Markov blankets, information geometry and stochastic thermodynamics.

Area of Science:

• Thermodynamics
• Information Theory
• Statistical Inference
• Complex Systems

Background:

• Self-organizing systems often operate far from equilibrium.
• Understanding the energetic costs of information processing is crucial for intelligence.
• Variational principles offer a framework for analyzing self-organization.

Purpose of the Study:

• To explore the thermodynamic aspects of belief updating within self-organizing systems.
• To establish a connection between information geometry and Bayesian inference.
• To link stochastic thermodynamics with the energetics of inference.

Main Methods:

• Utilizing a variational (free energy) principle for self-organization.
• Analyzing random dynamical systems with a Markov blanket.
• Applying concepts from information geometry and Bayesian model evidence.

Main Results:

• Systems with Markov blankets exhibit information geometry.
• Internal states of such systems parametrize probability densities over external states.
• Non-equilibrium steady-state dynamics correspond to gradient flows on Bayesian model evidence.

Conclusions:

• A natural Bayesian mechanics emerges for systems with Markov blankets.
• There is an explicit link between inference and the energetics of internal states.
• This framework connects information processing, self-organization, and thermodynamics.