Randomness
From Maze
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===III. The emergence of Randomness in Computing=== | ===III. The emergence of Randomness in Computing=== | ||
| − | A central element of this section is the work of John von Neumann, who sought to easily create (pseudo-)random numbers for the purposes of conducting computer simulations of the hydrogen bomb. A key point to tease out here is that the first computer-based random number generators were closely connected to the Cold War. In this way, the RND token is a tiny slice of a much larger shared history between war and computers. This connection has been well-traced out in regards to videogames, but the military's connection to something as simple as RND is something too easily glossed over. What's remarkable, in looking over the early papers discussing random number generation from the fifties and sixties, is that all of them were funded by either the military or the Atomic Energy Commission. | + | |
| + | The RND command is the algorithmic heart of 10 PRINT, its flip-flip-flopping beat powering the creation of the maze. It is also a paradox. The function of the RND token in 10 PRINT is immediately known yet its output is entirely unknown. Unpredictable. A cipher, the manifestation of a much deeper latent content, operating underneath the surface of the machine. | ||
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| + | An obvious question to ask about randomness is why bother at all? Why have the ability in the first place to generate random numbers? Computers are supposed to calculate with exactitude. Computers are supposed to order the world. Computers are supposed to sift through reams of data and model possible outcomes, possible futures. | ||
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| + | But it turns out that it’s impossible to function as any kind of calculating machine or simulation without taking into account randomness, a defining feature of life—and the universe for that matter. | ||
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| + | Quite simply, randomness is required for any statistical endeavor needing random sampling or tests of probability. And one or two random numbers are not enough. Large-scale statistical calculations or simulations require large batches of random numbers. In her history of humans’ quest for randomness--which Bennett suggests goes as far back as ancient Babylonia (17)--Bennett highlights one the early and largest set of random numbers, the RAND Corporation’s ''A Million Random Digits with 100,000 Normal Deviates'' (135). Generated in 1955 “by random frequency pulses of an electronic roulette wheel,” the RAND’s set of random numbers was an important contribution to any study of probability and is still in use today. As the forward to the undated online edition of the table notes: | ||
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| + | <blockquote>The tables of random numbers in the book have become a standard reference in engineering and econometrics textbooks and have been widely used in gaming and simulations that employ Monte Carlo trials. Still the largest known source of random digits and normal deviates, the work is routinely used by statisticians, physicists, polltakers, market analysts, lottery administrators, and quality control engineers. (RAND Corporation)</blockquote> | ||
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| + | These seemingly benign uses aside, the RAND report ominously explains that its one million random numbers were originally “prepared in connection with analyses done for the United States Air Force” (RAND Corporation). Like so many other advances in computing, randomness, it turns out, is intimately linked to Cold War military strategies. In fact, most of the early work on computer-based random number generation was performed under the auspices of the U.S. Atomic Energy Commission (see, for example, Rotenberg’s work in the late fifties) or the U.S military (such as Green et al’s work at MIT, with joint support of the U.S. Army, Navy, and Air Force). | ||
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| + | As Donald Knuth relates in ''The Art of Computer Programming'', John von Neumann was the first to propose the idea of harnessing a computer to generate random numbers (Knuth 3). It was around 1946 and von Neumann was fresh off the Manhattan Project and soon to begin his lead work on the hydrogen bomb. Von Neumann’s solution, for which a computer was to be used, was called the middle-square method. It worked by squaring an initial random number and extracting the middle digits; this number was then squared again, and the middle digits provided a new random number. The disadvantage of this method soon became apparent: because each number is a function of the one before it, the sequence, as Knuth explains, “isn’t random, but it appears to be” (3)--a phenomenon known today as “pseudo-randomness,” which arguably describes any human or machine attempt to generate random numbers. | ||
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| + | Knuth gamely admits that his own attempts to generate random numbers through software algorithms—using what he hoped to be a “’super random’ number generator”--failed, though it’s worth noting that he wanted his machine language program “to be so complicated that a man reading a listing of it without explanatory comments would not know what the program was doing” (Knuth 5). It does not take a leap of imagination to see in Knuth’s desire for obscure code an early version of the same kind of mystery residing in 10 PRINT. | ||
===IV. The emergence of computational randomness in aesthetic design=== | ===IV. The emergence of computational randomness in aesthetic design=== | ||
