What Causonomy is
Every organisation runs processes that exist to bring something about — and every failure is a gap between what a process warranted and what happened. Causonomy studies that gap formally. It shows that underneath their vocabulary, all processes are built from a small set of universal activities; that the ways those activities can go wrong form a finite, closed set; and that from a correctly stated problem, the possible causes can be derived rather than brainstormed.
The result: failure analysis in which two analysts, given the same facts, reach the same problem statement and the same complete candidate set — and in which eliminating candidates by evidence is conclusive, because the list was complete to begin with.
Who this research speaks to
Reliability & quality engineering
FMEA, root-cause analysis and incident review share a documented weakness: results depend on who is in the room. Causonomy replaces brainstormed failure lists with a derived, complete candidate set — making analyses reproducible and elimination conclusive.
AI research
Machine learning learns from deviation — the gap between output and expectation — yet transmits it as a single untyped number. Causonomy offers a closed type system for that signal, and a formal account of the norms every learning system presupposes.
Philosophy of science
A live test case for a priori structure: can the space of failure be closed by derivation rather than collected from experience? The programme is Kantian in method and Popperian in discipline — every closure claim is published with its refutation conditions.
Management theory
Management has practice, evidence and institutions — but no formal object. Causonomy proposes one: the normative system, whose governance reduces to a finite grammar. Not the scientisation of managerial practice; the science of the substrate practice works on.