Open or closed? Information flow decided by transfer operators and forecastability quality metric

A basic systems question concerns the concept of closure, meaning autonomy (closed) in the sense of describing the (sub)system as fully consistent within itself. Alternatively, the system may be nonautonomous (open), meaning it receives influence from an outside subsystem. We assert here that the concept of information flow and the related concept of causation inference are summarized by this simple question of closure as we define herein. We take the forecasting perspective of Weiner-Granger causality that describes a causal relationship exists if a subsystem's forecast quality depends on considering states of another subsystem. Here, we develop a new direct analytic discussion, rather than a data oriented approach. That is, we refer to the underlying Frobenius-Perron (FP) transfer operator that moderates evolution of densities of ensembles of orbits, and two alternative forms of the restricted Frobenius-Perron operator, interpreted as if either closed (deterministic FP) or not closed (the unaccounted outside influence seems stochastic and we show correspondingly requires the stochastic FP operator). Thus follows contrasting the kernels of the variants of the operators, as if densities in their own rights. However, the corresponding differential entropy comparison by Kullback-Leibler divergence, as one would typically use when developing transfer entropy, becomes ill-defined. Instead, we build our Forecastability Quality Metric (FQM) upon the "symmetrized" variant known as Jensen-Shannon divergence, and we are also able to point out several useful resulting properties. We illustrate the FQM by a simple coupled chaotic system. Our analysis represents a new theoretical direction, but we do describe data oriented directions for the future.