Long-range last-passage percolation on the line

TL;DR

This paper investigates the asymptotic behavior of maximum path weights in a directed last-passage percolation model on a random graph, revealing different regimes depending on the finiteness of the second moment of edge weights.

Contribution

It establishes a regenerative structure and laws of large numbers for finite variance weights, and derives scaling laws and distributions for infinite variance weights, connecting to continuous models.

Findings

Finite variance case exhibits a regenerative structure and a strong law of large numbers.

Infinite variance case reveals scaling laws and asymptotic distributions related to continuous last-passage percolation.

Results extend understanding of heavy-tailed weights in long-range percolation models.

Abstract

We consider directed last-passage percolation on the random graph G = (V,E) where V = Z and each edge (i,j), for i < j, is present in E independently with some probability 0 < p <= 1. To every present edge (i,j) we attach i.i.d. random weights v_{i,j} > 0. We are interested in the behaviour of w_{0,n}, which is the maximum weight of all directed paths from 0 to n, as n tends to infinity. We see two very different types of behaviour, depending on whether E[v_{i,j}^2] is finite or infinite. In the case where E[v_{i,j}^2] is finite we show that the process has a certain regenerative structure, and prove a strong law of large numbers and, under an extra assumption, a functional central limit theorem. In the situation where E[v_{i,j}^2] is infinite we obtain scaling laws and asymptotic distributions expressed in terms of a "continuous last-passage percolation" model on [0,1]; these are related to corresponding results for two-dimensional last-passage percolation with heavy-tailed weights obtained by Hambly and Martin.