Jean Bertoin

Consider first a memoryless population model described by the usual branching process with a given mean reproduction matrix on a finite space of types. Motivated by the consequences of atavism in Evolutionary Biology, we are interested in a modification of the dynamics where individuals keep full memory of their forebears and procreation involves the reactivation of a gene picked at random on the ancestral lineage. By comparing the spectral radii of the two mean reproduction matrices (with and without memory), we observe that, on average, the model with memory always grows at least as fast as the model without memory. The proof relies on analyzing a biased Markov chain on the space of memories. The existence of a unique ergodic law for the latter is demonstrated through asymptotic coupling.

Reinforced Galton–Watson processes describe the dynamics of a population where reproduction events are reinforced, in the sense that offspring numbers of forbearers can be repeated randomly by descendants. More specifically, the evolution depends on the empirical offspring distribution of each individual along its ancestral lineage. We are interested here in asymptotic properties of the empirical distributions observed in the population, such as concentration, evanescence and persistence. For this, we incorporate tools from the theory of large deviations to our preceding analysis [4, 5].

Consider first a memoryless population model described by the usual branching process with a given mean reproduction matrix on a finite space of types. Motivated by the consequences of atavism in Evolutionary Biology, we are interested in a modification of the dynamics where individuals keep full memory of their forebears and procreation involves the reactivation of a gene picked at random on the ancestral lineage. By comparing the spectral radii of the two mean reproduction matrices (with and without memory), we observe that, on average, the model with memory always grows at least as fast as the model without memory. The proof relies on analyzing a biased Markov chain on the space of memories, and the existence of a unique ergodic law is demonstrated through asymptotic coupling.

A random walk with counterbalanced steps is a process of partial sums \check S(n)=\check X_{1}+ \cdots + \check X_{n} whose steps \check X_{n} are given recursively as follows. For each n\geq 2 , with a fixed probability p , \check X_{n} is a new independent sample from some fixed law \mu , and with complementary probability 1-p , \check X_{n}= -\check X_{v(n)} counterbalances a previous step, with v(n) a uniform random pick from \{1, \ldots, n-\nobreak 1\} . We determine the asymptotic behavior of \check S(n) in terms of p and the first two moments of \mu . Our approach relies on a coupling with a reinforcement algorithm due to H. A. Simon, and on properties of random recursive trees and Eulerian numbers, which may be of independent interest. The method can be adapted to the situation where the step distribution \mu belongs to the domain of attraction of a stable law.

We determine the distributions of some random variables related to a simple model of an epidemic with contact tracing and cluster isolation. This enables us to apply general limit theorems for super-critical Crump–Mode–Jagers branching processes. Notably, we compute explicitly the asymptotic proportion of isolated clusters with a given size amongst all isolated clusters, conditionally on survival of the epidemic. Somewhat surprisingly, the latter differs from the distribution of the size of a typical cluster at the time of its detection, and we explain the reasons behind this seeming paradox.