Computational Complexity Theory. Developed mostly in the 1970s, computational complexity theory attempts to characterize how difficult certain computational tasks are to perform. Its concrete results have tended to be based on fairly specific programs with complicated structure yet rather simple behavior. The new kind of science in this book, however, explores much more general classes of programs—and in doing so begins to shed new light on various longstanding questions in computational complexity theory.
Cybernetics. In the 1940s it was thought that it might be possible to understand biological systems on the basis of analogies with electrical machines. But since essentially the only methods of analysis available were ones from traditional mathematics, very little of the complex behavior of typical biological systems was successfully captured.
Dynamical Systems Theory. A branch of mathematics that began roughly a century ago, the field of dynamical systems theory has been concerned with studying systems that evolve in time according to certain kinds of mathematical equations—and in using traditional geometrical and other mathematical methods to characterize the possible forms of behavior that such systems can produce. But what I argue in this book is that in fact the behavior of many systems is fundamentally too complex to be usefully captured in any such way.
Evolution Theory. The Darwinian theory of evolution by natural selection is often assumed to explain the complexity we see in biological systems—and in fact in recent years the theory has also increasingly been applied outside of biology. But it has never been at all clear just why this theory should imply that complexity is generated. And indeed I will argue in this book that in many respects it tends to oppose complexity. But the discoveries in the book suggest a new and quite different mechanism that I believe is in fact responsible for most of the examples of great complexity that we see in biology.
Experimental Mathematics. The idea of exploring mathematical systems by looking at data from calculations has a long history, and has gradually become more widespread with the advent of computers and