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Criterion
Paper illustration of Hooked Model with its method-specific working model.
Growth
Hooked Model
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
Paper illustration of Riskiest Assumption Test with a method-specific labelled workspace.
Product Discovery
Riskiest Assumption Test
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Purposedifferent
The Hooked Model helps clarify engagement loops, user behavior, and experiments. It makes repeat behavior measurable and captures the result as a hooked loop, trigger map, and reward design.When knowledge is distributed and direct dominance should be avoided, groups quickly fall under the sway of their opinion leaders. The Delphi Method gathers assessments iteratively and gradually brings expert judgments closer together.When an idea hinges on one critical unknown, it prioritizes testing that exact piece of uncertainty first. It separates problem, assumption, solution, and evidence. The result is captured as a prioritized list of assumptions, a test plan, and a result report.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
MediumHighMediumHigh
Timedifferent
Multiple workshops over several weeks1-4 Wochen1-2 Wochen pro Iteration1-4 Wochen
Participantsdifferent
2-86-30 Experten2-61-6
Formatdifferent
Workshop + asyncAsyncWorkshop + asyncAsync
Outputdifferent
Hooked loop, Trigger map, Reward design, Ethics checkExpert Forecast, Consensus Range, Assumption NotesPrioritized Assumption List, Test Plan, Results ReportExperiment results, Decision log, Learning summary
Tagsno overlap
GrowthBehaviorRetention
ForecastingExpertsDecisionStrategy
ExperimentsValidationDiscoveryAssumptions
ExperimentsGrowthAnalyticsValidation
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