Learning Curve

Mark S. Miller from the December 1996 issue

(Page 3 of 3)

Holland does not appreciate that there is no outside for the market. Any individual or group, given the power to wield a lever arm over the market, would become part of a system of incentives coupled to that market. This is the "public choice" insight. The resulting system is still one of feedback and incentives--now including the intervener--but no longer is it simply a market system. Given the evidence of history, the burden of proof is on those who propose mixed systems to explain why they would be an improvement. Perhaps Holland is too used to interacting with laboratory ecosystems, where he truly is outside, to have a sense of the force of this problem.

But even in his laboratory, should he attempt such outside intervention, he would still find himself suffering from the Hayekian knowledge problem, even though all the information literally is gathered together and available in one place. He would find that the system had come to embody, by adaptation, knowledge about itself to which he had no practical access. Indeed, much of the attraction of the spontaneous ordering approach to machine learning is that--in large measure because of Holland--we can get machines to adapt to complex phenomena we ourselves have difficulty figuring out.

The contrasting approach--having machines form explicit representations of the lessons they learn--has not resulted in much learning; and only these representations could have made the accumulated knowledge visible to an observer. Research to date shows that figuring out what an adaptive system has learned is astonishingly more difficult than the learning itself. Even with full access and control of the entire state of the machine, an intelligence orders of magnitude greater than the creatures populating the machine or of the machine as a whole, and from a position truly insulated from the system's incentives, Holland would still find it extremely hard to intervene to good effect, and he is wise not to try.

Despite the problems, Holland does a fine job proposing a discipline to study these questions and principles, and seeding it with his own research. His three software systems are powerful insight generators for all cas, not least because they remove the ghost from the machine. By stepping through the entire mechanisms of a few full evolutionary learning systems, we see there is no magic. Each is a system whose logic we can hold in our heads, thereby enabling thought experiments. Applying the resulting clarity of insight to the examination of other cas leads to better understandings of each and of the relationships among them.

The interdisciplinary investigation Holland is pursuing into the nature of spontaneously ordering evolutionary learning systems is the research program Hayek had earlier proposed. Hayek did much early original work and clearly would have loved to do more. However, as Thomas Jefferson said, "We shed our blood...that our children may be philosophers." Having seen the horrors statism leads to, Hayek shed much of his life warning us, rather than advancing his early groundbreaking work. This research program is finally being pursued, but, ironically, mostly by researchers unaware of Hayek's work. At the same time, those most influenced by Hayek still fight the old fights. Now that the tide has turned in the struggle to free minds and free markets, it is time to join these bold researchers in the quest to understand both.

Mark S. Miller is one of the pioneers of the field of agoric computation--using idealized markets as the foundation of secure distributed general-purpose computing. He works for Electric Communities in Cupertino, California, and can be found at http://www.caplet.com. He thanks Bill Tulloh, K. Eric Drexler, and others for their ideas and assistance with this article.

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