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| Criterion |  Knowledge Modeling Event Modeling |  Product Strategy DIBB |  Product Discovery Assumption Mapping |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | Event Modeling connects business workflows with commands, events, and views into one coherent mental model. It helps design behavior, UI, and technical slices from the same underlying logic. | 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. | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. | 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 | Low | Medium | Low |
Timedifferent | 2-6 h | 1-2 h | 45-60 min | 30-60 min |
Participantsdifferent | 2-8 | 2-8 | 2-8 | 1-5 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Event Model, UI Flow, Commands, Read Models | DIBB document, Belief list, Bet list, Learning report | Assumption map, Test backlog, Risk ranking | Completed Experiment Canvas, Success Metric |
Tagsno overlap | EventsBlueprintDomain-Driven DesignBehavior | StrategyDecisionAssumptionsHypothesis | AssumptionsRiskExperimentsValidation | ExperimentsValidationDiscoveryHypothesis |