# Prediction in a driven-dissipative system displaying a continuous phase transition

## TL;DR

This study investigates the predictability of event sizes in a critical system using neural networks, revealing that predictability diminishes near criticality and is limited to large, non-scaling events, implying challenges in forecasting earthquakes.

## Contribution

It demonstrates the limitations of neural network-based prediction in critical systems, especially near phase transitions, and highlights the difficulty of forecasting earthquakes with Gutenberg-Richter scaling.

## Key findings

- Prediction accuracy decreases near criticality.
- Large, non-scaling events are more predictable.
- Earthquake faults with Gutenberg-Richter scaling are hard to forecast.

## Abstract

Prediction in complex systems at criticality is believed to be very difficult, if not impossible. Of particular interest is whether earthquakes, whose distribution follows a power law (Gutenberg-Richter) distribution, are in principle unpredictable. We study the predictability of event sizes in the Olmai-Feder-Christensen model at different proximities to criticality using a convolutional neural network. The distribution of event sizes satisfies a power law with a cutoff for large events. We find that prediction decreases as criticality is approached and that prediction is possible only for large, non-scaling events. Our results suggest that earthquake faults that satisfy Gutenberg-Richter scaling are difficult to forecast.