Monotonicity properties for Bernoulli percolation on layered graphs -- a Markov chain approach

TL;DR

This paper introduces a Markov chain method to analyze monotonicity properties in Bernoulli percolation on layered graphs, providing partial results and potential for broader applications in percolation theory.

Contribution

The paper presents a novel Markov chain approach to study monotonicity in Bernoulli percolation on layered graphs, establishing results for large layers and suggesting wider applicability.

Findings

Monotonicity holds for layers beyond a certain threshold N

The approach applies to finite layered graphs

Potential for broader percolation problems

Abstract

A layered graph $G^\times$ is the Cartesian product of a graph $G = (V,E)$ with the linear graph $Z$, e.g. $Z^\times$ is the 2D square lattice $Z^2$. For Bernoulli percolation with parameter $p \in [0,1]$ on $G^\times$ one intuitively would expect that $P_p((o,0) \leftrightarrow (v,n)) \ge P_p((o,0) \leftrightarrow (v,n+1))$ for all $o,v \in V$ and $n \ge 0$. This is reminiscent of the better known bunkbed conjecture. Here we introduce an approach to the above monotonicity conjecture that makes use of a Markov chain building the percolation pattern layer by layer. In case of finite $G$ we thus can show that for some $N \ge 0$ the above holds for all $n \ge N$ $o,v \in V$ and $p \in [0,1]$. One might hope that this Markov chain approach could be useful for other problems concerning Bernoulli percolation on layered graphs.