expertjudgments

Expert judgments are estimates or probability assessments provided by individuals with specialized knowledge about a domain, used to quantify uncertainty when empirical data are incomplete, expensive, or unavailable. They are commonly applied in risk analysis, forecasting, project planning, and policy evaluation to supplement or replace data-driven evidence.

Structured elicitation refers to formal protocols that guide experts through problem definition, expert selection, and the collection of judgments. Common approaches include the Delphi method, which uses iterative rounds of independent judgment with feedback; Cooke’s classical method, which weights expert opinions by historically demonstrated calibration; and general probabilistic elicitation, where experts provide probability distributions, quantiles, or bounds rather than single point estimates. Elicitation often aims to capture both central estimates and uncertainty (e.g., confidence intervals or full distributions).

Aggregation of expert judgments can be at the individual level or a group level. Simple averaging of expert estimates is common, but weighting by calibration performance, coherence, or informativeness can improve accuracy. When possible, judgments are integrated with existing data through Bayesian updating or probabilistic synthesis.

Biases and limitations include overconfidence, anchoring, framing effects, selective recall, and cultural or disciplinary differences. The quality of expert judgments depends on expert selection, the clarity of questions, independence of judgments, and transparency of assumptions. Documentation and sensitivity analyses help assess robustness.

Applications span cost estimation, technological risk, environmental impact, epidemiology, and strategic planning. Properly designed elicitation and aggregation protocols can make expert judgments a valuable supplement to limited data, supporting informed decision-making under uncertainty.