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Criterion
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
Paper illustration for Wideband Delphi.
Decision Making
Wideband Delphi
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
Purposedifferent
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.A single opinion rarely holds up for planning when the future is genuinely uncertain. It separates options, evaluation criteria, and open risks. The result is captured as an Estimate Range, Assumption Log, and Expert Consensus Notes.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.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.
Complexitydifferent
LowHighHighHigh
Timedifferent
1-2 h1-4 h or multiple rounds30-90 min Setup, danach laufend1-4 Wochen
Participantsdifferent
2-84-12 Experten1-86-30 Experten
Formatdifferent
Workshop + asyncWorkshop + asyncWorkshop + asyncAsync
Outputdifferent
DIBB document, Belief list, Bet list, Learning reportEstimate Range, Assumption Log, Expert Consensus NotesForecast Percentiles, Throughput Dataset, Risk CommunicationExpert Forecast, Consensus Range, Assumption Notes
Tagsno overlap
StrategyDecisionAssumptionsHypothesis
EstimationExpertsForecasting
ForecastingFlowDelivery
ForecastingExpertsDecisionStrategy
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