*regions of interest to more special sciences: a system for which the patterns of economics, psychology, biology, chemistry, and physics are predictively useful is more complex than one for which only the patterns of chemistry and physics are predictively useful.*

**2.2.1 Dynamical Complexity as a Unifying Definition**

I have now given a definition of dynamical complexity. Before we close this theoretical discussion and move on to consider the special problems faced by climate science as a complex science, it’s worth briefly reviewing the attempted definitions of complexity we surveyed in **Section 2.1** to see how dynamical complexity fares as a unifying definition of complexity. In this section, I will argue that dynamical complexity succeeds in cherry-picking the best features of the mereological size measure, the hierarchical position measure, the information-theoretic measure, and the fractal dimension measure, while avoiding the worst difficulties of each of them. Let’s begin with the mereological size measure.

As I mentioned above, one of the strongest virtues of the mereological size measure is that (at least in its better formulations) it attends to the fact that complexity is a concept that deals not with static systems, but with *dynamic* systems—with systems that are moving, changing, and exchanging information with their environments. Strevens^{[1]}, for instance, emphasizes not only the presence of many parts in a complex system, but also the fact that those parts *interact* with one another in a particular way. This is an insight that is clearly incorporated into dynamical complexity: since dynamical complexity deals with the number of different ways of carving configuration space that yield informative time-evolution patterns for a given system, the presence of interacting constituent parts is indeed, on this view, a great contributor to

- ↑ Strevens (
*Ibid)*

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