Hidden Order: How Adaptation Builds Complexity.

• Author: Miller, Mark S.

"On an ordinary day in New York City, Eleanor Petersson goes to her favorite specialty store to pick up ajar of pickled herring. She fully expects the herring to be there. Indeed, New Yorkers of all kinds consume vast stocks of foods of all kinds, with hardly a worry about continued supply.... What enables cities to retain their coherence despite continual disruptions and a lack of central planning?"

With this question, John Holland begins his ambitious and intriguing, but often frustrating, book about spontaneously ordering systems. Holland is a professor of computer science and electrical engineering and of psychology at the University of Michigan, a recipient of the MacArthur Fellow award, and co-chairman of the Santa Fe Institute Steering Committee. (See "Complex Questions," January 1996.) In Hidden Order, he draws our attention to similarities among phenomena as diverse as the biological evolution of individual organisms and complex ecosystems; the functioning of the immune system; the way minds perceive and learn; the dynamics of the market economy; and some software systems of his invention, which learn to adapt to their environment. Though separate disciplines study these phenomena, Holland shows deep general principles that underlie them all. When fields separated by existing academic boundaries share unifying principles, the time is ripe for the creation of a new cross-cutting discipline.

Holland calls that discipline-in-formation the study of "complex adaptive systems," or cas. Like economists F. A. Hayek and Herbert Simon, he was one of the field's earliest contributors - and one of the best. Although Holland developed his ideas without knowledge of Hayek's work, the two scholars are wonderfully complementary. (Simon, whose work is beyond the scope of this review, is a leading artificial intelligence researcher and, like Hayek, a Nobel laureate in economics.) Whereas Holland started with machine learning and genetic evolution, and extended into psychology, epistemology, and symbiosis in ecosystems, Hayek started with psychology, evolution, and economics, and extended into epistemology, law, ethics, and culture.

By taking seriously the evolutionary, unplanned nature of markets, Hayek made seminal contributions to economics. These contributions help explain the nature of the knowledge learned by evolutionary processes, and how ecosystems can successfully self-organize to employ vast amounts of such knowledge. Though not well known by current neural-network researchers, Hayek's 1952 book The Sensory Order helped found (via the work of Frank Rosenblatt) that branch of machine-learning research. Taking different paths, Hayek and Holland came to a common notion of the territory they were exploring. Hayek also called for a new discipline to study cas, which he called "spontaneous orders."

Of all cas researchers, including Hayek, Holland is clearest that evolutionary learning is the important property shared by complex adaptive systems, and he has done the most to advance our general understanding of such learning. Unfortunately, though Hidden Order focuses on learning, speaks of the importance of applying cas insights to economics, and even employs idealized markets within the learning mechanism of classifier systems, it nowhere applies learning ideas to the study of markets.

Nonetheless, and to Holland's credit as one of the new discipline's progenitors, the book will encourage interested readers to consider the implications of his theory for how markets learn. Current economics focuses on markets as mechanisms for efficient allocation and distribution, and as arrangements providing freedom and rights, but it rarely examines the questions first raised by Hayek: how markets themselves learn, and how they successfully employ learned knowledge. Building on Holland's work could open up this important field of inquiry.

Holland came to complex adaptive systems through his work on machine learning - the effort to build artificial systems that learn by interacting with an environment. Using insights from biological evolution, he first invented "genetic algorithms," presented in his 1975 book Adaptation in Natural and Artificial Systems. Genetic algorithms have spawned an important branch of machine-learning research, complete with annual conferences, and are used in commercial software to do such complex tasks as grading wood and identifying fingerprints. Combining genetic algorithms with insights from cognitive psychology, epistemology, and economics, Holland went on to invent "classifier systems," an even more ambitious machine-learning architecture presented in his 1986 book Induction. By borrowing mechanisms from naturally occurring complex adaptive systems, and synthesizing them into machine-based systems, Holland hoped not only to find useful tools for computing but to gain new insights into natural cas.

In Hidden Order, Holland shifts his focus from machine learning to study of the general nature of complex adaptive systems. He starts by proposing seven characteristics, which he argues unify all cas. The book goes on to discuss Holland's three evolutionary software architectures: genetic algorithms, classifier systems, and a new one presented here for the first time, Echo. In Echo, complex creatures arise from symbiotic...