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For $i\ge 1$, the total holding rate is $(\lambda +1)2^i$, and the jump chain moves upward and downward with probabilitieswhile it moves from zero to one with probability one. Thus it is a reflected nearest-neighbour random walk. Recurrence is unaffected by the holding times, so it is recurrent exactly when $p\le q$. In the given range,For $\lambda >1$ the jump chain is transient.

The detailed-balance equations arewhere the formula also covers the boundary $i=0$. Hence every candidate invariant measure hasAt $\lambda =1$ this is summable and the chain is nonexplosive, givingFor $\lambda =2$ it is not summable. When $1<\lambda <2$, it is a summable formal solution of $\pi Q=0$, but the chain explodes, so it is not an invariant probability for the minimal process; this is precisely the caveat in invariant distribution of an explosive chain. Consequently

It remains to classify explosion. At $\lambda =1$, the recurrent jump chain visits zero infinitely often. The holding time at zero has rate one, so nonexplosion from recurrent visits to a slow state shows that the accumulated time is infinite almost surely.

For $\lambda >1$, let $V_i$ be the total number of visits of the transient jump chain to $i$. Using the stated uniform visit bound and writing $q_i=(\lambda +1)2^i$ for $i\ge 1$,because ${\sum }_i{2}^{-i}<\infty$. Conditional on the jump path, this is the expected sum of all holding times, so the total lifetime is finite almost surely. This is explosion of an upward-biased walk with geometrically increasing rates. Therefore

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 $e^{-cN}$.

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.