The Free Energy Principle (FEP) states that self-organizing systems which maintain a separation from their environments via a Markov blanket---including the brain and other physical systems---minimize their variational free energy (VFE) and expected free energy (EFE) via perception and action respectively^[1]. Unlike in other theories of agency, under FEP, action and perception are unified as inference problems under similar objectives. In some cases, variational free energy reduces to prediction error, which is the difference between the predictions made about the environment and the actual outcomes experienced. The mathematical content of FEP is based on Variational Bayesian methods.

Although FEP has an extremely broad scope, it makes a number of very specific assumptions^[2] that may restrict its applicability to real-world systems. Ongoing theoretical work attempts to reformulate the theory to hold under more realistic assumptions. Some progress has been made: newer formulations of FEP, unlike their predecessors, do not assume a constant Markov blanket (but rather, some Markov blanket trajectory)^[3] and do not assume the existence of a non-equilibrium steady state^[4].

FEP has been influential in neuroscience and neuropsychology and more recently has been used to describe systems on all spatiotemporal scales, from cells and biological species to AIs and societies.

Process theories

Since FEP is an unfalsifiable mathematical principle, it does not make sense to ask whether FEP is true (because it is true mathematically given the assumptions.) Rather, it makes sense to ask whether its assumptions hold for a given system, and, if so, how that system implements the minimization of VFE and EFE. Unlike the FEP itself, a proposal of how some particular system minimizes VFE and EFE---a process theory---is falsifiable.

There are two FEP process theories most relevant to neuroscience.^[5] Predictive processing is a process theory of how VFE is minimized in brains during perception. Active Inference (AIF) is a process theory of the "action" part of FEP, which can also be seen as an agent architecture.

It has been argued^[6] that AIF as an agent architecture manages the model complexity (i.e. the bias-variance tradeoff) and the exploration-exploitation tradeoff in a principled way; favours explicit, disentangled, and hence more interpretable beli...