Experience Verification
Experience Verification determines whether a candidate model is sufficiently supported, coherent, and bounded to be released for future use within its declared scope.
Why Verification Is Necessary
Human internal models can be incomplete or biased. Written sources can conflict. LLM-generated structures can be plausible but wrong. Reuse magnifies both value and error.
A defined verification process determines whether a model is sufficiently supported for its declared scope; the confidence of the person or system that proposed it is not a substitute for that support. Verification cannot create normative authority. Where a rule is binding, its force comes from the governing source, while verification checks that the model represents that constraint correctly and preserves the source relationship.
Evidence
Every material model claim should have an identifiable basis appropriate to its role and declared scope. Evidence connects model structure to the sources and observations that support, constrain, or define it and distinguishes supported meaning from plausible reconstruction.
Evidence quality and source role matter. Applicable binding laws, regulations, mandatory standards, contracts, and governing specifications have a different normative role from examples, commentary, marketing material, research used only for context, or informal descriptions.
Source Authority
Verification respects source authority when evidence conflicts. The process must distinguish which source can establish a binding constraint, which source is explanatory, and which source is merely illustrative within the relevant scope. Authority comes from the source's governing status in the relevant context, not from the confidence with which a claim is generated. The model records the relationship between that governing source and the rule or constraint it represents.
Structural Integrity
Verification tests the model structure against its declared scope and supporting or governing sources. Are required states present? Are transitions supported? Are conditions attached to the appropriate actions? Are results defined? Are important facts or constraints missing? Do relationships remain coherent across the model?
Integrity means that the model preserves a consistent structure rather than accumulating disconnected correct-looking fragments.
Readiness
A candidate model can be incomplete without being useless. Verification should state readiness explicitly. A model may be ready for a defined scope, require additional sources, contain unresolved conflicts, or lack facts needed for a class of decisions.
Readiness turns uncertainty into a visible model property rather than forcing a premature yes-or-no judgment.
Risk
The cost of a model error increases with reuse. A mistake in one person's interpretation affects one decision. The same mistake in a widely reused model can affect many applications. Verification effort should therefore be proportional to the consequence, reach, and degree of reliance placed on the model.
Auditability
A reusable model should be inspectable after release. Reviewers should be able to identify its scope, version, supporting evidence, unresolved issues, important changes, and the governing sources that establish any binding rules it represents.
Auditability makes reuse accountable without requiring every user to reconstruct the model from the original corpus.
Continuous Verification
Models evolve because domains evolve. New source versions, changed standards, new evidence, and discovered structural defects can trigger review and release of a new model version.
Experience Verification determines whether a reconstructed candidate model is sufficiently supported, inspectable, and bounded to qualify for release as a reusable asset for its declared scope.