  We improve the framework of open games with agency by showing how the
players' counterfactual analysis giving rise to Nash equilibria can be
described in the dynamics of the game itself (hence diegetically), getting rid
of devices such as equilibrium predicates. This new approach overlaps almost
completely with the way gradient-based learners are specified and trained.
Indeed, we show feedback propagation in games can be seen as a form of
backpropagation, with a crucial difference explaining the distinctive character
of the phenomenology of non-cooperative games. We outline a functorial
construction of arena of games, show players form a subsystem over it, and
prove that their 'fixpoint behaviours' are Nash equilibria.
