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| Criterion |  Decision Making Decision Tree |  Product Discovery Opportunity Solution Tree |  Decision Making Constraint Analysis |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
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 discovery keeps swinging between goals, ideas, and learning loops, it creates clear steering logic. It connects the desired outcome, opportunities, and experiments into a legible structure. | When an initiative stalls, the limit often lies not in the idea but in hard or soft boundary conditions. Constraint Analysis separates these limits and shows which of them can actually be shaped. | 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. |
Complexitydifferent | Medium | Medium | Low | Low |
Timedifferent | 30-90 min | 1-2 h Setup, laufend | 30-90 min | 30-60 min |
Participantsdifferent | 1-6 | 2-6 | 2-8 | 1-5 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Decision Tree, Option Map, Assumption List | Opportunity Solution Tree, Experiment Backlog, Learning Log | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Completed Experiment Canvas, Success Metric |
Tagsno overlap | DecisionTreeOptions | DiscoveryOutcomesExperimentsOpportunity | ConstraintsDecisionPlanningOptions | ExperimentsValidationDiscoveryHypothesis |