Pairwise equality of stationary probability flow implies stationarity and time-reversal symmetry. A small computation will anchor the general statement before the abstraction takes over.
The mathematical object
A discrete-time Markov chain has transition matrix \(P=(p_{ij})\) and forgets the past after conditioning on the present. Matrix powers \(P^n\) give multi-step transition probabilities.
I keep the defining relation \(\mathsf D\) above the derived relation \(\mathsf C\). This exposes whether cancellation used \(x\ne0\) and whether the conclusion is canonical.
One explicit computation
An explicit case prevents the notation from becoming ceremonial. Every subscript and superscript in the display contributes to the value.
Why the identity matters
The aligned summary deliberately puts the datum and conclusion on different rows. Mathematically, this is the distinction between specifying an object and proving a property of it.
Where it can fail
A stationary distribution need not be unique without irreducibility, and convergence to it can fail without aperiodicity.
With the dependency made explicit, the same pattern can be recognised safely in nearby problems. A changed hypothesis should now be easy to spot.