Crossover Behaviour In Driven Cascades

TL;DR

This paper introduces a model explaining how power-law crossover behavior emerges in cascading failure systems, using a stochastic propagation power that influences cascade size distributions and exhibits critical phenomena.

Contribution

The paper presents an analytical model linking propagation power dynamics to crossover behavior in cascade size distributions, revealing how power-law exponents change across regimes.

Findings

Crossover behavior arises from averaging over propagation power distribution.

Analytical exponents for cascade size distribution before and after crossover.

Model captures critical divergence and subcritical constraints in cascading systems.

Abstract

We propose a model which explains how power-law crossover behaviour can arise in a system which is capable of experiencing cascading failure. In our model the susceptibility of the system to cascades is described by a single number, the propagation power, which measures the ease with which cascades propagate. Physically, such a number could represent the density of unstable material in a system, its internal connectivity, or the mean susceptibility of its component parts to failure. We assume that the propagation power follows an upward drifting Brownian motion between cascades, and drops discontinuously each time a cascade occurs. Cascades are described by a continuous state branching process with distributional properties determined by the value of the propagation power when they occur. In common with many cascading models, pure power law behaviour is exhibited at a critical level of propagation power, and the mean cascade size diverges. This divergence constrains large systems to the subcritical region. We show that as a result, crossover behaviour appears in the cascade distribution when an average is performed over the distribution of propagation power. We are able to analytically determine the exponents before and after the crossover.