Information flow in a kinetic Ising model peaks in the disordered phase

Area of Science:

• Complex Systems
• Statistical Physics
• Information Theory

Background:

• Mutual information often peaks at phase transitions in complex systems.
• Understanding information flow dynamics is crucial for predicting system behavior.

Purpose of the Study:

• To propose and verify the conjecture that information flow peaks on the disordered side of a phase transition.
• To explore the predictive power of information dynamics in complex systems.

Main Methods:

• Utilized a ferromagnetic 2D lattice Ising model with Glauber dynamics.
• Employed a transfer entropy-based measure for systemwide information flow analysis.

Main Results:

• Confirmed the conjecture: information flow peaks strictly on the disordered side of the phase transition.
• Demonstrated the model's ability to capture critical transition dynamics.

Conclusions:

• Information flow dynamics offer a novel approach to predicting phase transitions in complex systems.
• This approach has potential applications in financial markets and understanding neurological events like epileptic seizures.