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system’s complexity: while it surely makes the system more complicated, complexity seems to require something more. This is a fact that the mereological size measure (especially in Kiesling’s phrasing) quite rightly seizes on: complexity is (at least partially) a fact not just about parts of a system, but about how those parts interact.

Let’s start to refine Chapter One’s definition, then, by thinking through some examples. As a reminder, let’s remind ourselves of the example we worked through there: consider a thermodynamically-isolated system consisting of a person standing in a kitchen, deliberating about whether or not to stick his hand in the pot of boiling water. As we saw, a system like this one admits of a large number of ways of carving up the associated configuration space[1]: describing the situation in the vocabulary of statistical mechanics will yield one set of time-evolution patterns for the system, while describing it in the vocabulary of biology will yield another set, and so on. Fundamental physics provides the “bit mapping” from points in the configuration space representing the system at one instant to points in the same space at another instant; the different special sciences, then, offer different compression algorithms by which the state of a particular system can be encoded. Different compressions of the same system will evince different time-evolution patterns, since the encoding process shifts the focus from points in the configuration space to regions in the same space. All of this is laid out in significantly more detail in Chapter One.

Now, consider the difference between the person-stove- water system and the same system, only with the person removed. What’s changed? For one thing, the dimensionality of the

  1. Equivalently, we might say that a system like this admits of a very large number of interesting configuration spaces; there are very many ways that we might describe the system such that we can detect a variety of interesting time-evolution patterns.