  We discuss an approach to mathematically modelling systems made of objects
that are coupled together, using generative models of the dependence
relationships between states (or trajectories) of the things comprising such
systems. This broad class includes open or non-equilibrium systems and is
especially relevant to self-organising systems. The ensuing variational free
energy principle (FEP) has certain advantages over using random dynamical
systems explicitly, notably, by being more tractable and offering a
parsimonious explanation of why the joint system evolves in the way that it
does, based on the properties of the coupling between system components. Using
the FEP allows us to model the dynamics of an object as if it were a process of
variational inference, because variational free energy (or surprisal) is a
Lyapunov function for its dynamics. In short, we argue that using generative
models to represent and track relations among subsystems leads us to a
particular statistical theory of interacting systems. Conversely, this theory
enables us to construct nested models that respect the known relations among
subsystems. We point out that the fact that a physical object conforms to the
FEP does not necessarily imply that this object performs inference in the
literal sense; rather, it is a useful explanatory fiction which replaces the
'explicit' dynamics of the object with an 'implicit' flow on free energy
gradients - a fiction that may or may not be entertained by the object itself.
