The Science of Causonomy

Causonomy is a formal theory of failure and causation in normative systems — systems organised to bring about required results. A production line, a hospital ward, a supply chain and a software platform are all normative systems: each exists to produce a Positive Outcome, and each can therefore fail in a definable way. A Negative Outcome is a gap between what the process warranted and what occurred.

Six universal activities

Underneath domain vocabulary, every process is composed of six activities and only six: Store (hold through time), Move (change position), Acquire (bring across the boundary), Release (send across the boundary), Transform (change the form itself), and Check (evaluate against a criterion). Domain diversity is combinatorial — a small alphabet with unbounded expression. The jargon is clothing; the process underneath is universal.

A finite grammar of deviation

Each activity can deviate in a finite number of ways — modes concerning the activity itself (existence, magnitude, timing) and modes concerning the forms it handles (structure and quantity). Crossing activities with deviation modes yields a closed space of Negative Outcomes: a complete map of how normative processes can fail, established prior to any particular domain. The published foundational work derives this closure formally and states the conditions under which it would be refuted.

The Full Problem Statement

Diagnosis begins with formulation. An everyday complaint — “the delivery was wrong” — contains several structurally different problems. Causonomy resolves it into one Full Problem Statement: which activity, which deviation, of what exactly. A fixed sequence of intake questions performs the resolution in minutes.

Derived, complete causation

Because nothing in a process appears from nowhere — every form present was brought by an activity — causation has a fixed alternating structure, and each activity’s outcome passes through a small, fixed set of success terms. Walking those terms along the provenance chain derives the complete set of admissible causes for a stated problem. Completeness is what makes evidence decisive: eliminating candidates from a complete set proves the survivor.

Execution and governance

Causes arise in the execution layer, where work is done. Root causes live in the governing layer — the standing system of definitions, plans, limits, permissions and checks under which the work runs — because only a change there closes the route by which a failure travels. Root-cause analysis is the same analysis, applied one level up.