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.”
When This Archetype Applies¶
Complete catalog groundingAt least one sufficient condition set is fully represented by existing primes or domain-specific abstractions.
Diagnostic problem
A system contains multiple potentially interacting factors, but decisions are being made as if each factor's effect can be understood independently. The hidden risk is that combinations may produce amplified benefit, destructive interference, masked harm, or context-dependent reversals.
What this problem means
The structural problem is hidden non-additivity. The system is being reasoned about as if each factor contributes its own separable effect, but the real outcome depends on the relation among factors.
This creates several recurring errors. Teams overestimate bundles because each part worked alone. They miss synergies because the right pair was never tested. They blame one factor for failure when the problem was the pairing. They generalize evidence from one context to another without noticing that a co-factor has changed.
Interaction Effect Mapping addresses that problem by shifting the unit of evaluation from isolated factors to factor combinations.
Show the applicability expression
Applicability expression3 distinct conditions
groundedpartly groundedopen
3 conditions, all required.
3At least one of theselettered A–C
Any single one of these completes the pattern.
Context-varying effects · grounded · any one of 10
Observed effects vary sharply across contexts or populations.
The source archetype describes the situation as follows: Past results vary sharply across contexts or populations. The normalized requirement above isolates the load-bearing portion used in this condition set.
Negative combined interaction · grounded
Two individually useful elements produce a combined effect below the specified no-interaction baseline.
The source archetype describes the situation as follows: Two useful elements appear to underperform when deployed together. The normalized requirement above isolates the load-bearing portion used in this condition set.
Unmeasured plausible interactions · open
Dependency, compatibility, or dose interactions are plausible but have not been measured against a baseline.
The source archetype describes the situation as follows: There are plausible dependency, compatibility, or dose interactions. The normalized requirement above isolates the load-bearing portion used in this condition set.
Other requirements and context (2)
Why these sit outside the expression
Supporting context — it may accompany or help interpret the situation, but it is not a load-bearing condition in a sufficient diagnostic set.
Application gate — it governs whether applying the archetype is appropriate or material, rather than defining the structural problem itself.
Supporting contextMultiple interventions, components, or variables will be used together.
Use this archetype when multiple interventions, components, policies, actors, incentives, or conditions will operate together and isolated evidence is not enough. In this archetype, the relevant contextual consideration is: Multiple interventions, components, or variables will be used together. It helps interpret the situation or strengthens the practical case for examining the archetype.
Application gateA team is choosing among bundles, sequences, or policy packages.
Coverage
2 of 3 conditions grounded · 1 open.
None of the 1 open conditions sit in the shared core — each falls inside one alternative branch, so grounding any one of them closes only that branch.
Mechanisms / Implementations¶
- Factorial Experiment: Tests the focal factor, potentiating factor, and paired condition so interaction effects can be separated from isolated effects.
- Pairwise Combination Testing: A reduced testing method that checks two-factor combinations to detect likely interaction effects.
- Interaction Matrix Table: A table or grid used to display factor combinations, combined effects, evidence confidence, and recommended actions.
- Compatibility Screening: A checklist, rule set, or review procedure that filters unsafe, invalid, infeasible, or uninterpretable combinations.
- Dependency Interaction Map: A map of how dependencies among components, services, processes, resources, or teams modify one another's effects.
- Treatment Interaction Analysis: A method for evaluating whether an intervention's effect changes under different co-treatments, conditions, populations, or moderators.
- Design of Experiments Protocol: A planning protocol that determines which factors, levels, combinations, assignment rules, and measurement windows will be used to detect interaction effects efficiently.
Related Abstractions¶
Abstractions this archetype builds on — directly (a source ingredient) or as a related pattern. Links follow the typed catalog namespace.
Built directly on (3)
- Factorial Design: Multiple variables tested together.
- Relation: Describes associations or dependencies.
- Synergy and Antagonism: Amplified or diminished effects.
Also references 4 related abstractions
- Causality: Cause-effect relationships.
- Composition: Arranges components into a cohesive whole.
- Cross-Impact Analysis: Interacting trends.
- Effect Size: Magnitude of effect.
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.
Editorial Notes¶
Problem Classification¶
Classification: Representation, Classification & Model Misfit → Relation, Interaction & Multicausal Structure
Problem kernel: factor interactions are omitted from additive decisions
Rationale: Components can amplify, suppress, or reverse one another, so isolated effect estimates do not represent combined causal structure.
Independent corroboration: The earliest necessary condition in the frozen evidence is: A system contains multiple potentially interacting factors, but decisions are being made as if each factor's effect can be understood independently. That is a relation interaction and multicausal structure problem because Object-centered or additive descriptions hide direction, composition, interaction, nonlocal influence, and multiple causal pathways among entities.
Review outcome: Independent reviewer agreement; high confidence.