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Criterion
Design sheets and interface cards connect to jointly written pseudocode with highlighted unresolved questions.
Architecture
ARID (Active Reviews for Intermediate Designs)
Paper illustration for Future Reality Tree.
Systems Thinking
Future Reality Tree
Paper illustration of Leverage Points with its method-specific working model.
Systems Thinking
Leverage Points
Causal Loop Diagram workspace showing the question, observations, and next decision.
Systems Thinking
Causal Loop Diagram
Purposedifferent
ARID examines how well developers can work with a technical design. It focuses on a part of the system that can still be revised, between the architecture overview and the detailed specification.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.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 Causal Loop Diagram makes feedback, reinforcement, and balance in a system legible. It uncovers side effects and self-reinforcement that stay hidden in linear explanations.
Complexitydifferent
MediumHighHighMedium
Timedifferent
2–4 h review as a planning range; preparation and evaluation additional2-4 hHalf day1-3 h
Participantsdifferent
Designers, developers as users, and facilitation3-83-122-8
Formatdifferent
Workshop + asyncWorkshopWorkshopWorkshop
Outputdifferent
Prioritized development tasks, Jointly produced code or pseudocode, Observed difficulties and open design questions, Proposed design revisionsFuture Reality Tree, Negative Branches, Assumption List, Improved InjectionsLeverage Map, Action StrategyCausal Loop Diagram, Feedback Notes, Leverage Points
Tagsno overlap
ArchitectureEngineeringFeedbackValidationTasks
Theory of ConstraintsSystems thinkingChange
Systems thinkingChangeStrategy
FeedbackSystems thinkingCausalityDynamics
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