy and informativeness, and on flexible frameworks that reconcile differing expert views. Together, these developments demonstrate the global significance of expert elicitation as both a scientific discipline and a practical instrument for transparent management of uncertainty.

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Leading work has emphasised the scientific foundations of expert elicitation by articulating best practices for minimising bias and ensuring rigour. One seminal study set out protocols for translating subjective judgments into formal probability distributions, compared major elicitation frameworks and demonstrated joint‐distribution elicitation through a detailed case study. In health‐care decision‐making, a mixed‐methods investigation developed a flexible reference protocol for structured expert elicitation, balancing principles such as fitness for purpose, expert selection and individual versus consensus elicitation. More recently, an empirical Bayes adaptation of classical weighting methods has been proposed to shrink experts’ weights, reducing mean squared error in aggregated forecasts. Although initial tests showed mixed improvements, the shrinkage approach offers a pathway to greater reliability in small‐sample elicitation studies by tempering the influence of highly variable performance scores.

Expert Judgment Elicitation in Uncertainty Analysis publication trend

The graph below shows the total number of articles in expert judgment elicitation in uncertainty analysis across all publications each year (not limited to Nature Index journals).

Technical terms

Expert elicitation: A formal process for converting experts’ subjective beliefs about uncertain quantities into probability distributions.

Calibration: The assessment of how well an expert’s probability judgments correspond to observed outcomes, often used to weight their contributions.

Aggregation: The mathematical combination of multiple experts’ distributions into a single consensus or performance‐weighted distribution.

Prior distribution: In Bayesian analysis, the representation of uncertainty about a parameter before consideration of new data, frequently supplied by elicitation.

Cognitive bias: Systematic deviations from rational judgment (e.g. overconfidence) that elicitation protocols seek to identify and attenuate.

References

1. Expert Knowledge Elicitation: Subjective but Scientific. The American Statistician (2019).
2. The use of expert elicitation in environmental health impact assessment: a seven step procedure. Environmental Health (2010).
3. Developing a reference protocol for structured expert elicitation in health-care decision-making: a mixed-methods study. Health Technology Assessment (2021).
4. A web-based tool for eliciting probability distributions from experts. Environmental Modelling & Software (2014).
5. EXPLICIT: a feasibility study of remote expert elicitation in health technology assessment. BMC Medical Informatics and Decision Making (2017).
6. Shrinking the Variance in Experts’ “Classical” Weights Used in Expert Judgment Aggregation. Forecasting (2023).
7. Eliciting Dirichlet and Gaussian copula prior distributions for multinomial models. Statistics and Computing (2016).