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| Criterion |  Systems Thinking Leverage Points |  Product Discovery Assumption Mapping |  Systems Thinking Future Reality Tree |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | Leverage Points show exactly where interventions in a system produce a disproportionate effect. It draws relationships, patterns, and feedback loops. The result is captured as a leverage map and action strategy. | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. | A Future Reality Tree shows how desired actions are meant to lead to a better system state. The method checks whether a path of change holds together logically before time flows into implementation. | When two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. |
Complexitydifferent | High | Medium | High | High |
Timedifferent | Half day | 45-60 min | 2-4 h | 1-4 Wochen |
Participantsdifferent | 3-12 | 2-8 | 3-8 | 1-6 |
Formatdifferent | Workshop | Workshop + async | Workshop | Async |
Outputdifferent | Leverage Map, Action Strategy | Assumption map, Test backlog, Risk ranking | Future Reality Tree, Negative Branches, Assumption List, Improved Injections | Experiment results, Decision log, Learning summary |
Tagsno overlap | Systems thinkingChangeStrategy | AssumptionsRiskExperimentsValidation | Theory of ConstraintsSystems thinkingChange | ExperimentsGrowthAnalyticsValidation |