Critical Hawkes Processes with Random Fertilities: Stationarity in Law Beyond Infinite Mean Activity

Genuinely critical dynamics have been proposed to organize many natural and social systems, yet exact criticality is usually thought to preclude stationarity because the mean activity diverges. I show that this conclusion is not generally valid for self-exciting Hawkes point processes. At criticality, stationarity in law is controlled not by the mean intensity, but by local finiteness of the infinite-past Poisson-cluster construction. The relevant object is the fixed-window hitting probability $H_T(u)$, the probability that a cluster born at time $-u$ contributes at least one event to a window of length $T$. For memory tails $\mathbb{P}(T>t)\sim t^{-\theta }$ and fertility tails $\mathbb{P}(\kappa >x)\sim x^{-\gamma }$, I prove stationarity for $1<\gamma <2$ and $\theta >\gamma$ via a finite-mean-lifetime criterion. In the finite-memory, finite-variance regime, $H_T(u)$ is asymptotically comparable to the cluster-survival probability, and the exact local-finiteness condition fails. A direct asymptotic analysis of $H_T$ gives the sharper condition $\theta >\gamma -1$ for stationarity to hold in the infinite-fertility-variance regime. Thus broad fertility fluctuations can stabilize critical Hawkes dynamics in law, producing locally finite stationary sample paths despite infinite mean activity.