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PREDICTIONS – 10 Years Later - Santa Fe Institute

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2. NEEDLES IN A HAYSTACK<br />

This pattern of learning is not restricted to individuals. It is also encountered<br />

with a well-defined group of people, a country or humanity as<br />

a whole. For example, the scientific community has followed this same<br />

pattern, as if it were a single individual, while discovering the stable<br />

chemical elements throughout the centuries. If we make a graph of the<br />

number of elements known at any given time, an accumulation of<br />

knowledge, we obtain a pattern that resembles the S-curve of an infant’s<br />

learning process. The main difference is that the time scale is in centuries<br />

instead of years. What may have been thought of as random<br />

discoveries are seen to be associated with a regular curve.<br />

The first dozen elements had been known for a very long time; iron,<br />

copper, gold, mercury, and carbon were familiar to the men of antiquity.<br />

In the middle of the eighteenth century, however, there was a thirst and<br />

a push for discovering more and more stable elements, and by the mid<br />

twentieth century, all the naturally available elements had been found.<br />

Still, researchers pushed for more discoveries, now carried out in particle<br />

accelerators. These late entries—one may say concoctions—hardly<br />

deserve the name “stable” elements because they last only a short time<br />

before they decay. At any rate, we have been running out of those, too.<br />

The last artificially created element was established in 1984. Overall,<br />

the 250-year-long discovery process seems to have followed the path of<br />

natural growth and reached completion.<br />

In both learning processes discussed above, we see “random” discoveries<br />

following overall orderly curves, an apparent contradiction<br />

since randomness is associated with chaotic behavior while S-curves<br />

represent the order imposed by the logistic law. It can be shown, however,<br />

that chaos is seeded with S-curves to begin with, and James Gleick<br />

in his book Chaos explains how chaos can result from a prolonged action<br />

of the logistic law. One can find an “eerie sense of order” in chaotic<br />

patterns. We will be looking more closely at the relationship between<br />

order and chaos in Chapter Ten, and will conclude that such a type of<br />

order should not be so eerie after all.<br />

48

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