Complexity_ A Guided Tour - Melanie Mitchell [142]
This all sounds well and good, but where are examples of such principles? Of course proposals for common or universal principles abound in the literature, and we have seen several such proposals in this book: the universal properties of chaotic systems; John von Neumann’s principles of self-reproduction; John Holland’s principle of balancing exploitation and exploration; Robert Axelrod’s general conditions for the evolution of cooperation; Stephen Wolfram’s principle of computational equivalence; Albert-László Barabási and Réka Albert’s proposal that preferential attachment is a general mechanism for the development of real-world networks; West, Brown, and Enquist’s proposal that fractal circulation networks explain scaling relationships; et cetera. There are also many proposals that I have had to leave out because of limited time and space.
I stuck my neck out in chapter 12 by proposing a number of common principles of adaptive information processing in decentralized systems. I’m not sure Gordon would agree, but I believe those principles might actually be useful for people who study specific complex systems such as the ones I covered—the principles might give them new ideas about how to understand the systems they study. As one example, I proposed that “randomness and probabilities are essential.” When I gave a lecture recently and outlined those principles, a neuroscientist in the audience responded by speculating where randomness might come from in the brain and what its uses might be. Some people in the room had never thought about the brain in these terms, and this idea changed their view a bit, and perhaps gave them some new concepts to use in their own research.
On the other hand, feedback must come from the specific to the general. At that same lecture, several people pointed out examples of complex adaptive systems that they believed did not follow all of my principles. This forced me to rethink what I was saying and to question the generality of my assertions. As Gordon so rightly points out, we should be careful to not ignore “anything that doesn’t fit a pre-existing model.” Of course what are thought to be facts about nature are sometimes found to be wrong as well, and perhaps some common principles will help in directing our skepticism. Albert Einstein, a theorist par excellence, supposedly said, “If the facts don’t fit the theory, change the facts.” Of course this depends on the theory and the facts. The more established the theory or principles, the more skeptical you have to be of any contradicting facts, and conversely the more convincing the contradicting facts are, the more skeptical you need to be of your supposed principles. This is the nature of science—an endless cycle of proud proposing and disdainful doubting.
Roots of Complex Systems Research
The search for common principles governing complex systems has a long history, particularly in physics, but the quest for such principles became most prominent in the years after the invention of computers. As early as the 1940s, some scientists proposed that there are strong analogies between computers and living organisms.
In the 1940s, the Josiah Macy, Jr. Foundation sponsored a series of interdisciplinary scientific meetings with intriguing titles, including “Feedback Mechanisms and Circular Causal Systems in Biological and Social Systems,” “Teleological Mechanisms in Society,” and “Teleological Mechanisms and Circular Causal Systems.” These meetings were organized by a small group of scientists and mathematicians who were exploring common principles of widely varying complex systems. A prime mover of this group was the mathematician Norbert Wiener, whose work on the control of anti-aircraft guns during World War II had convinced him that the science underlying complex systems in both biology and engineering should focus not on the mass, energy, and force concepts of