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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 NoEstimates.
Agile
NoEstimates
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
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.When estimating slows a team down more than it helps, it shifts the focus to flow, small slices, and real lead time. It sorts work by value, risk, and delivery ability. The result is captured as Throughput Data, Flow Forecast, and Slicing Rules.DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report.
Complexitydifferent
HighMediumMediumLow
Timedifferent
1-4 Wochen30-90 minlaufend1-2 h
Participantsdifferent
6-30 Experten3-122-122-8
Formatdifferent
AsyncWorkshopWorkshop + asyncWorkshop + async
Outputdifferent
Expert Forecast, Consensus Range, Assumption NotesAffinity Size Map, Grouped Estimates, Unclear ItemsThroughput Data, Flow Forecast, Slicing RulesDIBB document, Belief list, Bet list, Learning report
Tagsno overlap
ForecastingExpertsDecisionStrategy
EstimationBacklogRelative sizing
EstimationForecastingFlow
StrategyDecisionAssumptionsHypothesis
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