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Path entropy production, fluctuation symmetry and work identities — Examples

1 · Prerequisites

2 · Summary

3 · Logical flowchart

4 · Definitions, postulates, theorems and proofs

ExampleConstruction: AI-adaptedVerification: AI-generatedverified 2026-10-22 (gpt-6.1-sol)Open item page →

Entropy production on a finite biased ring

Example

Let N≥3 be an integer and let the state space be the ring E=Z/NZ. From state i, let the constant clockwise rate to i+1 be a>0 and the counterclockwise rate to i−1 be b>0, with all other off-diagonal rates zero. Start the finite-state process in the uniform law πi=1/N. If N+ and N− denote the numbers of clockwise and counterclockwise jumps on [0,T], then π is stationary and, almost surely, the stationary path entropy is

ST=(N+−N−)log⁡(a/b).

Its mean is E[ST]=T(a−b)log⁡(a/b)≥0. Reversibility here means detailed balance with respect to the stationary law; it holds exactly when a=b. For every finite T≥0, this ring also satisfies the exact measure fluctuation relation μT(ds)=esμT(−ds), where μT is the law of ST. If a,b are physical rates, they have units of inverse time; aT,bT and ST are dimensionless.

Facts & Assumptions

Given: The ring size N≥3, positive rates a,b, finite duration T≥0, and uniform initial law in the Example.

[F1]

Finite-state continuous-time Markov jump processes with bounded rates: from each state i, the only transitions are i→i+1 at rate a and i→i−1 at rate b.

[F2]

Forward equation and stationary laws for finite-state rate processes: if a probability row vector π satisfies πQ=0 for a constant rate matrix, then the process started in π has law π at every time.

[F3]

Expected directed jump counts for a finite-state rate process: for distinct states i,j, E[Nij(T)]=∫0TP(Xt=i)qij dt.

[F4]

Stationary path entropy for a finite-state jump process: each jump i→j contributes log⁡(πiqij/(πjqji)) to ST.

[F5]

Finite-time fluctuation identity for stationary path entropy: for a finite irreducible chain with positive stationary law and symmetric support, E[e−ST]=1 and μT(ds)=esμT(−ds) as measures.

[A1]

The Axiom of Countable Choice (ACω): Countable Choice, used by the Lebesgue jump-path construction underlying [F3] and [F5].

Verification

Given: The finite ring and the suppliers [F1]–[F6].

1.1F1F2givenalgebra

The total rate leaving every state is a+b. Each state also receives total stationary flow (a+b)/N from its two neighbors, so the uniform row vector satisfies πQ=0. By [F2], starting the process in π makes it stationary. The condition N≥3 ensures i+1 and i−1 are distinct states.

1.2F4givenalgebra

By [F1] and [F4], almost surely every jump is clockwise or counterclockwise. A clockwise jump i→i+1 contributes log⁡((πia)/(πi+1b))=log⁡(a/b), while a counterclockwise jump contributes log⁡((πib)/(πi−1a))=−log⁡(a/b). Summing these almost-sure increments gives ST=(N+−N−)log⁡(a/b) almost surely, including the no-jump case when both counts are zero.

1.3F1givenalgebra

Detailed balance on an edge requires πia=πi+1b, which under the uniform law is equivalent to a=b. If a=b, this equality holds on every edge; nonedges have zero rate in both directions. Hence detailed balance holds exactly in the symmetric-rate case.

2.1F3F4F6step 1.1step 1.2algebra

By [F3] and stationarity from step 1.1, E[N+]=∑iE[Ni,i+1]=∑i∫0T1Na dt=aT,E[N−]=∑iE[Ni,i−1]=∑i∫0T1Nb dt=bT. Thus step 1.2 gives E[ST]=(aT−bT)log⁡(a/b)=T(a−b)log⁡(a/b). Since log⁡ is strictly increasing by [F6], a−b and log⁡(a/b) have the same sign, so their product is nonnegative.

3.1A1F1F2F5step 1.1∎

The ring is irreducible because both rates are positive and repeated clockwise jumps connect every pair of states. It has positive stationary law π and symmetric support because every edge has both rates positive. Therefore [F5] applies and gives the stated exact finite-time fluctuation relation, with no assumption of a probability density for ST.

5 · Examples, experiments and counterexamples

None yet.

Sources