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| Criterion |  Decision Making Decision Tree |  Decision Making Delphi Method |  Product Discovery Experiment Canvas |  Product Strategy DIBB |
|---|---|---|---|---|
Purposedifferent | 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. | 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 experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. | 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 | Medium | High | Low | Low |
Timedifferent | 30-90 min | 1-4 Wochen | 30-60 min | 1-2 h |
Participantsdifferent | 1-6 | 6-30 Experten | 1-5 | 2-8 |
Formatdifferent | Workshop + async | Async | Workshop + async | Workshop + async |
Outputdifferent | Decision Tree, Option Map, Assumption List | Expert Forecast, Consensus Range, Assumption Notes | Completed Experiment Canvas, Success Metric | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | DecisionTreeOptions | ForecastingExpertsDecisionStrategy | ExperimentsValidationDiscoveryHypothesis | StrategyDecisionAssumptionsHypothesis |