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Criterion
Context Map workspace showing the question, observations, and next decision.
Domain Modeling
Context Map
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
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
Purposedifferent
When several contexts need to talk to each other, it makes their relationships and dependencies legible. It helps sort integration pressure and responsibilities across system boundaries.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.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
MediumHighHighLow
Timedifferent
1-3 h30-90 min Setup, danach laufend1-4 Wochen1-2 h
Participantsdifferent
2-81-86-30 Experten2-8
Formatdifferent
Workshop + asyncWorkshop + asyncAsyncWorkshop + async
Outputdifferent
Context Map, Integration Patterns, Boundary NotesForecast Percentiles, Throughput Dataset, Risk CommunicationExpert Forecast, Consensus Range, Assumption NotesDIBB document, Belief list, Bet list, Learning report
Tagsno overlap
Domain-Driven DesignBoundariesStrategy
ForecastingFlowDelivery
ForecastingExpertsDecisionStrategy
StrategyDecisionAssumptionsHypothesis
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