Predictability of Self-Organizing Systems

TL;DR

This paper investigates the predictability of large events in self-organizing systems, demonstrating that spatial activity patterns serve as effective precursors, outperforming traditional temporal measures across various models.

Contribution

It introduces a novel precursor based on spatial distribution of activity for predicting large events in self-organizing systems, validated across multiple models.

Findings

Spatial activity patterns outperform temporal measures as precursors.

Detectable correlations exist between small precursory events and large events.

The new spatial precursor improves forecasting accuracy in models.

Abstract

We study the predictability of large events in self-organizing systems. We focus on a set of models which have been studied as analogs of earthquake faults and fault systems, and apply methods based on techniques which are of current interest in seismology. In all cases we find detectable correlations between precursory smaller events and the large events we aim to forecast. We compare predictions based on different patterns of precursory events and find that for all of the models a new precursor based on the spatial distribution of activity outperforms more traditional measures based on temporal variations in the local activity.