SO MUCH DATA, SO LITTLE TIME


We must not confuse a computer’s ability to win a game of chess or drive a car in formalized settings with human cognition. Gaming and driving are impressive programming accomplish- ments, but they don’t require any true intelligence, understanding or thinking from the computer. Te computer is only process- ing 1s and 0s. Tat’s it. It has no “understanding” of anything it is doing, why it matters, or what makes one set of 1s and 0s more important than another. Te computer simply processes the digits it is given, exactly how it is instructed to. It never wonders why, gets bored, or has a sudden insight. Te program designer is doing all the thinking, making all the value judgments, and deciding if any of the 1s and 0s being produced have meaning or worth. Tere’s nothing our modern computer science mystics have done to change this.


Te challenge of quickly amassing useful information is not new. Long before the present big data mania, 19th-century theorist Carl von Clausewitz, in his 1873 book “On War,” described a pervasive characteristic of war as a “fog” of uncertainty in which a military force must operate. However, it’s not just the opera- tional military that lacks useful information when it is needed and must take action under uncertainty. Tis condition charac- terizes every aggressive, forward-thinking organization engaged in ambitious undertakings.


GOLDILOCKS DATA During a lecture at the University of Virginia in the 1960s, Nobel Prize-winning economist Ronald Coase said, “If you torture the data long enough, nature will always confess.” Tis remains a central concern of the big data revolution. Employing powerful computers to churn through mountains of data does not guaran- tee increased insight. Te basic rules of research still apply: We must begin with a question and be clear about what we are trying


FIGURE 1 eT MODEL ERROR eM ERROR eS COMPLEXITY


Leinweber’s model-error illustration shows that the mini- mum total model error results from the trade-off between decreasing model error (eS) from increasing model specific- ity (eT) and measurement error from increased computation on imperfect data (eM). (SOURCE: “Models, Complex- ity, and Error. A RAND Note prepared for the U.S. Department of Energy,” by David Leinweber, 1979)


to accomplish. Otherwise, we can become lost in the data moun- tains, following the computer on a digital path to nowhere, in a high-tech version of the blind leading the blind.


“Garbage in, garbage out” (GIGO) expresses the fundamental principle that computer algorithms can only produce results as good as the data that feeds them. Te simple fact is that no algo- rithm can create quality information from garbage data. And increasing the quantity of such data doesn’t improve matters.


Te sheer volume of data that computers can process today makes the GIGO problem increasingly acute. In 1979, David Leinweber of the RAND Corp. prepared a note for the U.S. Department of Energy that illustrates this principle clearly and succinctly. (See Figure 1.) His hand-drawn chart, published before the advent of computer graphics, demonstrates the inescapable trade-off between increasing model complexity (called model specificity (S)) and measurement error (M) in mathematical models. Leinwe- ber’s chart shows how increasing model complexity can increase a model’s explanatory power by reducing unexplained variation or mathematical error (eS). But Leinweber’s illustration also shows that this comes at a price. It turns out that, the more calculations we do on imprecise measurements, the more the measurement error (eM) is compounded. Tis is shown by the eM line rising from left to right.


It is also important to understand that, even if we can attain perfect data, model error can increase with model size and complexity as the result of a wide range of distorting influences. For example, even with today’s high-powered computers, many important problems remain too large to ever solve or at least take too long to solve in the available time, so solutions can only be approximated by a sequence of mathematical shortcuts. Tis


124


Army AL&T Magazine


Summer 2019


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