The Predictioneer’s Game: Using the Logic of Brazen Self-Interest to See and Shape the Future

it’s not especially hard to do.

To get a sense of how readily experts know the information needed to make reliable predictions, and to see how easily the information can be turned into numbers, try an experiment. Interview someone you think of as really knowing about your friends and family, including perhaps yourself. Pick an issue that is important to your family or friends. It doesn’t have to be about world affairs; it could be about where to have dinner, or what movie to see, or whatever else leads to disagreements. The easiest sort of issue to do as a first try is what I call a beauty contest. Say you and some friends are trying to choose between two movies. Anyone who really truly wants to see The Sound of Music (or fill in whatever first-run movie might grab your fancy) gets a value of 100, and anyone really committed to seeing A Clockwork Orange (another great old film) gets a 0. Then you should be able to rate how strongly each friend leans toward one movie or the other. Any who are truly indifferent get a 50; anyone leaning slightly toward A Clockwork Orange might be close to 50, say at 40 or 45, and so on. You or the “expert” you are interviewing need to calibrate their strength of feeling as accurately as possible. This way, movie preferences are turned into one value for each chooser—that is, each family member and/or friend involved in shaping the decision. The process is exactly the same, although the choices may be more complex, whether deciding what movie to see or addressing North Korea’s nuclear choices. Sure, the stakes differ, but once the essential facts are extracted, the process of turning stated objectives into a predicted outcome is the same.

Now estimate how eager each friend or family member—each player—is to weigh in on the decision. If you think a family member will drop what he or she is doing to discuss the movie to see, rate that person’s “salience” (variable 3 on my list) close to 100 (no one is ever really at 100). The less focused you think someone is on the movie choice, the lower the salience score. If a family member is the sort who would say, “Look, I’ll go to whatever movie you choose, but really I don’t have time to get involved in picking which one,” that’s somewhere around a 10. On the other hand, if you think a friend will say, “I’m busy right now but call me back in ten minutes,” that’s pretty high salience. “Call me back in an hour” is lower, and “Call me back next week” is much lower. With a bit of effort, it shouldn’t be that hard to calibrate how important the movie decision is to each person compared to the other decisions they have to make (not compared to each other, mind you, but compared to other things they need to do or deal with).

Finally, figure out who you think has the most influence among your friends or family members if you assume that everyone thinks the choice is equally important. Give the person credited as being most persuasive a score of 100 and rate everybody else relative to that. So if Harry is 100 and Jane is 60 and John is 40 in potential clout, and Jane and John want to see The Sound of Music and Harry wants to see A Clockwork Orange, then that means that John and Jane together just offset Harry’s ability to persuade if they all care equally intensely about choosing a movie. If Jane were 60 and John were 70, then, all else being equal, they could persuade Harry to moderate his view and give more consideration to the movie Jane and John prefer. Of course, if someone else supported Harry’s choice, that might create a strong enough coalition to defeat John and Jane. The dynamics get complicated, but the basic idea should be straightforward.

It’s important to note that most of us make these assessments of interests in any situation. We just do such calculations naturally with relative judgments of where people stand on a given issue. What I’ve sketched above is simply a formalization of that natural process—which becomes all the more needed the more complicated the problems in question become.

By now you are probably thinking, Sure, people can fill in numbers to the questions, but it’s just guesswork. Ask two experts the same question and you’ll get two different answers. Guess what—that’s not true. If it were, then there is barely any chance that a model like mine could achieve any consistent accuracy. There would be too much luck involved. In fact, the CIA has checked out the risk that different experts give greatly different answers leading to greatly different predictions. They found little variation in the predictive results from the sort of modeling I do, even when the people asked had dramatically different access to information. Academic experts, for instance, generally do not know the classified information that intelligence analysts have access to. Yet both groups tend to provide data so similar, wherever it’s from, that the results hardly change when moving across these experts. Even more surprisingly, the answers often don’t change much when the inputs for the computer model are put together by undergraduate students with no expertise at all.

Once I was teaching an undergraduate class at the University of Rochester while also investigating how best to get Ferdinand Marcos to resign as head of the Philippine government and create an atmosphere ripe for a free election in that country. William Casey, then Ronald Reagan’s director of intelligence, asked me to study this problem, and I was locked in a (cold) lead-lined vault at CIA headquarters to do it. I had access to classified information, but was not even allowed to read my own report when it was finished. The report was for the eyes of only the president, the vice president, the secretaries of state and defense, the national security adviser, and a few others. Meanwhile, my students worked on the same problem and were given access to the computer program I had developed to help solve such problems. They extracted the required information from magazines such as The Economist and newspapers such as The New York Times and fed their data into the computer. Ninety percent of them arrived at the same conclusions I reached in the lead- lined vault, and those conclusions about strategy proved to work rather well. This should tell us that the information needed to make good predictions is not terribly exotic; it does not require years of learning some other country’s language, history, and culture, although all of that is a big help. It should also tell us that a lot of classified information is easily reproduced from open, public sources for those who are willing to work at it.

Now that we have an idea of where and how to find our information, working out how to get what any given player wants is the key to generating predictions, and ultimately to engineering outcomes. Since everyone involved in a given problem is concerned with getting what they want, their behavior and choices are predictable. Each and every one of them will act so as to lead to the attainable outcome that is closest to what they want, given what they believe about the situation.

What might people’s goals be in a generic sense? Whatever the specific issue, I always operate under the assumption that everyone wants two things when making a decision (although different people weight those two things differently). One thing they want is a decision that is as close as possible to the choice they advocate. The second thing they want is glory—the ego satisfaction that comes from the recognition by others that they played an important part in putting a deal together.

Some people care so much about getting credit for putting an agreement together that they’re willing to shift their position dramatically if that will help promote a deal. Others prefer to go down in a blaze of glory, backing a losing position rather than making concessions that would make a deal feasible. Everyone shares these two goals: get their preferred outcome, and get credit for any outcome. Different people value one or the other differently, and so they’re willing to trade away returns on one dimension to get better returns on the other.

Let’s return to the North Koreans. After interviewing experts and compiling research, we know three important pieces of information about each player: what they say they want, how much they care, and how influential they can be. In the case of North Korea, the range of policy choices is depicted in figure 4.1, both in terms of substantive meaning and numerical value. (One easy way to get numbers out of policy stances is to ask experts to make marks on a line for several policy options, emphasizing that they should be spaced to reflect how close or distant the choices are substantively from each other. Then a ruler can be used to measure the distances, and voila, a simple numeric scale has been created.)

My 2004 study of North Korea identified more than fifty players in this complicated international game. Two, Kim Jong Il and George W. Bush, had a veto, which meant that no deal could be struck without their support. Kim