Blog / Learn
Learning material on formalization, checked rulebooks, modeling choices, proof assistants, and evidence.
Learning material on formalization, checked rulebooks, modeling choices, proof assistants, and evidence.
Learn
Learning material on formalization, checked rulebooks, modeling choices, proof assistants, and evidence.
Formalization is the discipline of turning a rule, policy, workflow, data model, or mathematical idea into an artifact precise enough to inspect, test, or check.
The learning material should not start with syntax. It should start with the problem: prose often hides choices. A phrase such as “priority access” can mean a queue position, score bonus, reserved quota, or tie-break rule. A sustainability rule can become a spreadsheet, dashboard, allocation decision, or audit claim. If the meaning is unclear, automation only makes the ambiguity official.
The training material presents formality as a ladder, not as a moral ranking:
plain prose
-> structured rule
-> table or schema
-> executable check
-> mathematical specification
-> proof-assistant check
Higher formality gives stronger checks, but it also requires clearer commitments. The right level depends on the question.
A proof assistant checks an artifact. It does not decide whether the artifact captures the right policy, ethical assumption, evidential basis, or domain boundary.
The useful slogan is:
The checker checks what you wrote.
Experts decide whether you wrote the right thing.
That distinction is the same one used in Codex Scribe: AI helps draft and explore; experts approve meaning; the proof assistant checks the formal artifact.
The ontology-shaped abstraction material makes an important point: many formalization failures are modeling failures before they are logic failures.
Before writing rules, a team should ask:
The three practical families are taxonomic, attribute-based, and graph-like abstractions. Most useful models combine them.