Expertpeiling

Expertpeiling is a practice in which a structured elicitation of judgments is sought from a selected panel of experts on a particular issue where data are incomplete or uncertain. The aim is to synthesize informed assessments rather than public opinion, and to quantify uncertainty where possible.

Typically, expertpeiling involves designing questions, choosing experts, collecting estimates or probabilities, and aggregating the responses. Methods emphasize reducing bias through anonymity, controlled feedback, and iterative rounds. In some implementations, experts provide confidence intervals or probability distributions rather than single-point estimates.

Common variants include the Delphi method, which uses several rounds with anonymized responses and feedback; and structured expert judgment, which emphasizes formal scoring and uncertainty quantification. The choice of aggregation method—simple average, weighted opinions, or approaches like Cooke's method—affects the final forecast.

Applications include technology forecasting, risk assessment, policy analysis, climate projections, and strategic planning. Benefits include leveraging specialized knowledge and explicitly addressing uncertainty; limitations include selection bias, potential overconfidence, and the influence of dominant minds if process controls are weak.

Origins lie in mid-20th century efforts to forecast outcomes in science and defense research, with the Delphi method developed at RAND. Today, expertpeiling remains a valuable tool when data are sparse, controversial, or rapidly changing, providing structured judgments to inform decisions and policy.