Renormalisation of hierarchically interacting Cannings processes
Abstract
The present paper brings a new class of interacting jump processes into focus. We start from a single-colony -process, which arises as the continuum-mass limit of a -Cannings individual-based population model, where is a finite non-negative measure that describes the offspring mechanism. After that we introduce a system of hierarchically interacting -processes, where the interaction comes from migration and reshuffling-resampling based on measures both acting in -blocks of the hierarchical group. We refer to this system as the -process. The dual process of the -process is a spatial coalescent with multi-level block coalescence. For the above system we carry out a full renormalisation analysis in the hierarchical mean-field limit . Our main result is that, in the limit as , on each scale the -block averages of the -process converge to a random process that is a superposition of a -process and a Fleming-Viot process, the latter with a volatility and with a drift of strength towards the limiting -block average. It turns out that is a function of and for all . Thus, it is through the volatility that the renormalisation manifests itself. We discuss the implications of the scaling of for the behaviour on large space-time scales of the -process. We compare the outcome with what is known from the renormalisation analysis of hierarchically interacting Fleming-Viot diffusions, pointing out several new features. We obtain a new classification for when the process exhibits clustering, respectively, exhibits local coexistence. Finally, we show that for finite the same dichotomy between clustering and local coexistence holds as for .
Cite
@article{arxiv.1209.1856,
title = {Renormalisation of hierarchically interacting Cannings processes},
author = {Andreas Greven and Frank den Hollander and Sandra Kliem and Anton Klimovsky},
journal= {arXiv preprint arXiv:1209.1856},
year = {2015}
}
Comments
96 pages, 5 figures; final version; appears in ALEA, Lat. Am. J. Probab. Math. Stat