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Interaction Effect Mapping

Map how factors change one another's effects when combined so interventions are not evaluated only in isolation.

The Diagnostic Story

Symptom: Each component tested well in isolation, but when deployed together the results disappeared or reversed. Stakeholders are citing different evidence because each worked with a different combination of conditions. A high-value element is repeatedly blamed or praised without anyone checking what it was paired with. Small contextual changes produce unexpectedly large outcome shifts that isolated factor analysis cannot explain.

Pivot: Convert isolated factor evaluation into combination-aware evaluation: identify which factors will be used together, represent their possible pairings or bundles, and measure or infer combined effects rather than only individual ones. Classify interaction types — synergy, antagonism, dose-dependence, compatibility — so the map can feed back into design and sequencing decisions.

Resolution: Hidden synergies are discovered and intentionally used; harmful antagonism or interference is detected before broad rollout. Attribution improves because effects are traced to combinations, not isolated components. Design choices become combination-aware, and evidence uncertainty remains visible alongside the interaction map.

Reach for this when you hear…

[product experimentation] “We A/B tested each feature separately, they both won, then we shipped them together and conversion dropped — we needed to test the combination, not just the parts.”

[pharmacology] “Both drugs are well-studied in isolation but no one checked how they interact at these doses, and that's exactly what the adverse event report is showing us now.”

[agricultural systems] “The fertilizer trial showed a yield gain and the irrigation schedule showed a yield gain, but together in our soil type they're competing in a way neither single-factor trial would have predicted.”

Mechanisms / Implementations

  • Factorial Experiment: A factorial experiment is a formal way to test factors and combinations.
  • Pairwise Combination Testing: Pairwise testing examines two-factor combinations when full mapping is too expensive.
  • Interaction Matrix Table: An interaction matrix table is a visible artifact for recording combinations, expected effects, observed effects, confidence levels, and recommended actions.
  • Compatibility Screening: Removes unsafe, invalid, infeasible, or uninterpretable combinations before testing.
  • Dependency Interaction Map: A dependency interaction map shows how services, processes, teams, or resources modify one another's behavior through dependency pathways.
  • Treatment Interaction Analysis: Evaluates whether an intervention's effect changes under different co-treatments, populations, moderators, or conditions.
  • Design of Experiments Protocol: A design of experiments protocol plans factor levels, combination coverage, randomization, blocking, and measurement windows.

Abstractions this archetype builds on — directly (a source ingredient) or as a related pattern. Links follow the typed catalog namespace.

Built directly on (3)

Also references 4 related abstractions

Variants

Narrower or domain-specific specializations that share this archetype's core structure. Recognized variants are established; candidate variants are provisional.

Factorial Interaction Mapping · mechanism family variant · recognized

Maps interaction effects through a planned factorial or fractional-factorial design that estimates main effects and interaction terms.

Pairwise Interaction Mapping · scale variant · recognized

Maps two-factor interactions as a tractable approximation when full combination mapping is infeasible.

Dependency Interaction Mapping · domain variant · recognized

Maps how dependencies among processes, services, resources, or organizational units modify one another's behavior when active together.