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Related Experiment Video

Updated: Aug 12, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Estimating predictability of a dynamical system from multiple samples of its evolution.

Dmitry Mukhin¹, Sergey Kravtsov¹, Aleksei Seleznev¹

¹Institute of Applied Physics of RAS, 46 Ulyanov Str., Nizhny Novgorod 603950, Russia.

Chaos (Woodbury, N.Y.)

|February 1, 2023

Summary

This study presents a data-driven method to predict complex natural and social systems. It estimates external influences and internal variability to forecast system behavior, aiding in understanding underlying dynamics.

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Area of Science:

Complex Systems Science
Data-Driven Modeling
Predictability Analysis

Background:

Natural and social systems display complex dynamics governed by unknown laws.
Estimating system predictability is crucial for understanding and forecasting behavior.

Purpose of the Study:

To develop a unified data-driven approach for estimating the predictability of complex systems.
To provide a framework for analyzing systems with multiple independent realizations.

Main Methods:

Ensemble mean estimation for external factors (forcings) in quasi-linear dynamics.
Bayesian linear stochastic modeling to capture residual internal variability.
Identifying predictable patterns using self-forecast covariance matrices.

Main Results:

The method successfully decomposes system evolution into forced signals and internal variability.
Demonstrated application to climate modeling (sea-surface temperature) and economic data (consumer spending).
Revealed diverse predictability characteristics, from low-dimensional forced signals to complex forcings with limited predictable modes.

Conclusions:

The proposed framework offers insights into the underlying dynamical processes of complex systems.
The decomposition technique is versatile, applicable to both natural and social systems.
Highlights the utility of data-driven approaches in assessing and understanding system predictability.