Filter the matrix with cross-consistency assessment
Identify which value combinations are impossible or contradictory to reduce the space to viable solutions.
Key takeaways
- What it is: Identify which value combinations are impossible or contradictory to reduce the space to viable solutions.
- Why it works: A large morphological matrix typically contains thousands of potential combinations, most of which are either technically impossible, logically contradictory, or wildly impractical. Cross-consistency assessment pairs each value with each value on every other dimension and marks incompatible pairings. This eliminates much of the combinatorial space without value judgments about quality — only about feasibility — leaving a reduced set of coherent, viable combinations to evaluate.
- Evidence: Practitioner experience / self-report; no formal study.
- Avoid: Applying quality judgments ("this combination is unlikely to succeed") during the consistency step rather than only feasibility judgments ("this combination cannot exist") — which reintroduces the intuition bias the method is designed to bypass.
Why it works
A large morphological matrix typically contains thousands of potential combinations, most of which are either technically impossible, logically contradictory, or wildly impractical. Cross-consistency assessment pairs each value with each value on every other dimension and marks incompatible pairings. This eliminates much of the combinatorial space without value judgments about quality — only about feasibility — leaving a reduced set of coherent, viable combinations to evaluate.
How to do it
- 1After building the matrix, take two dimensions at a time and ask: "Can value X on dimension A coexist with value Y on dimension B?"
- 2Mark incompatible pairs; any combination containing an incompatible pair is eliminated.
- 3Repeat for all dimension pairs; the remaining combinations are your viable solution candidates.
What the evidence says
AnecdotalCross-consistency assessment is a formal technique developed within General Morphological Analysis by Tom Ritchey building on Zwicky’s work. It is used in policy analysis and futures research to systematically reduce large combinatorial spaces.
Honest caveat: The technique is established in futures and policy research; its effectiveness compared to other filtering methods in creative applications has not been formally trialed.
- — Ritchey (2011), Wicked Problems — Social Messes, Springer — documents General Morphological Analysis (GMA)
Common mistake
Applying quality judgments ("this combination is unlikely to succeed") during the consistency step rather than only feasibility judgments ("this combination cannot exist") — which reintroduces the intuition bias the method is designed to bypass.
IX Coach guides the cross-consistency check pair by pair and maintains the matrix as combinations are eliminated, so the filtering is systematic rather than intuition-shaped.
Practice this with IX Coach →