Excerpt

A few years ago, I saw a cartoon showing two scientists arguing over the meaning of complexity. In suitably dogmatic terms the first scientist asserted, “Complexity is what you don’t understand.” Responding to this temerarious claim, his colleague replied, “You don’t understand complexity.” This circular exchange mirrors perfectly to my eye how the informal term complexity has been bandied about in recent years—especially within the normally flinty-eyed community of system scientists—as a characterization of just about everything from aardvarkology to zymurgology. Without benefit of anything even beginning to resemble a definition, we find the putative “science” of complexity being described in terms rosy enough to emit heat: adaptive behavior, chaotic dynamics, massively parallel computation, self-organization, and even on to the creation of life itself within the cozy confines of a machine. And, to add a final touch of spice, all this hoopla often comes wrapped up in language vague enough to warm the heart of any continental philosopher. But useful as all this fuzziness is for fending off cocktail-party bores and writing research grant proposals, it becomes a major impediment when we start talking seriously about a “science” of complex systems. The problem is that an integral part of transforming complexity (or anything else) into a science involves making that which is fuzzy precise, not the other way around, an exercise we might more compactly express as “formalizing the informal.” This short essay represents an exploration into some of the dimensions of this problem, as we try to “scientify” the simply complex.

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