Model Checking / Verification
Updated 2026-08-13
INTRODUCTION
English translation pending.
CORE DEFINITION
Model checking in formal methods verifies whether a system satisfies a specification by exploring all reachable states exhaustively, rather than by sampling test cases. In statistics the same name refers to validating whether a fitted model generalizes, using held-out data, residual analysis, and calibration checks. The core proposition in both senses is that a claim about a model should be tested against evidence the model has not already absorbed. The key qualification is that exhaustive checking is bounded by the model's fidelity: it proves properties of the model, not of the system it represents.
SCAFFOLDING EFFECT
Reduce cognitive load
- Spec first: write the property to be verified in precise terms before running any check at all. - Coverage claim: state explicitly whether the check is exhaustive or sample-based. - Held-out test: evaluate generalization on data that the model has genuinely never seen before.
Anchor fast decisions
Exhaustive state exploration removes the possibility that an unvisited case violates the property, which sampling can never guarantee. Statistical validation works by the same logic applied to data: performance on held-out cases estimates how the model behaves on cases it has not seen. In both senses, the value of the result depends on whether the check could have failed.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Model_checkingverified
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