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Combining lower bounds on entropy production in complex systems with multiple interacting components

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.