The research programme
Every organisation runs processes that exist to bring something about, and every failure is a gap between what a process warranted and what happened. The Institute studies that gap formally: how the universal activities beneath any process are derived, what closure of the deviation grammar requires, and under what conditions the closure claim would fail. Causonomy is defined at causonomy.org.
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.