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ExampleConstruction: AI-generatedVerification: AI-generatedPipeline-generatedprecheck passverified 2026-10-15 (gpt-6-luna, gpt-6.1-sol, and delegated Codex review/repair agents; see completed run evidence)
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An identity dynamics leaves a noncanonical finite preparation unchanged

Physical scope

A two-state finite model with explicitly frozen identity dynamics; it shows that a canonical preparation rule alone supplies no relaxation dynamics and is not an empirical statement.

Example

Let Ω={a,b} with energies Ea=0, Eb=ε>0 joules, and fix T>0 kelvins. The separately adopted canonical law has probabilities proportional to (1,e−βε). For the dynamical comparison, use a dimensionless model-time parameter τ≥0, stipulate the identity transition map Φτ(a)=a, Φτ(b)=b for all τ, and start with the point mass δa.

Facts & Assumptions

Given: A two-state energy model, positive temperature, the canonical preparation rule, and the explicitly stipulated identity dynamics.

[F1]

Finite statistical microstate model: the two microstates and their fixed finite energies define an admissible finite statistical model.

[F2]

Canonical sampling assumption for finite and normalized statistical models: the canonical probabilities are the normalized weights e−βEx; this is an adopted preparation rule and includes no time-evolution equation.

[A1]

The comparison dynamics is the deterministic identity map Φτ for every dimensionless model time τ≥0, and the initial law for that comparison is δa.

Verification

Given: ε>0, T>0, and the stipulated dynamics.

1.1F1F2algebra

Put q=e−βε, so 0<q<1. The canonical law is Pβ(a)=1/(1+q) and Pβ(b)=q/(1+q); in particular it is not δa.

2.1A1step 1.1algebra∎

The identity map preserves the initial point mass at every model time: (Φτ)∗δa=δa. Hence the time-evolved law does not converge to the canonical law, whose probability of b is strictly positive. This is a counterexample to deriving equilibration from the sampling rule alone.

Depends on

Dependency tree · 2 levels

Sources