CausonomyCausonomy
Causonomy noun
The science of how systems fail. For any problem stated within a system that has requirements, the causes that could have produced it form a finite, closed set — one that can be derived before the investigation begins.
The claim is stronger than it first sounds. Not that causes can be classified once found, but that the candidates are fixed by structure before any evidence is gathered. An investigation can therefore know what it has not yet ruled out, and can finish rather than merely stop.
Why this differs from root cause analysis
Conventional problem solving works forwards from evidence: gather what is known, generate hypotheses, test them, and settle when an explanation satisfies. The weakness is not rigour but completeness — nothing in the method accounts for what was never considered. Two competent teams investigating the same failure routinely produce different causes, and neither can say what the other missed.
Causonomy works from structure. Because the admissible causes are derivable, the question shifts from what might have caused this to which of the causes that could have caused this did. Investigation becomes elimination against a known field rather than generation from imagination.
Where it applies
The structure is domain-independent, because it is derived from what it means for a system to have requirements at all — not from any particular industry's content. A shortage in a warehouse, an unperformed inspection, a payment released twice and a sample tested against a superseded standard are different in every operational respect and identical in structure. The domain supplies the vocabulary; it does not supply the logic.
The name. From causa, cause, and -nomy, the ordering or law of a domain — as in astronomy or taxonomy. Causonomy is the ordering of causes: not the study of causation in general, but of how causes are structured and bounded within systems that carry requirements.