Learning Curve

Mark S. Miller from the December 1996 issue

Hidden Order: How Adaptation Builds Complexity, by John Holland, New York: Addison-Wesley, 185 pages, $24.00/$12.00 paper

"On an ordinary day in New York City, Eleanor Petersson goes to her favorite specialty store to pick up a jar 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 patterns formed from simpler creatures. These three systems are not introduced primarily to teach about machine learning, however, but to explore cas issues in a clear and concrete manner. Throughout, Holland sprinkles examples from a good mix of different cas, including markets. Finally, the book concludes with an attempted call-to-arms that presents potential contributions to economics as the primary motivation for studying cas and suggests some public policy implications.

One of Holland's principal purposes is to propose underlying characteristics common across all complex adaptive systems. He seeks to create a theoretical underpinning for the new field that will "separate fundamental characteristics from fascinating idiosyncrasies and incidental features," so that systematic research becomes possible. "Theory is crucial," he writes. "Serendipity may occasionally yield insight, but is unlikely to be a frequent visitor. Without theory, we make endless forays into uncharted badlands." With a workable theory, however, we can begin to ask and explore useful questions.

Holland begins, therefore, by organizing the properties and mechanisms that he argues are universal among cas. Very briefly, these are:

• Aggregation. Complexity emerges from interactions of simpler components, often themselves complex systems emergent from interactions of yet simpler components: Bodies are made of organs, made of tissues, made of cells.
• Tagging. Agents carry recognizable markers allowing other agents to suspect which ones have particular properties. Examples include trademarks, pheromones, and the immune system's ability to spot past invaders.
• Nonlinearity. Agents interact, rather than just adding together.
• Flows. Agents organize into networks of potential interactions. One interaction triggers another, causing effects to flow through these networks.
• Diversity. Agents evolve to fill diverse niches, which are defined by how the agent interacts with other agents. Niches usually outlive their current occupants, and the change of niches over time has a much greater effect on an ecosystem than changes to individual agents. For markets, if agents are businesses then niches are industries.
• Internal models. Agents experience internal changes that result from sensing an external world. Such changes bias actions toward those likely to be effective in a world that produced those sensations. These internal states are often a form of tacit knowledge of the world--they embody a discovery of how to exploit a regularity of the world without representing the regularity itself. Evolution adapted the eye to facts about optics, but nowhere in the eye can one find a representation or explanation of those facts.
• Building blocks. Components are reused for multiple purposes. This is the flip side of aggregation. When businesses are not vertically integrated, for instance, a supplier serves multiple companies. These independent suppliers learn less about any one company, instead learning more general lessons by serving a diversity of companies.

Though not quite as universal as Holland claims, these characteristics are universal enough to indicate general principles. Holland's three ecosystems provide clear examples of several of these. Genetic algorithms search for solutions to hard problems by variation and selection of creatures representing proposed answers. Genetic algorithms are based on a simplification of genetic evolution: Each "creature" is essentially a single fixed-length chromosome--a string of computer symbols--whose "fitness" is rated according to the quality of answer it represents. Initially, one creates a population of creatures made of random chromosomes. The ratings then determine how many variations of each creature will form the next generation of the population. This procedure is repeated from one generation to the next.

For example, say a salesman must visit certain cities and needs a route that minimizes total distance. To use genetic algorithms to find a short route, the salesman generates a population where each creature is a randomly ordered list of these cities. To pose a problem, the salesman creates a reward function: When a creature's list is interpreted as a route, the shorter the route, the more offspring the creature has. Over generations, the quality of routes present in the population improves. The answers produced by genetic algorithms, while embedded in the rules governing the system, are often far from predictable. By mimicking genetic evolution, such systems are able to generate better solutions more quickly than conventional programs. They learn.

Sexual reproduction, in particular, leads to a form of learning that produces substantially faster search. In the sales-route example, when two creatures mate, their progeny inherit sub-sequences of cities from each parent, leading to the eventual combination of separately discovered good sub-routes. Combining parts of separately evolved answers produces rapid convergence on a good overall route. In effect, the sub-sequences are treated as proposed answers to possible sub-problems and serve as reusable building blocks for assembling ever larger answers. Learning proceeds by accumulation, improvement, and growth of these building blocks. Holland's analysis of sex also helps explain the power of combining partial solutions by entrepreneurship or interdisciplinary study.

Comments

nfl jerseys|11.16.10 @ 8:25PM|#

xryujryt

log in or register to reply