Energy Dissipation and Information Flow in Coupled Markovian Systems

A stochastic system under the influence of a stochastic environment will become correlated with both present and future states of the environment. Such a system can be seen as a predictive model of future environmental states. The non-predictive model complexity in such a model has been shown in a recent paper to be fundamentally equivalent to thermodynamic dissipation. In this dissertation, this abstract result is explored in concrete models in order to illustrate how it emerges in realistic systems. In steady-state, this model complexity is found to be the dominant form of dissipation when the system is strongly driven and quick to relax back to equilibrium. Model complexity being the dominant form of dissipation is shown to be equivalent to the rate at which the system learns about its environment being large compared to the heat dissipation.