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Strong Local Survival of Branching Random Walks is Not Monotone

Part of: Markov processes

Published online by Cambridge University Press:  22 February 2016

Daniela Bertacchi  and
Fabio Zucca
Daniela Bertacchi*
Affiliation:
Università di Milano-Bicocca
Fabio Zucca*
Affiliation:
Politecnico di Milano
*
∗ Postal address: Dipartimento di Matematica e Applicazioni, Università di Milano-Bicocca, via Cozzi 53, 20125 Milano, Italy. Email address: [email protected]
∗∗ Postal address: Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy. Email address: [email protected]
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Abstract

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In this paper we study the strong local survival property for discrete-time and continuous-time branching random walks. We study this property by means of an infinite-dimensional generating function G and a maximum principle which, we prove, is satisfied by every fixed point of G. We give results for the existence of a strong local survival regime and we prove that, unlike local and global survival, in continuous time, strong local survival is not a monotone property in the general case (though it is monotone if the branching random walk is quasitransitive). We provide an example of an irreducible branching random walk where the strong local property depends on the starting site of the process. By means of other counterexamples, we show that the existence of a pure global phase is not equivalent to nonamenability of the process, and that even an irreducible branching random walk with the same branching law at each site may exhibit nonstrong local survival. Finally, we show that the generating function of an irreducible branching random walk can have more than two fixed points; this disproves a previously known result.

Keywords

Branching random walkbranching processstrong local survivalrecurrencegenerating functionmaximum principle

MSC classification

Primary: 60J05: Discrete-time Markov processes on general state spaces
Secondary: 60J80: Branching processes (Galton-Watson, birth-and-death, etc.)
Type
General Applied Probability
Information
Advances in Applied Probability , Volume 46 , Issue 2 , June 2014 , pp. 400 - 421
Copyright
© Applied Probability Trust 

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