Verification and validation

A model raises two separate questions. This chapter keeps them apart.

Verification: does the model do what the specification says?

Verification asks whether the implementation realises the intended model — the equations, rules, and accounting. It is answered with the code and tests, not with data. Syren supports several techniques:

• Named equation tests. Test each equation or rule against a hand-computed expected value. The macroeconomy example tests its named equations this way.
• Invariants and accounting identities. Assert conservation laws each tick — for example that stocks and flows balance, or that GDP by output and by expenditure agree within tolerance.
• Ordering checks. Assert that systems run in the intended phase order.
• Determinism checks. Assert thread-count invariance and seed divergence, so a refactor cannot silently introduce order sensitivity.

The test suite is built for verification, and the reproducibility guarantees make its checks trustworthy.

Validation: does the model match reality?

Validation asks whether the model's behaviour matches the empirical system it represents. It is answered with data and domain judgement, and is outside what the framework can establish. Syren makes model outputs reproducible and records which parameters and inputs produced them, which is a precondition for credible validation. Whether a calibration is correct, or a result is empirically meaningful, is the modeller's responsibility.

What to claim

State which question a result answers. "The accounting identities hold each tick" is a verification claim the tests can back. "The model reproduces the observed distribution of firm sizes" is a validation claim that needs data and belongs to the study, not the framework. Keep the two separate in documentation and papers.