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| Criterion |  Systems Thinking Leverage Points |  Systems Thinking Future Reality Tree |  Product Discovery Hypothesis Prioritization Canvas |  Product Strategy DIBB |
|---|---|---|---|---|
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. | 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 many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. | 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 | High | High | Medium | Low |
Timedifferent | Half day | 2-4 h | 60-90 min | 1-2 h |
Participantsdifferent | 3-12 | 3-8 | 3-8 | 2-8 |
Formatdifferent | Workshop | Workshop | Workshop | Workshop + async |
Outputdifferent | Leverage Map, Action Strategy | Future Reality Tree, Negative Branches, Assumption List, Improved Injections | Prioritization Canvas, Hypothesis Backlog | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | Systems thinkingChangeStrategy | Theory of ConstraintsSystems thinkingChange | ExperimentsPrioritizationDiscoveryHypothesis | StrategyDecisionAssumptionsHypothesis |