Free A Sense of the Enemy: The High Stakes History of Reading Your Rival's Mind by Zachary Shore Page A

has been searching for ways to extend its gaze into the future, and militaries have been eager to pay for it.
In the seventeenth century, merely gaining an early glimpse of the enemy’s actions was enough to advantage one side over the other. By the twentieth century, strategists needed much more. They needed greater predictive power for anticipating enemy moves. Technology alone could not, and still cannot, fill that gap. Strategists have always needed to develop a sense of the enemy, but the craving for more concrete, reliable predictions has left militaries easily seduced by science. Lately, that longing has led them to focus on the wrong objective: predicting the unpredictable.
    The Numbers That Count
The rush is on to quantify as much as possible and let the algorithms tell us what the future holds. While this method offers obvious advantages, it is not without serious pitfalls. In many realms of prediction, we often go astray when we focus on the facts and figures that scarcely matter, as Nate Silver has shown in his thoughtful, wide-ranging study, The Signal and the Noise . Silver is America’s election guru. He has rocketed to prominence for his successful forecasts of U.S. primary and general election results. In his book, Silver concentrates on those predictions reliant on large, sometimes massive, data sets—so-called “big data.” Silver himself dwells mainly in the realm of number crunchers. He quantifies every bit of data he can capture, from baseball players’ batting averages to centuries of seismologic records, from poker hands to chessboard arrangements, and from cyclone cycles to election cycles. In short, if you can assign a number to it, Silver can surely crunch it.
After four years of intensive analysis, Silver concludes that big data predictions are not actually going very well. Whether the field is economics or finance, medical science or political science, most predictions are either entirely wrong or else sufficiently wrong as to be of minimal value. Worse still, the wrongness of so many predictions, Silver says, tends to proliferate throughout academic journals, blogs,and media reports, further misdirecting our attention and thwarting good science. Silver contends that these problems mainly result from our tendency to mistake noise for signals. The human brain is wired to detect patterns amidst an abundance of information. From an evolutionary perspective, the brain developed ways of quickly generalizing about both potential dangers and promising food sources. Yet our brain’s wiring for survival, the argument goes, is less well-suited to the information age, when too much information is inundating us every day. We cannot see the signal in the noise, or, more accurately put, we often fail to connect the relevant dots in the right way.
Silver urges us to accept the fallibility of our judgment but also to enhance our judgment by thinking probabilistically. In short, he wants us to think like a “quant.” A quant—someone who seeks to quantify most of the problems in life—adheres to an exceedingly enthusiastic belief in the value of mathematical analysis. I use the term quant with respect, not simply because mathematical agility has never been my own strength and I admire this ability in others but also because I recognize the tremendous value that mathematics brings to our daily lives.
Naturally, not everything is quantifiable, and assigning probabilities to nonquantifiable behaviors can easily cause disaster. Part of what makes Silver’s book so sensible is that he freely admits the value in combining mathematical with human observations. In his chapter on weather forecasts, he observes that the meteorologists themselves can often eyeball a weather map and detect issues that their own algorithms would be likely to miss. And when discussing baseball players’ future fortunes, Silver shows that the best predictions come when quants and scouts can both provide their insights. Software