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| Criterion |  Knowledge Modeling Event Modeling |  Decision Making Decision Tree |  Product Discovery MVP Test Matrix |  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. | 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 several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan. | 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 | Medium | Low |
Timedifferent | 2-6 h | 30-90 min | 45-75 min | 30-60 min |
Participantsdifferent | 2-8 | 1-6 | 2-8 | 1-5 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Event Model, UI Flow, Commands, Read Models | Decision Tree, Option Map, Assumption List | Test Matrix, Test Plan | Completed Experiment Canvas, Success Metric |
Tagsno overlap | EventsBlueprintDomain-Driven DesignBehavior | DecisionTreeOptions | ExperimentsValidationDiscoveryOptions | ExperimentsValidationDiscoveryHypothesis |