The Single Big Jump Principle in Physical Modelling

TL;DR

This paper extends the big jump principle to complex stochastic processes with correlations and disorder, enabling better prediction of rare events in systems with heavy-tailed distributions.

Contribution

It generalizes the big jump principle to account for correlations, cutoffs, and memory, broadening its applicability to complex physical models.

Findings

Extended the big jump principle to correlated and disordered systems.

Successfully predicted rare events in Le9vy walks and laser cooling models.

Provided a framework for risk estimation in complex processes.

Abstract

The big jump principle is a well established mathematical result for sums of independent and identically distributed random variables extracted from a fat tailed distribution. It states that the tail of the distribution of the sum is the same as the distribution of the largest summand. In practice, it means that when in a stochastic process the relevant quantity is a sum of variables, the mechanism leading to rare events is peculiar: instead of being caused by a set of many small deviations all in the same direction, one jump, the biggest of the lot, provides the main contribution to the rare large fluctuation. We reformulate and elevate the big jump principle beyond its current status to allow it to deal with correlations, finite cutoffs, continuous paths, memory and quenched disorder. Doing so we are able to predict rare events using the extended big jump principle in L\'evy walks, in a model of laser cooling, in a scattering process on a heterogeneous structure and in a class of L\'evy walks with memory. We argue that the generalized big jump principle can serve as an excellent guideline for reliable estimates of risk and probabilities of rare events in many complex processes featuring heavy tailed distributions, ranging from contamination spreading to active transport in the cell.