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Criterion
Design Thinking as a learning loop: an observation notebook, a focused problem statement, different idea sketches, a cardboard prototype and user-test notes. Feedback leads to new understanding.
Innovation
Design Thinking
Decision Tree method illustration showing its working structure
Decision Making
Decision Tree
Paper illustration of an assumption matrix with prioritized test cards
Decision Making
Assumption Surfacing
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
Purposedifferent
For complex user problems with an uncertain cause, the method connects observation, interpretation, and experiment into a learning cycle. It keeps the focus on real needs and avoids premature solution language. This produces robust decisions for product and service.For decisions with follow-on paths and dependencies, a linear comparison is often not enough. A Decision Tree shows how options branch under conditions and which consequences hang on each branch.Plans often contain assumptions that were never openly stated and remain dangerous precisely because of that. Assumption Surfacing makes these silent premises visible and prioritizes which of them carry the initiative or could sink it.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 Tag bis mehrere Wochen30-90 min45-90 min1-2 h
Participantsdifferent
4-101-62-82-8
Formatdifferent
WorkshopWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Problem Statement, Prototype, Test LearningsDecision Tree, Option Map, Assumption ListAssumption List, Critical Assumptions, Learning PlanDIBB document, Belief list, Bet list, Learning report
Tagsno overlap
InnovationPrototypeResearch
DecisionTreeOptions
AssumptionsRiskDecisionDiscovery
StrategyDecisionAssumptionsHypothesis
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