For a broad class of discrete-time, finite-range interacting particle systems on ℤ, we establish a linear spreading speed and a one-dimensional shape theorem on the event of survival, without assuming monotonicity or attractiveness of the dynamics. The method requires that the system admits a coupling with supercritical oriented percolation on a coarse-grained lattice. The central technical step is an approximate subadditivity property for the hitting times, obtained through a `shifted coupling' that compensates for the absence of monotonicity. As a concrete application, we show that a discrete-time branching annihilating random walk fits into this framework, and consequently exhibits a linear spreading speed.

This article studies large and local large deviations for sums of i.i.d. real-valued random variables in the domain of attraction of an α-stable law, α∈ (0,2], with emphasis on the case α=2. There are two different scenarios: either the deviation is realised via a collective behaviour with all summands contributing to the deviation (a Gaussian scenario), or a single summand is atypically large and contributes to the deviation (a one-big-jump scenario). Such results are known when α∈ (0,2) (large deviations always follow a one big-jump scenario) or when the random variables admit a moment of order 2+δ for some δ>0. We extend these results, including in particular the case where the right tail is regularly varying with index -2 (treating cases with infinite variance in the domain of attraction of the normal law). We identify the threshold for the transition between the Gaussian and the one-big-jump regimes; it is slightly larger when considering local large deviations compared to integral large deviations. Additionally, we complement our results by describing the behaviour of the sum and of the largest summand conditionally on a (local) large deviation, for any α∈ (0,2], both in the Gaussian and in the one-big-jump regimes. As an application, we show how our results can be used in the study of condensation phenomenon in the zero-range process at the critical density, extending the range of parameters previously considered in the literature.

We study coalescent processes conditional on the population pedigree under the exchangeable diploid bi-parental population model of . While classical coalescent models average over all reproductive histories, thereby marginalizing the pedigree, our work analyzes the genealogical structure embedded within a fixed pedigree generated by the diploid Cannings model. In the large-population limit, we show that these conditional coalescent processes differ significantly from their marginal counterparts when the marginal coalescent process includes multiple mergers. We characterize the limiting process as an inhomogeneous (Ψ,c)-coalescent, where Ψ encodes the timing and scale of multiple mergers caused by generations with large individual progeny (GLIPs), and c is a constant rate governing binary mergers. Our results reveal fundamental distinctions between quenched (conditional) and annealed (classical) genealogical models, demonstrate how the fixed pedigree structure impacts multi-locus statistics such as the site-frequency spectrum, and have implications for interpreting patterns of genetic variation among unlinked loci in the genomes of sampled individuals. They significantly extend the results of , which considered a sample of size two under a specific Wright-Fisher model with a highly reproductive couple, and those of , where Kingman coalescent was the limiting process. Our proofs adapt coupling techniques from the theory of random walks in random environments.

We study a discrete-time branching annihilating random walk (BARW) on the $d$-dimensional lattice. Each particle produces a Poissonian number of offspring with mean $\mu$ which independently move to a uniformly chosen site within a fixed distance $R$ from their parent's position. Whenever a site is occupied by at least two particles, all the particles at that site are annihilated. We prove that for any $\mu>1$ the process survives when $R$ is sufficiently large. For fixed $R$ we show that the process dies out if $\mu$ is too small or too large. Furthermore, we exhibit an interval of $\mu$-values for which the process survives and possesses a unique non-trivial ergodic equilibrium for $R$ sufficiently large. We also prove complete convergence for that case.

Consider an advantageous allele that arises in a haploid population of size $N$ evolving in continuous time according to a skewed reproduction mechanism, which generates under neutrality genealogies lying in the domain of attraction of a Beta$(2-\alpha, \alpha)$-coalescent for $\alpha \in (1,2)$. We prove in a setting of moderate selection that the fixation probability $\pi_N$ of the advantageous allele is asymptotically equal to $\alpha^{1/(\alpha-1)} s_N^{1/(\alpha-1)}$ , where $s_N$ is the selection strength of the advantageous allele. Our proof uses duality with a suitable $\Lambda$-ancestral selection graph.

We consider a simple diploid population-genetic model with potentially high variability of offspring numbers among individuals. Specifically, against a backdrop of Wright-Fisher reproduction and no selection, there is an additional probability that a big family occurs, meaning that a pair of individuals has a number of offspring on the order of the population size. We study how the pedigree of the population generated under this model affects the ancestral genetic process of a sample of size two at a single autosomal locus without recombination. Our population model is of the type for which multiple-merger coalescent processes have been described. We prove that the conditional distribution of the pairwise coalescence time given the random pedigree converges to a limit law as the population size tends to infinity. This limit law may or may not be the usual exponential distribution of the Kingman coalescent, depending on the frequency of big families. But because it includes the number and times of big families, it differs from the usual multiple-merger coalescent models. The usual multiple-merger coalescent models are seen as describing the ancestral process marginal to, or averaging over, the pedigree. In the limiting ancestral process conditional on the pedigree, the intervals between big families can be modeled using the Kingman coalescent but each big family causes a discrete jump in the probability of coalescence. Analogous results should hold for larger samples and other population models. We illustrate these results with simulations and additional analysis, highlighting their implications for inference and understanding of multilocus data.

In this work we extend the quenched local limit theorem obtained by the authors in [BBDS23]. More precisely, we consider a directed random walk on the backbone of the supercritical oriented percolation cluster in dimensions d+1 with d≥ 1 being the spatial dimension. In [BBDS23] an annealed local central limit theorem was proven for all d≥ 1 and a quenched local limit theorem under the assumption d≥ 3. Here we show that the latter result also holds for all d ≥ 1.

We consider Beta(2 - alpha, alpha)-coalescents with parameter range 1 < alpha < 2 starting from n leaves. The length l(r)((n)) of order r in the n-Beta(2 - alpha, alpha)-coalescent tree is defined as the sum of the lengths of all branches that carry a subtree with r leaves. We show that for any s is an element of N the vector of suitably centered and rescaled lengths of orders 1 <= r <= s converges in distribution to a multivariate stable distribution as the number of leaves tends to infinity.

We consider random walks in dynamic random environments which arise naturally as spatial embeddings of ancestral lineages in spatial locally regulated population models. In particular, as the main result, we prove the quenched central limit theorem for a random walk in dynamic random environment generated by time reversal of logistic branching random walks in a regime where the population density is sufficiently high. As an important tool we consider as auxiliary models random walks in dynamic random environments defined in terms of the time-reversal of oriented percolation. We show that the quenched central limit theorem holds if the influence of the random medium on the walks is suitably weak. The proofs of the quenched central limit theorems in these models rely on coarse-graining arguments and a construction of regeneration times for a pair of conditionally independent random walks in the same medium, combined with a coupling that relates them to a pair of independent random walks in two independent copies of the medium.

Consider two ancestral lineages sampled from a system of two-dimensional branching random walks with logistic regulation in the stationary regime. We study the asymptotics of their coalescence time for large initial separation and find that it agrees with well known results for a suitably scaled two-dimensional stepping stone model and also with Malécot's continuous-space approximation for the probability of identity by descent as a function of sampling distance. This can be viewed as a justification for the replacement of locally fluctuating population sizes by fixed effective sizes. Our main tool is a joint regeneration construction for the spatial embeddings of the two ancestral lineages.

In the article Quenched large deviation principle for words in a letter sequence, Probab. Theory Relat. Fields 148, no. 3/4 (2010), 403-456 we derived a quenched large deviation principle for the empirical process of words obtained by cutting an i.i.d. sequence of letters according to an independent renewal process. We derived a representation of the associated rate function for stationary word processes in terms of certain specific relative entropies. Our proof of this representation is correct when the mean word length is finite, but is flawed when the mean word length is infinite. In this paper we fix the flaw in the proof. Along the way we derive new representations of the rate function that are interesting in their own right. A key ingredient in the proof is the observation that if the rate function in the annealed large deviation principle is finite at a stationary word process, then the letters in the tail of the long words in this process are typical.

We consider a directed random walk on the backbone of the supercritical oriented percolation cluster in dimensions d+1 with d≥3 being the spatial dimension. For this random walk we prove an annealed local central limit theorem and a quenched local limit theorem. The latter shows that the quenched transition probabilities of the random walk converge to the annealed transition probabilities reweighted by a function of the medium centred at the target site. This function is the density of the unique measure which is invariant for the point of view of the particle, is absolutely continuous with respect to the annealed measure and satisfies certain concentration properties.

When we cut an i.i.d. sequence of letters into words according to an independent renewal process, we obtain an i.i.d. sequence of words. In the annealed large deviation principle (LDP) for the empirical process of words, the rate function is the specific relative entropy of the observed law of words w.r.t. the reference law of words. In the present paper we consider the quenched LDP, i.e., we condition on a typical letter sequence. We focus on the case where the renewal process has an algebraic tail. The rate function turns out to be a sum of two terms, one being the annealed rate function, the other being proportional to the specific relative entropy of the observed law of letters w.r.t. the reference law of letters, with the former being obtained by concatenating the words and randomising the location of the origin. The proportionality constant equals the tail exponent of the renewal process. Earlier work by Birkner considered the case where the renewal process has an exponential tail, in which case the rate function turns out to be the first term on the set where the second term vanishes and to be infinite elsewhere. In a companion paper the annealed and the quenched LDP are applied to the collision local time of transient random walks, and the existence of an intermediate phase for a class of interacting stochastic systems is established.

We consider Beta(2− α, α)-coalescents with parameter range 1 < α < 2 starting from n leaves. The length l (n) r of order r in the n-Beta(2 − α, α)-coalescent tree is defined as the sum of the lengths of all branches that carry a subtree with r leaves. We show that for any s ∈ N the vector of suitably centered and rescaled lengths of orders 1 ≤ r ≤ s converges in distribution to a multivariate stable distribution as the number of leaves tends to infinity.

We study a small open economy with labor, capital accumulation, random death, taxation and a government budget balanced in the long run. We offer methods that provide ordinary differential equations for means and analytical expressions for densities. The latter is achieved by solving stochastic differential equations analytically and deriving the density from this solution. Starting from any distribution, the aggregate distribution converges, both on a transition path towards a steady state and on a transition path towards balanced growth, to a Pareto-distribution. We provide an intuitive economic interpretation for a stationary long-run density with an infinite mean in an economy on a balanced growth path. We also show how government tax policy can lead to non-monotonic links between the equilibrium growth rate of the economy and risk aversion of households.

Let ξ_1, ξ_2, … be i.i.d. non-negative random variables whose tail varies regularly with index -1, let S_n be the sum and M_n the largest of the first n values. We clarify for which sequences x_n→∞ we have ℙ(S_n ≥ x_n) ∼ℙ(M_n ≥ x_n) as n→∞. Outside this regime, the typical size of S_n conditioned on exceeding x_n is not completely determined by the largest summand and we provide an appropriate correction term which involves the integrated tail of ξ_1.

We review recent progress in the understanding of the role of multiple- and simultaneous multiple merger coalescents as models for the genealogy in idealised and real populations with exceptional reproductive behaviour. In particular, we discuss models with `skewed offspring distribution' (or under other non-classical evolutionary forces) which lead in the single locus haploid case to multiple merger coalescents, and in the multi-locus diploid case to simultaneous multiple merger coalescents. Further, we discuss inference methods under the infinitely-many sites model which allow both model selection and estimation of model parameters under these coalescents.

We give a short overview on our work on ancestral lineages in spatial population models with local regulation. We explain how an ancestral lineage can be interpreted as a random walk in a dynamic random environment. Defining regeneration times allows to prove central limit theorems for such walks. We also consider several ancestral lineages in the same population and show for one prototypical example that in one dimension the corresponding system of coalescing walks converges to the Brownian web.

We model the evolution of the number of individuals that are reported to be sick with COVID-19 in Germany. Our theoretical framework builds on a continuous time Markov chain with four states: healthy without infection, sick, healthy after recovery or after infection but without symptoms and dead. Our quantitative solution matches the number of sick individuals up to the most recent observation and ends with a share of sick individuals following from infection rates and sickness probabilities. We employ this framework to study inter alia the expected peak of the number of sick individuals in a scenario without public regulation of social contacts. We also study the effects of public regulations. For all scenarios we report the expected end of the CoV-2 epidemic.