complex systems science, and that recognizing that fact is essential if we’re to make progress as rapidly as we need to. More specifically, I’ll argue that the parallels between climate science and other complex systems sciences—particularly economics—have been largely overlooked, and that this oversight is primarily a result of the tradition of dividing the sciences into physical and social sciences. This division, while useful, has limitations, and (at least in this case) can obfuscate important parallels between different branches of the scientific project. The complex/simple systems distinction cuts across the physical/social science distinction, and serves to highlight some important lessons that climate science could learn from the successes (and failures) of other complex systems sciences. This is the second (and last) chapter that will be primarily philosophical in character; with the last of our conceptual tool-kit assembled here, we’ll be ready to move on to a far more concrete discussion in Chapter Three and beyond.
2.1 What is “Complexity?”
Before we can actually engage with complex systems theories (and bring those theories to bear in exploring the foundations of climate science), we’ll need to articulate what exactly makes a system complex, and examine the structure of complex systems theories generally. Just as in Chapter One, my focus here will be primarily on exploring the actual practice of contemporary, working science: I’m interested in what climate scientists, economists, and statistical physicists (as well as others working in the branches of science primarily concerned with predicting the behavior of complex systems) can learn from one another, rather than in giving a priori pronouncements on the structure of these branches of science. With that goal in mind, we will anchor our discussion with examples drawn from contemporary scientific theories whenever possible, though a certain amount of purely abstract theorizing is unavoidable. Let's get that over
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