A Sense of the Enemy: The High Stakes History of Reading Your Rival's Mind (29 page)

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 programs as well as human observations can easily go awry, and errors are most likely to occur when either the computer or the person is focused on the wrong data. If the software is designed to project a minor league pitcher’s future strike-outs but fails to include information on the weakness of the batters that pitcher faced, then the pitcher will be in for a rough ride when he reaches the major leagues. By the same token, scouts who assess a player’s promise by the athlete’s imposing physique might overlook some underlying flaws. Though he does not state it directly, Silver finds that scouts do better when they focus on pattern breaks. “I like to see a hitter, when he flails at a pitch, when he takes a big swing and to the fans it looks ridiculous,”
one successful scout told Silver, “I like to look down and see a smile on his face. And then the next time—bam—four hundred feet!” There’s no substitute for resilience, and it can best be seen at those times when things don’t go as planned.
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While prudent, thoughtful quantification can serve us well in many areas, it cannot be applied in every area. As a case in point, toward the close of his book, Silver turns to intelligence assessments, drawing specifically on the failure to predict the attacks on Pearl Harbor and 9/11. On the one hand he advocates that intelligence analysts must remain open to all possibilities, particularly by assigning probabilities to all imaginable scenarios, no matter how remote they might seem. On the other hand, he assumes that analyzing individuals is a less profitable endeavor. Silver writes: “At a microscopic level, then, at the level of individual terrorists or individual terror schemes, there are unlikely to be any magic bullet solutions to predicting attacks. Instead, intelligence requires sorting through the spaghetti strands of signals . . .” Of course it is true that we have no magic bullets. Statesmen do, however, possess ways of improving their odds. Rather than mining the trove of big data for patterns in their enemies’ behavior, or sorting through a sticky web of conflicting signals, statesmen can focus instead on the moments of pattern breaks. Again, it is obvious that this will not guarantee successful predictions, but it can help illuminate what the enemy truly seeks.

As a quant, Silver is understandably less comfortable analyzing how individuals behave. His forte is calculating how groups of individuals are likely to behave over the long run most of the time. Here then is a crucial difference between the type of predictions made by Silver and his fellow quants and those predictions made by statesmen at times of conflict. Quantitative assessments work best with iterative, not singular, events. The financial investor, for example, can come out ahead after years of profits and losses, as long as his overall portfolio of investments is profitable most of the time. Depending on the arena, a good strategy could even be one that makes money just 60 percent of the time, as is a common benchmark in personal finance. The same is true of the poker player, baseball batter, or chess master. When the game is iterative, played over and over, a winning strategy just has to be marginally, though consistently, better than that of a coin flip. But leaders, in painful contrast, have to get it right this one time, before lives are
lost. In the dangerous realm of international conflict, statesmen must be 100 percent right when it matters most. They cannot afford to repeat again and again the Nazi invasion of Russia or the American escalation in Vietnam. Unlike in competitive poker, the stakes in this setting are simply too high.

The political scientist Bruce Bueno de Mesquita is arguably the king of quants when it comes to predicting foreign affairs. Frequently funded by the Defense Department, Bueno de Mesquita insists that foreign affairs can be predicted with 90 percent accuracy using his own secret formula. Of course, most of his 90 percent accuracy likely comes from predictions that present trends will continue—which typically they do.

The crux of Bueno de Mesquita’s model rests largely on the inputs to his algorithm. He says that in order to predict what people are likely to do, we must first approximate what they believe about a situation and what outcomes they desire. He insists that most of the information we need to assess their motives is already available through open sources. Classified data, he contends, are rarely necessary. On at least this score, he is probably correct. Though skillful intelligence can garner some true gems of enemy intentions, most of the time neither the quantity nor the secrecy of information is what matters most to predicting individual behavior. What matters is the relevant information and the capacity to analyze it.

The crucial problem with Bueno de Mesquita’s approach is its reliance on consistently accurate, quantifiable assessments of individuals. A model will be as weak as its inputs. If the inputs are off, the output must be off—and sometimes dramatically so, as Bueno de Mesquita is quick to note on his own website: “Garbage in, garbage out.” Yet this awareness does not dissuade him from some remarkable assertions. Take for example the assessments of Adolf Hitler before he came to power. Bueno de Mesquita spends one section of his book,
The Predictioneer’s Game
, explaining how, if politicians in 1930s Germany had had access to his mathematical model, the Socialists and Communists would have seen the necessity of cooperating with each other and with the Catholic Center Party as the only means of preventing Hitler’s accession to Chancellor.
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He assumes that Hitler’s opponents could easily have recognized Hitler’s intentions. He further assumes that the Catholic Center Party could have been persuaded to align against the Nazis, an assumption
that looks much more plausible in a post–World War II world. In 1932, the various Party leaders were surely not envisioning the future as it actually unfolded. Their actions at the time no doubt seemed the best choice in a bad situation. No mathematical model of the future would likely have convinced them otherwise. Assessments are only as good as the assessors, and quantifying bad assessments will yield useless, if not disastrous, results.

None of this means that all efforts at prediction are pure folly. Bueno de Mesquita’s larger aim is worthy: to devise more rigorous methods of foreseeing behavior. An alternative approach to his quantitative metrics is to develop our sense for how the enemy behaves. Though less scientific, it could be far more profitable, and it is clearly very much in need.

Quants are skilled at harnessing algorithms for spotting pattern recognition and also pattern breaks. But their methods work best when their algorithms can scan big data sets of iterative events, focusing on the numbers that truly count. Anyone who has ever received a call from a credit card company alerting her to unusual activity on her account knows that MasterCard and Visa employ sophisticated algorithms to identify purchasing patterns and sudden deviations. This is a realm in which computers provide enormous added value. But in the realms where human behavior is less amenable to quantification, we must supplement number crunching with an old-fashioned people sense. It is here that meaningful pattern breaks can contain some clues. Perhaps surprisingly, within the heart of America’s defense establishment, one man and his modest staff have spent decades refining their strategic empathy. Their successes, as well as their failures, offer useful tips for those who would predict their enemies’ behavior.

Yoda in the Pentagon

In October 1973, Arab states attacked Israel with overwhelming strength in numbers. The Egyptians deployed some 650,000 soldiers—a massive military force in its own right. Syria, Iraq, and other Arab states added another quarter of a million troops. Against these 900,000 enemies Israel could muster no more than 375,000 soldiers, and 240,000 of those were from the reserves. But the war was really a battle of tanks, and on this score the numbers looked even more daunting. Israel’s 2,100 tanks
confronted a combined Arab fleet of 4,500.
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On the northern front when the war began, Syria massed 1,400 tanks against 177 Israeli—a crushing ratio of eight to one. Given the extraordinary disparity of force, after Israel recovered from initial losses and decisively won the war, most Western observers interpreted the conflict as proof of Israel’s unbreakable will to survive. Yet when Andrew Marshall and his staff analyzed the numbers, they saw something else entirely.

Tucked into a nondescript section deep within the Pentagon’s labyrinthine rings, the Office of Net Assessment had just been created the previous year. Studying the war’s less glamorous details and drawing on the substantial research of others, Marshall and his team discovered an Egyptian army with a Soviet-style flaw. The entire military was astonishingly short on maintenance. When one of its tanks became damaged in battle, Egypt had no effective means for repairing it. Israel, in contrast, had well-trained technicians able to make rapid repairs. It turned out that on average Israeli tanks returned to battle three times, but Egyptian tanks were used only until damaged. In other words, the initial number of tanks was not the number that mattered.

Superior force, by standard measures, did not win. The number that truly counted was the one that revealed a tank’s likely longevity. Counting tanks before the war was a necessary but insufficient exercise. It didn’t tell observers what they needed to know in order to assess the net strength of each side in the conflict. “What impressed me about the ’73 War,” Marshall explains, “was how asymmetric it was. Israel was not only much better prepared to recover and repair its tanks, it also dominated the battlefield, making recovery possible.”

When Marshall and his analysts next looked at the Soviet Union’s capacity for repairs, they found that the United States had a distinct and meaningful advantage. The bulk of the Soviet forces were comprised of conscripts, young men compelled to serve for two years in the army or three in the navy. Most were poorly trained and lacking technical know-how. American soldiers, conversely, were given better, longer, and more specialized training. Each unit working on ships or aircraft contained men able to perform some repairs when necessary. The Soviet military didn’t work that way. Most of the time, when an engine or other critical part of an aircraft, tank, or ship malfunctioned, the Soviets had to send that part back to a depot or factory for repair. The Soviet Air Force, for
example, purchased six engines for each engine position on its aircraft. The United States bought only one and a quarter—a dramatic cost-saving measure when multiplied by thousands of planes. Those costs, of course, counted not just in rubles but in time. The Soviet delays in servicing aircraft parts meant that American planes would be available more of the time when needed most.

Likewise, American ships had on-board crews that could make repairs on the spot, but Soviet naval crews did not possess the same level of maintenance training. The longer their ships were at sea, the less effectively they would function. The numbers of ships in each side’s fleet was not important. Marshall recognized that less obvious asymmetries mattered far more. The simple and seemingly insignificant difference in repair capabilities meant that Soviet forces would come under extreme pressure during a protracted conflict.

Ensuring that America could continue to strike and engage the Soviets in a prolonged military conflict meant that the United States would ultimately have the advantage. It was this type of thinking that contributed to America’s Cold War strategy. In Marshall’s case, the insight derived not from sophisticated algorithms but from unorthodox thinking about how best to compare competing military forces.

The Office of Net Assessment (ONA) director, Andrew Marshall, once a mathematical whiz kid, has one overriding mission: to assess the balance between competing militaries. At age ninety-two (that is not a typographical error), Marshall is not merely sharp but deeply engaged in running ONA’s affairs. The office churns out countless reports analyzing military and strategic issues ranging across the globe, examining everything from advances in neuro-pharmacology to Swedish innovations in submarine design to the future of microrobot warriors, always with an eye to their impact on American national security. The fact that Marshall has remained ONA’s sole director since its inception, serving eight Presidents and more than a dozen secretaries of defense over the past forty years, suggests that he either is exceedingly astute at political survival, provides a product of substantial value, or both.
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During the Cold War, Marshall and ONA paid careful attention to the numbers and patterns that mattered most—the ones that constrained enemy behavior. Many credit Marshall with contributing to the policy of spending the Soviet Union into bankruptcy. He allegedly
encouraged keeping long-range bombers in service while developing a new generation, both of which forced the Soviets to invest in costly air defense systems, though Marshall would not confirm this. Perhaps it was this seeming inscrutability, maybe his political longevity, or simply his age that earned him the affectionate nickname “Yoda.”