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Criterion
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
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.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.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
HighHighLow
Timedifferent
1-4 Wochen30-90 min Setup, danach laufend1-2 h
Participantsdifferent
6-30 Experten1-82-8
Formatdifferent
AsyncWorkshop + asyncWorkshop + async
Outputdifferent
Expert Forecast, Consensus Range, Assumption NotesForecast Percentiles, Throughput Dataset, Risk CommunicationDIBB document, Belief list, Bet list, Learning report
Tagsno overlap
ForecastingExpertsDecisionStrategy
ForecastingFlowDelivery
StrategyDecisionAssumptionsHypothesis
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Methods with strong topical overlap with the current selection, not yet in the comparison.

Paper illustration of an assumption matrix with prioritized test cards
Decision Making
Assumption Surfacing
Paper illustration of Cost of Delay with its method-specific working model.
Delivery
Cost of Delay
Paper illustration of the Force Field Analysis working structure.
Decision Making
Force Field Analysis