Symmetric stochastic attrition process ($(a,b)\to (a-1,b)\text{ at rate }\kappa b,(a,b)\to (a,b-1)\text{ at rate }\kappa a$)

This finite-state Markov jump process models two opposing populations, each suffering losses at a rate proportional to the surviving opposing population. Both coordinate axes are absorbing. Its embedded jump chain removes one individual at each step, with probabilities $b/(a+b)$ and $a/(a+b)$. The corresponding deterministic equations $\dot{a}=-\kappa b$, $\dot{b}=-\kappa a$ conserve $a^2-b^2$; finite stochastic populations satisfy a near-invariant instead.

In the jump chain of the symmetric stochastic attrition process, the two possible increments of $D$ are $-2A+1$ and $2B-1$. Their mean is the displayed quantity, bounded by one in absolute value. Subtracting these conditional means produces a martingale. If both initial populations are at most $N$, it has at most $2N$ nonzero increments, each bounded by $4N$. Thus Azuma-Hoeffding inequality controls changes of order $N^2$ with exponentially small probability of order $e^{-cN}$.

Start the symmetric stochastic attrition process at $(N,\lfloor \alpha N\rfloor )$, with $0<\alpha <1$, and let $X^N$ be the final first-population size divided by $N$. The bound holds for all sufficiently large $N$, depending on $\alpha ,\delta >0$. At absorption, one coordinate is zero. If the first survives, the terminal squared difference is $N^2(X^N{)}^2$; if it is extinguished, that difference is nonpositive. In either case the indicated deviation forces an error of at least $\sqrt{1-{\alpha }^2}\delta N^2$ from the limiting squared invariant. The initial rounding error and total conditional drift are only $O(N)$. Apply Azuma-Hoeffding inequality to the centered squared-population near-invariant for stochastic attrition, using its $2N$ increments bounded by $4N$, to obtain the displayed exponent.