English

Stochastic flows in the Brownian web and net

Probability 2013-05-29 v2

Abstract

Certain one-dimensional nearest-neighbor random walks in i.i.d. random space-time environments are known to have diffusive scaling limits. In the continuum limit, the random environment is represented by a `stochastic flow of kernels', which is a collection of random kernels that can be loosely interpreted as the transition probabilities of a Markov process in a random environment. The theory of stochastic flows of kernels was introduced by Le Jan and Raimond, who showed that each such flow is characterized by its n-point motions. We focus on a class of stochastic flows of kernels with Brownian n-point motions which, after their inventors, will be called Howitt-Warren flows. We give a graphical construction of general Howitt-Warren flows, where the underlying random environment takes on the form of a suitably marked Brownian web. Alternatively, we show that a special subclass of the Howitt-Warren flows can be constructed as random flows of mass in a Brownian net. Using these constructions, we prove some new results for the Howitt-Warren flows. In particular, we show that the kernels spread with a finite speed and have a locally finite support at deterministic times if and only if the flow is embeddable in a Brownian net. We show that the kernels are always purely atomic at deterministic times, but with the exception of a special subclass known as the erosion flows, exhibit random times when the kernels are purely non-atomic. We moreover prove ergodic statements for a class of measure-valued processes induced by the Howitt-Warren flows. Along the way, we also prove some new results for the Brownian web and net.

Keywords

Cite

@article{arxiv.1011.3895,
  title  = {Stochastic flows in the Brownian web and net},
  author = {Emmanuel Schertzer and Rongfeng Sun and Jan M. Swart},
  journal= {arXiv preprint arXiv:1011.3895},
  year   = {2013}
}

Comments

Revised version. To appear in Memoirs of the American Mathematical Society

R2 v1 2026-06-21T16:44:59.208Z