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Active Inference, developed by Karl Friston and colleagues, offers a formal approach to perception, learning and action. Models of future action can bring together preferred outcomes and expected information gain.

The Free Energy Principle offers a theoretical account of how systems can persist through their exchanges with an environment. Under its assumptions, these dynamics can be described through the minimization of variational free energy. This is broader than simply correcting a prediction error.

A Markov blanket describes a statistical boundary: internal and external states are conditionally independent given the blanket states. In Active Inference models, sensory and active states mediate their interaction. A cell membrane or an organization chart is not, by itself, sufficient to establish such a boundary.

I use multi-scale Active Inference as a lens for exploring connections across systems. Applying it to a team, company or society requires specifying the model and testing its assumptions. My five-stage loop is a teaching synthesis inspired by this work, rather than a formal five-step definition of the theory.

Read the research: Active inference and epistemic value ↗The Markov blankets of life ↗