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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 Planning Poker.
Agile
Planning Poker
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 estimates within the team diverge too much, it puts differing expectations on a common test bench. It separates rough complexity from unspoken assumptions.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
HighMediumLowLow
Timedifferent
1-4 Wochen30-90 min2-5 min je Item1-2 h
Participantsdifferent
6-30 Experten3-123-92-8
Formatdifferent
AsyncWorkshopWorkshop + asyncWorkshop + async
Outputdifferent
Expert Forecast, Consensus Range, Assumption NotesAffinity Size Map, Grouped Estimates, Unclear ItemsRelative Estimates, Assumption Notes, Split CandidatesDIBB document, Belief list, Bet list, Learning report
Tagsno overlap
ForecastingExpertsDecisionStrategy
EstimationBacklogRelative sizing
EstimationAgileRelative sizingTeam
StrategyDecisionAssumptionsHypothesis
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