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A Better Crystal Ball

2 39 13

Every policy is a prediction. Tax cuts will boost the economy. Sanctions will slow Iran’s nuclear program. Travel bans will limit the spread of COVID-19. These claims all posit a causal relationship between means and ends. Regardless of party, ideology, or motive, no policymaker wants his or her recommended course of action to produce unanticipated consequences. This makes every policymaker a forecaster. But forecasting is difficult, particularly when it comes to geopolitics—a domain in which the rules of the game are poorly understood, information is invariably incomplete, and expertise often confers surprisingly little advantage in predicting future events.

These challenges present practical problems for decision-makers in the U.S. government. On the one hand, the limits of imagination create blind spots that policymakers tend to fill in with past experience. They often assume that tomorrow’s dangers will look like yesterday’s, retaining the same mental map even as the territory around them changes dramatically. On the other hand, if policymakers addressed all imaginable threats, the United States would need so large and expensive a national security establishment that the country could do little else. By many measures, it is nearing this point already. The United States has military bases in more than 70 countries and territories, boasts more than four million federal employees with security clearances, and fields 1.3 million active-duty troops, with another million in reserve. According to one estimate, the United States spends $1.25 trillion annually on national security. When it comes to anticipating the future, then, the United States is getting the worst of both worlds. It spends untold sums of money preparing yet still finds itself the victim of surprise—fundamentally ill equipped for defining events, such as the emergence of COVID-19.

There is a better way, one that would allow the United States to make decisions based not on simplistic extrapolations of the past but on smart estimates of the future. It involves reconciling two approaches often seen to be at philosophical loggerheads: scenario planning and probabilistic forecasting. Each approach has a fundamentally different assumption about the future. Scenario planners maintain that there are so many possible futures that one can imagine them only in terms of plausibility, not probability. By contrast, forecasters believe it is possible to calculate the odds of possible outcomes, thereby transforming amorphous uncertainty into quantifiable risk. Because each method has its strengths, the optimal approach is to combine them. This holistic method would provide policymakers with both a range of conceivable futures and regular updates as to which one is likely to emerge. For once, they could make shrewd bets about tomorrow, today.

Although widely used in business today, the first element of this duo—scenario planning—grew out of post–World War II national security concerns, specifically the overwhelming uncertainty of the nuclear revolution. Previously, martial experience was thought to offer some guidance through the fog of war. Nuclear weapons, however, presented a novel problem. With the newfound ability to destroy each other as functioning societies in a matter of minutes or hours, the United States and the Soviet Union faced an unprecedented situation. And unprecedented situations are, by definition, uncertain. They lack any analogy to the past that would allow decision-makers to calculate the odds of possible outcomes.

Still, early U.S. efforts at nuclear-war planning sought to turn that problem into a calculable one. During World War II, the Allies had great success with the new field of operations research, the application of statistical methods to improve the outcome of tactical engagements. After the war, the RAND Corporation—a “think factory” that the U.S. Air Force established as a repository for leading researchers—hoped to parlay this success into a new, more rational approach to war, based less on the intuition of generals and more on the quantification afforded by models and data.

Unfortunately, methods that worked at the tactical level proved nearly farcical at the strategic level. As the historian David Jardini has chronicled, RAND’s first attempt to model a nuclear strategy ignored so many key variables that it nonsensically called for deploying a fleet of aging turboprop bombers that carried no bombs because the United States did not have enough fissile material to arm them; the goal was simply to overwhelm Soviet air defenses, with no regard for the lives of the pilots. In the wake of such failures, it became clear that analysts could not entirely banish uncertainty. In 1960, even Charles Hitch, a man predisposed to calculation by dint of being RAND’s top economist and president of what was then the Operations Research Society of America, cautioned, “No other characteristic of decision-making is as pervasive as uncertainty.”

That, of course, raised the question of how to formulate sensible strategy. Unexpectedly, it was a RAND mathematician and physicist, Herman Kahn, who offered an answer. If the lived past could not shape strategy, perhaps the imagined future could. Frustrated with RAND’s attempts to scientize war, Kahn devoted himself to crafting scenarios in the pursuit of “ersatz experience” that would prepare the United States for the future through what were essentially thought experiments. Policymakers could use these scenarios as “artificial ‘case histories’ and ‘historical anecdotes,’” Kahn wrote, thus making up for a lack of actual examples or meaningful data. They would provide analogies where there were none.

Early methods of generating scenarios were often freewheeling and discursive. But after scenario planning migrated to the business world, it took on more structured........

© Foreign Affairs

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