  The past two decades have seen a revolution in statistical physics,
generalizing it to apply to systems of arbitrary size, evolving while
arbitrarily far from equilibrium. Many of these new results are based on
analyzing the dynamics of the entropy of a system that is evolving according to
a Markov process. These results comprise a sub-field called ``stochastic
thermodynamics''. Some of the most powerful results in stochastic
thermodynamics were traditionally concerned with single, monolithic systems,
evolving by themselves, ignoring any internal structure of those systems. In
this chapter I review how in complex systems, composed of many interacting
constituent systems, it is possible to substantially strengthen many of these
traditional results of stochastic thermodynamics. This is done by ``mixing and
matching'' those traditional results, to each apply to only a subset of the
interacting systems, thereby producing a more powerful result at the level of
the aggregate, complex system.
