Exact solution for a sample space reducing stochastic process

Abstract: Stochastic processes wherein the size of the state space is changing as a function of time offer models for the emergence of scale-invariant features observed in complex systems. I consider such a sample-space reducing (SSR) stochastic process that results in a random sequence of strictly decreasing integers ${x(t)}$, $0\le t \le \tau$, with boundary conditions $x(0) = N$ and $x(\tau)$ = 1. This model is shown to be exactly solvable: $\mathcal{P}N(\tau)$, the probability that the process survives for time $\tau$ is analytically evaluated. In the limit of large $N$, the asymptotic form of this probability distribution is Gaussian, with mean and variance both varying logarithmically with system size: $\langle \tau \rangle \sim \ln N$ and $\sigma{\tau}^{2} \sim \ln N$. Correspondence can be made between survival time statistics in the SSR process and record statistics of i.i.d. random variables.