attribute-mechanism — simulate before you attribute

An observed signature plus a plausible story is not an attribution. A wrong attribution is expensive because it resembles understanding: the wrong quantity gets recalibrated, data gets corrected for an effect that is not present, or later physics is built on a bug. The rule this skill enforces: never state the cause of a non-trivial signature without a simulation, or an equivalent quantitative model, that reproduces the signature and rules the alternatives out.

Two patterns motivate the discipline. Arithmetic that matches the data (alias folding, a harmonic coincidence) can support an attribution that fails at every physically plausible parameter value, while a mechanism nobody proposed reproduces the signature. A minimal exact model that sweeps the candidates against each other separates the two, and converts "does this effect matter here?" from an open question into a boundary with numbers and stated conditions.

The method

1. Pin the signature quantitatively. Extract the numbers that any explanation must reproduce: frequency or period, amplitude (absolute or relative), phase, scaling with a control parameter, onset conditions. A mechanism that matches the kind of feature but not its numbers is not an explanation. Check the extraction chain first, per signal-extraction-pitfalls.md. A signature that is itself an artifact has no cause to attribute.
2. Enumerate candidate mechanisms. List all of them, including the boring ones (miscalibration, drift, analysis artifact) alongside the interesting ones. Write the list down: an unlisted mechanism is never tested. frame-problem's effect inventory is the natural source.
3. Build the minimal model with one knob per mechanism. Not the production simulator: the smallest model that contains only the candidate mechanisms, each behind an explicit, user-controlled knob (a detuning, an amplitude error, a phase scheme, a drift rate). Minimality is what makes the sweep interpretable. In a model carrying unrelated ingredients the sweep cannot isolate which one produced the effect. Exactness over realism: a 2-component exact model beats a 20-component approximate one for attribution.
4. Sweep each knob through plausible values, and through implausible ones: a mechanism that would need a value 10× larger than physically possible is ruled out, which is itself a verdict. Tabulate the signature each mechanism produces: its frequency, amplitude, scaling. Only the mechanisms that reproduce the observed numbers survive. Combinations count as candidates too.
5. Falsify the survivor. Find a limit where the surviving hypothesis makes a sharp, parameter-free prediction: a limiting case where the effect must vanish exactly, a scaling it must obey, or a control measurement where candidates diverge. Check that prediction in the simulation, and on the instrument where that is cheap. An attribution that has survived a falsification attempt is CONFIRMED; one that merely fits is PROVISIONAL. Grade it and state the grade (verify's ladder).

Rules of evidence

• Reasoning forward from "it looks like X" is inadmissible. The test runs backward: does X, simulated at plausible strength, produce this signature with these numbers?
• Arithmetic coincidences are not mechanisms. Alias folding, harmonic matching, and order-of-magnitude numerology generate candidate mechanisms. They never confirm one. Every such argument must be re-derived by the numerical model before it counts.
• The null candidate is always on the list: the signature is an artifact of the extraction or analysis chain, not of the physics. Re-extract with an independent method before attributing anything subtle.
• Negative results are verdicts. "Mechanism X cannot produce this at any plausible strength" is a result. Record each ruled-out mechanism, dated, with the sweep that ruled it out, so it is not re-litigated.
• Validate the minimal model itself against one known limit before trusting its verdicts: the hard-pulse, zero-coupling, or static limit where the answer is known analytically. A minimal model with a bug attributes signatures to mechanisms that are not present.

Output

A short attribution record, in the journal, a findings note, or record-decision if a decision hangs on it. It carries the pinned signature, the candidate table (mechanism → simulated signature → verdict), the surviving mechanism with its falsification test and grade, and what would revisit it. One figure: the observed signature overlaid with the surviving mechanism's simulation and, ideally, the runner-up that failed.

Composition

frame-problem supplies the candidate inventory · dimensional-analysis bounds each mechanism's plausible strength before anything is coded · signal-extraction-pitfalls.md guards step 1 · debug takes over if the survivor is "analysis artifact" · verify grades the final attribution and an independent path recomputes it before promotion · record-decision when a calibration or hardware change follows from it.

Checklist

• Signature pinned as numbers, not adjectives?
• Extraction chain checked: could the signature be an artifact?
• All candidates enumerated in writing, boring ones included?
• Minimal model, one explicit knob per mechanism, validated in a known limit?
• Every candidate swept through plausible strengths and tabulated?
• Survivor falsified in a sharp limit, and graded CONFIRMED only if it passed?
• Ruled-out mechanisms recorded with their sweeps?
• No conclusion resting on un-simulated arithmetic?