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Michel Benaim

Publications and source records attributed to Michel Benaim.

18 recordsLinked to original sources
arXiv2024

Dispersal-induced growth or decay in a time-periodic environment. The case of reducible migration matrices

This paper is a follow-up to a previous work where we considered populations with time-varying growth rates living in patches and irreducible migration matrix between the patches. Each population, when isolated, would become extinct. Dispersal-induced growth (DIG) occurs when the populations are able to persist and grow exponentially when dispersal among the populations is present. In this paper, we consider the situation where the migration matrix is not necessarily irreducible. We provide a mathematical analysis of the DIG phenomenon, in the context of a deterministic model with periodic variation of growth rates and migration. Our results apply in the case, important for applications, where there is migration in one direction in one season and in the other direction in another season. We also consider dispersal-induced decay (DID), where each population, when isolated, grows exponentially, while populations die out when dispersal between populations is present.

math.DS↗
arXiv2024

Regularity of invariant densities for random switching between two linear odes in Rd

In a paper entitled singularities of invariant densities for random switching between two linear odes in 2D, Bakhtin et al [5], consider a Markov process obtained by random switching between two stable linear vector fields in the plane and characterize the singularities of the invariant density in terms of the switching and contraction rates. This paper considers a generalization of this model obtained by random switching between two stable linear vector fields in Rd and provides sufficient conditions ensuring that the invariant distribution is absolutely continuous and has a Cr density. In dimension greater than 3 it provides, to the best of our knowledge, the first fully non-elliptic example of random switching for which quantitative conditions guaranteeing smoothness of the invariant density can be proved.

math.PR↗
arXiv2023

When can a population spreading across sink habitats persist ?

We consider populations with time-varying growth rates living in sinks. Each population, when isolated, would become extinct. Dispersal-induced growth (DIG) occurs when the populations are able to persist and grow exponentially when dispersal among the populations is present. We provide a mathematical analysis of this surprising phenomenon, in the context of a deterministic model with periodic variation of growth rates and non-symmetric migration which are assumed to be piecewise continuous. We also consider a stochastic model with random variation of growth rates and migration. This work extends existing results of the literature on the DIG effects obtained for periodic continuous growth rates and time independent symmetric migration.

math.DS↗
arXiv2023

Stochastic Persistence

Let $(X_t)_{t \geq 0}$ be a continuous time Markov process on some metric space $M,$ leaving invariant a closed subset $M_0 \subset M,$ called the {\em extinction set}. We give general conditions ensuring either "Stochastic persistence" (Part I) : Limit points of the occupation measure are invariant probabilities over $M_+ = M \setminus M_0;$ or "Extinction" (Part II) : $X_t \rightarrow M_0$ a.s. In the persistence case we also discuss conditions ensuring the a.s convergence (respectively exponential convergence in total variation) of the occupation measure (respectively the distribution) of $(X_t)$ toward a unique probability on $M_+.$ These results extend and generalize previous results obtained for various stochastic models in population dynamics, given by stochastic differential equations, random differential equations, or pure jump processes.

math.PR↗
arXiv2017

Stochastic approximation of quasi-stationary distributions on compact spaces and applications

In the continuity of a recent paper ([6]), dealing with finite Markov chains, this paper proposes and analyzes a recursive algorithm for the approximation of the quasi-stationary distribution of a general Markov chain living on a compact metric space killed in finite time. The idea is to run the process until extinction and then to bring it back to life at a position randomly chosen according to the (possibly weighted) empirical occupation measure of its past positions. General conditions are given ensuring the convergence of this measure to the quasi-stationary distribution of the chain. We then apply this method to the numerical approximation of the quasi-stationary distribution of a diffusion process killed on the boundary of a compact set and to the estimation of the spectral gap of irreducible Markov processes. Finally, the sharpness of the assumptions is illustrated through the study of the algorithm in a non-irreducible setting.

math.PR↗
arXiv2016

Generic Behaviour of Strongly Reinforced Polya Urns : Convergence and Stability

We consider, as proposed and studied in Hofstad et.\ al.\ \cite{HHKR}, a class of graph-based "interacting urn"-type Polya urn model inspired by neuronal processing in the brain where a signal enters the brain at some (randomly) chosen neuron and is transmitted to a (random) single neighbouring neuron with a probability depending on the relative `efficiency' of the synapses connecting the neurons, and in doing so the efficiency of the utilized synapse is improved/reinforced. We study the structures (or architectures) and relative efficiency of the neuronal networks that can arise from repeating this process a very large number of times in a "strong reinforcement regime". Under the most general conditions, we prove in the affirmative a part of the main open conjecture in \cite{HHKR} i.e. the zero probability of convergence of the corresponding "urn process" to any 'unstable' equilibrium. Under very generic conditions, i.e., for an open and dense subset of parameters with full measure, we also prove the full open conjecture by showing the finiteness of the equilibrium set and hence the unit probability of convergence of the "urn process" to some 'stable' equilibrium.

math.PR↗
arXiv2015

Self-repelling diffusions via an infinite dimensional approach

In the present work we study self-interacting diffusions following an infinite dimensional approach. First we prove existence and uniqueness of a solution with Markov property. Then we study the corresponding transition semigroup and, more precisely, we prove that it has Feller property and we give an explicit form of an invariant probability of the system.

math.PR↗
arXiv2014

On Gradient like Properties of Population games, Learning models and Self Reinforced Processes

We consider ordinary differential equations on the unit simplex of $\RR^n$ that naturally occur in population games, models of learning and self reinforced random processes. Generalizing and relying on an idea introduced in \cite{DF11}, we provide conditions ensuring that these dynamics are gradient like and satisfy a suitable "angle condition". This is used to prove that omega limit sets and chain transitive sets (under certain smoothness assumptions) consist of equilibria; and that, in the real analytic case, every trajectory converges toward an equilibrium. In the reversible case, the dynamics are shown to be $C^1$ close to a gradient vector field. Properties of equilibria -with a special emphasis on potential games - and structural stability questions are also considered.

math.DS↗
arXiv2013

A generalized Polya's urn with graph based interactions

Given a finite connected graph G, place a bin at each vertex. Two bins are called a pair if they share an edge of G. At discrete times, a ball is added to each pair of bins. In a pair of bins, one of the bins gets the ball with probability proportional to its current number of balls raised by some fixed power a>0. We characterize the limiting behavior of the proportion of balls in the bins. The proof uses a dynamical approach to relate the proportion of balls to a vector field. Our main result is that the limit set of the proportion of balls is contained in the equilibria set of the vector field. We also prove that if a<1 then there is a single point v=v(G,a) with nonzero entries such that the proportion converges to v almost surely. A special case is when G is regular and a is at most 1. We show e.g. that if G is non-bipartite then the proportion of balls in the bins converges to the uniform measure almost surely.

math.PR↗
arXiv2011

On Mean Field Convergence and Stationary Regime

Assume that a family of stochastic processes on some Polish space $E$ converges to a deterministic process; the convergence is in distribution (hence in probability) at every fixed point in time. This assumption holds for a large family of processes, among which many mean field interaction models and is weaker than previously assumed. We show that any limit point of an invariant probability of the stochastic process is an invariant probability of the deterministic process. The results are valid in discrete and in continuous time.

math.DS↗
arXiv2009

Self-interacting diffusions IV: Rate of convergence

Self-interacting diffusions are processes living on a compact Riemannian manifold defined by a stochastic differential equation with a drift term depending on the past empirical measure of the process. The asymptotics of this measure is governed by a deterministic dynamical system and under certain conditions it converges almost surely towards a deterministic measure (see Benaïm, Ledoux, Raimond (2002) and Benaïm, Raimond (2005)). We are interested here in the rate of this convergence. A central limit theorem is proved. In particular, this shows that greater is the interaction repelling faster is the convergence.

math.PR↗
arXiv2008

A class of non homogeneous self interacting random processes with applications to learning in games and vertex-reinforced random walks

Using an approximation by a set-valued dynamical system, this paper studies a class of non Markovian and non homogeneous stochastic processes on a finite state space. It provides an unified approach to simulated annealing type processes. It permits to study new models of vertex reinforced random walks and new models of learning in games including Markovian fictitious play.

math.PR↗
arXiv2007

Effective resistance on random electrical networks

This paper has been withdrawn. See v1 still available to understand the problem: Proposition 2.2 is false. The error in the proof is in claim (3). Then, the whole paper collapses. We do not have any correction for now. We apologize to everyone.

math.PR↗
arXiv2006

A modified Poincare inequality and its application to First Passage Percolation

We extend a Gaussian functional inequality to a countable product of Gaussian measures. This inequality improves on the classical Poincare inequality for Gaussian measures. As an application, we prove that First Passage Percolation has sublinear variance when the edge times distribution belongs to a wide class of continuous distributions, including the exponential one. This extends a result by Benjamini, Kalai and Schramm, valid for positive Bernoulli edge times.

math.PR↗
arXiv2004

Generalized Urn Models of Evolutionary Processes

Generalized Polya urn models can describe the dynamics of finite populations of interacting genotypes. Three basic questions these models can address are: Under what conditions does a population exhibit growth? On the event of growth, at what rate does the population increase? What is the long-term behavior of the distribution of genotypes? To address these questions, we associate a mean limit ordinary differential equation (ODE) with the urn model. Previously, it has been shown that on the event of population growth, the limiting distribution of genotypes is a connected internally chain recurrent set for the mean limit ODE. To determine when growth and convergence occurs with positive probability, we prove two results. First, if the mean limit ODE has an ``attainable'' attractor at which growth is expected, then growth and convergence toward this attractor occurs with positive probability. Second, the population distribution almost surely does not converge to sets where growth is not expected

math.PR↗
arXiv2003

Self-Interacting Diffusions : Symmetric Interactions

Let $M$ be a compact Riemannian manifold. A {\em self-interacting diffusion} on $M$ is a stochastic process solution to $$dX_t = dW_t(X_t) - \frac{1}{t}(\int_0^t \nabla V_{X_s}(X_t)ds)dt$$ where $\{W_t\}$ is a Brownian vector field on $M$ and $V_x(y) = V(x,y)$ a smooth function. Let $μ_t = \frac{1}{t} \int_0^t δ_{X_s} ds$ denote the normalized occupation measure of $X_t$. We prove that, when $V$ is symmetric, $μ_t$ converges almost surely to the critical set of a certain nonlinear free energy functional $J$. Furthermore, $J$ has generically finitely many critical points and $μ_t$ converges almost surely toward a local minimum of $J.$ Each local minimum having a positive probability to be selected.

math.PR↗