Randomness
From Maze
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The RND command acts as the algorithmic heart of 10 PRINT, its flip-flopping beat powering the construction of the maze. The RND function is as fully specified and well-known as any BASIC keyword, but its output is, by that definition, unpredictable. This paradoxical function manifests a much deeper, latent sequence, operating underneath the surface of the machine. | The RND command acts as the algorithmic heart of 10 PRINT, its flip-flopping beat powering the construction of the maze. The RND function is as fully specified and well-known as any BASIC keyword, but its output is, by that definition, unpredictable. This paradoxical function manifests a much deeper, latent sequence, operating underneath the surface of the machine. | ||
| − | 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. Yet a function to create random numbers was present in the first Dartmouth BASIC. Every version of BASIC since then has had one or more ways to create random numbers. Finally, nearly every contemporary programming language, including Python, Perl, Java, JavaScript and C++, has a way to generate randomness. It turns out to be impossible to simulate a wide variety of situations and systems without taking into account randomness. | + | An obvious question to ask about randomness is why bother at all? Chance might produce stunning poetry, breathtaking art, and uncanny music, but what is the role of chance in the sciences? Why give a computer the ability to generate random numbers in the first place? 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. Yet a function to create random numbers was present in the first Dartmouth BASIC. Every version of BASIC since then has had one or more ways to create random numbers. Nearly every contemporary programming language, including Python, Perl, Java, JavaScript and C++, has a way to generate randomness. Why? It turns out to be impossible to simulate a wide variety of situations and systems without taking into account randomness. |
| − | [Explain why exactly people purchased & used AMRD - what did they do with it? Also Reference the modern Parodies to it?] | + | Quite simply, randomness is necessary for any statistical endeavor or any simulation that involves unknown variables. And ''everything'' involves unknown variables. The meteorological conditions at rocket launch site, the flow of air under a bomber's wings, the vector of an infectious disease. But also the traffic, the cost of bread, and a drip of water from the kitchen faucet. Forecasting any of these phenomena requires reckoning with uncertainty, which in turn requires a pool of random numbers that can be plugged into sampling algorithms and 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 of 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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| − | 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 of 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: | ||
<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> | <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> | ||
