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Criterion
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
Paper illustration for Affinity Estimation.
Agile
Affinity Estimation
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Purposedifferent
When knowledge is distributed and direct dominance should be avoided, groups quickly fall under the sway of their opinion leaders. The Delphi Method gathers assessments iteratively and gradually brings expert judgments closer together.When many tasks need to be classified quickly, it sorts them by perceived effort and similarity. It reduces the effort of fine-grained estimation for large volumes.Helps clarify scope, sequence, and delivery flow in concrete terms. It makes work, boundaries, and order steerable. The result is captured as Forecast Percentiles, Throughput Dataset, and Risk Communication.
Complexitydifferent
HighMediumHigh
Timedifferent
1-4 Wochen30-90 min30-90 min Setup, danach laufend
Participantsdifferent
6-30 Experten3-121-8
Formatdifferent
AsyncWorkshopWorkshop + async
Outputdifferent
Expert Forecast, Consensus Range, Assumption NotesAffinity Size Map, Grouped Estimates, Unclear ItemsForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
ForecastingExpertsDecisionStrategy
EstimationBacklogRelative sizing
ForecastingFlowDelivery
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Often compared together

Methods with strong topical overlap with the current selection, not yet in the comparison.

Paper illustration for Wideband Delphi.
Decision Making
Wideband Delphi
Paper illustration of Cost of Delay with its method-specific working model.
Delivery
Cost of Delay