Like every model, it needed data. The State Department’s phone call about India came in just as I was trying to figure out where to get data to feed into my war and peace model. The timing was perfect. The phone call got me thinking that maybe war and peace decisions really aren’t that different from everyday political confrontations. Sure, the stakes are higher—people get killed in wars—but then any politician seeking high office or about to lose high office sees the personal political stakes as pretty darn high. Probably all of us make similar calculations about how to advance our own well-being in any complex situation involving big risks and potentially big rewards, whether that involves politics, business, or daily life.
The State Department was pressing me for an answer and I wanted to help them. I also wanted to see how well my new model worked. I decided to find out whether the model could really be a useful tool to sort out the political infighting in India. Linking that model to Indian politics was a huge “Aha!” moment for me, one that would change the rest of my life.
I grabbed a yellow pad and picked my own brain, putting together the information the model needed. I wrote down a list of everyone I thought would try to influence the selection of India’s next government. For each of those people (political party leaders, members of India’s parliament, and some members of critical state governments) I also wrote down my estimate of how much clout each had, what their preference was between the various plausible candidates for prime minister, and how much they cared about trying to shape that choice. With just one page of my yellow pad filled with numbers, I had all the information the computer program needed to predict what would happen. I plugged those data into my little program and crunched the numbers overnight. When the computing was done the next morning—computers were slow in those days—I pored over the hundred or so pages of calculated values to see what the model’s predictions looked like.
I thought I had personal insight into what was going to happen in India. My “pundit” knowledge had led me to believe that a man named Jagjivan Ram would be the next prime minister. He was a popular and prominent politician who was better liked than his main rivals for the prime minister’s job. I was confident that he was untouchable—truly unbeatable—in the political arena, and not just in the sense of his caste status. He had paid his political dues and it seemed like his time had come. Many other India watchers thought the same thing. Imagine my surprise, then, when my computer program, written by me and fed only with my data, predicted an entirely different result. It predicted that Charan Singh would become prime minister and that he would include someone named Y. B. Chavan in his cabinet, and that they would gain support—albeit briefly—from Indira Gandhi, then the recently ousted prime minister. The model also predicted that the new Indian government would be incapable of governing and so would soon fall.
I found myself forced to choose between personal opinion and my commitment to logic and evidence as the basis for coming to conclusions about politics. I believed in the logic behind my model and I believed in the correctness of the data I had jotted down. After staring at the output, working out how my own program came to a conclusion so different from my personal judgment, I chose science over punditry. In fact, I told colleagues at Rochester what the model’s prediction was even before I reported back to the State Department. When I spoke with the official at State he was taken aback. He noted that no one else was suggesting this result and that it seemed strange at best. He asked me how I had come to this judgment. When I told him I’d used a computer program based on a model of decision making that I was designing, he just laughed and urged me not to repeat that to anyone.
A few weeks later, Charan Singh became the prime minister with Y. B. Chavan as his deputy prime minister, with support from Indira Gandhi. And a few months after that, Charan Singh’s government unraveled, Indira Gandhi withdrew her support, and a new election was called, just as the computer-generated forecast had indicated. This got me pretty excited. Here was a case where my personal judgment had been wrong, and yet my knowledge was the only source of information the computer model had. The model came up with the right answer and I didn’t. Clearly there were at least two possibilities: I was just lucky, or I was onto something.
Luck is great, but I’m not a great believer in luck alone as an explanation for results. Sure, rare events happen—rarely. I set out to push my model by testing it against lots of cases, hoping to learn whether it really worked. I applied it to prospective leadership changes in the Soviet Union; to questions of economic reform in Mexico and Brazil; and to budgetary decisions in Italy—that is, to wide-ranging questions about politics and economics. The model worked really well on these cases—so well, in fact, that it attracted the attention of people in the government who heard me present some of the analyses at academic conferences. Eventually this led to a grant from the Defense Advanced Research Projects Agency (DARPA), a research arm of the Department of Defense (and the sponsors of research that fostered the development of the Internet long before Al Gore “invented” it). They gave me seventeen issues to examine, and as it happened, the model—by then somewhat more sophisticated—got all seventeen right. Government analysts who provided the data the model needed—we’ll talk more about that later—didn’t do nearly as well. Confident that I was onto something useful, I started a small consulting company with a couple of colleagues who had their own ideas about how to predict big political events. Now, many years later, I operate a small consulting firm with my partner and former client, Harry Roundell. Harry, formerly a managing director at J. P. Morgan, and I apply a much more sophisticated version of my 1979 model to interesting business and government problems. We’ll see lots of examples in the pages to come.
It’s easy to see if predictions are right or wrong when they are precise, and almost impossible to judge them when they are cloaked in hazy language. In my experience, government and private businesses want firm answers. They get plenty of wishy-washy predictions from their staff. They’re looking for more than “On the one hand this, but on the other hand that”—and I give it to them. Sometimes that leads to embarrassment, but that’s the point. If people are to pay attention to predictions, they need real evidence as to the odds that the predictions are right. Being reluctant to put predictions out in public is the first sign that the prognosticator doesn’t have confidence in what he’s doing.
According to a declassified CIA assessment, the predictions for which I’ve been responsible have a 90 percent accuracy rate.6 This is not a reflection of any great wisdom or insight on my part—I have little enough of both, and believe me, there are plenty of ivy-garlanded professors and NewsHour intellectuals who would agree. What I do have is the lesson I learned in my “Aha!” moment: Politics is predictable. All that is needed is a tool—like my model—that takes basic information, evaluates it by assuming everyone does what they think is best for them, and produces reliable assessments of what they will do and why they will do it. Successful prediction does not rely on any special personal qualities. You don’t need to walk around conjuring the future, plucking predictions out of thin air. There’s no need for sheep entrails, tea leaves, or special powers. The key to good prediction is getting the logic right, or “righter” than any way that is achieved by other means of prediction.
Accurate prediction relies on science, not artistry—and certainly not sleight of hand. It is a reflection of the power of logic and evidence, and testimony to the progress being made in demystifying the world of human thought and decision. There are lots of powerful tools for making predictions. Applied game theory, my chosen method, is right for some problems but not all. Statistical forecasting is a terrific way to address questions that don’t involve big breaks from past patterns. Election prognosticators, whether at universities, polling services, or blogs on the Web (like Nate Silver, the son of an old family friend) all estimate the influence of variables on past outcomes and project the weight of that influence onto current circumstances. Online election m
arkets work well too. They work just the way jelly bean contests work. Ask lots of people how many jelly beans there are in a jar, and just about no one will be close to being right, but the average of their predictions is often very close to the true number. These methods have terrific records of accuracy when applied to appropriate problems.
Statistical methods are certainly not limited to just studying and predicting elections. They help us understand harder questions too, such as what leads to international crises or what influences international commerce and investments. Behavioral economics is another prominent tool grounded in the scientific method to derive insights from sophisticated statistical and experimental tests. Steven Levitt, one of the authors of Freakonomics, has introduced millions of readers to behavioral economics, giving them insights into important and captivatingly interesting phenomena.
Game-theory models, with their focus on strategic behavior, are best for predicting the business and national-security issues I get asked about. I say this having done loads of statistical studies on questions of war and peace, nation building, and much more, as well as historical and contemporary case studies. Not every method is right for every problem, but for predicting the future the way I do, game theory is the way to go, and I’ll try to convince you of that not only by highlighting the track record of my method, but also by daring to be embarrassed later in this book when I make predictions about big future events.
Prediction with game theory requires learning how to think strategically about other people’s problems the way you think about your own, and it means empathizing with how others think about the same problems. A fast laptop and the right software help, but any problem whose outcome depends on many people and involves real or imagined negotiations is susceptible to accurate forecasting drawn from basic methods.
In fact, not only can we learn to look ahead at what is likely to happen, but—and this is far more useful than mere prediction and the visions of seers past and present—we can learn to engineer the future to produce happier outcomes. Sadly, our government, business, and civic leaders rarely take advantage of this possibility. Instead, they rely on wishful thinking and yearn for “wisdom” instead of seeking help from cutting-edge science. In place of analytic tools they count on the ever-present seat of their pants.
We live in a world in which billions—even trillions—of dollars are spent on preparations for war. Yet we spend hardly a penny on improving decision making to determine when or whether our weapons should be used, let alone how we can negotiate successfully. The result: we get bogged down in far-off places with little understanding of why we are there or how to advance our goals, and even less foresight into the roadblocks that will lie in our way. That is no way to run a twenty-first-century government when science can help us do so much better.
Business leaders do no better than their political peers. They spend fortunes doing financial analyses of their expected gains and losses from this or that deal, but they spend virtually nothing analyzing how their counterparts on the other side of the table think about their own gains and losses. The result: companies have a good idea how much a business is worth to them before they try to buy it, but they don’t know how much they need to pay for it. In my experience, they often pay far too much, or, to look at the other side of the coin, they sell for much less than the buyer was prepared to pay. Too bad for their shareholders.
How can anyone make prudent choices without first thinking through how others will see those choices and react to them? Yet that is how most big decisions are made, blind to anyone else’s point of view. Plowing ahead without much thought to what motivates our rivals, whether in business or in government, is a surefire way to make a mess of things, leaving us to muddle through at best, our hopes pinned to shortsighted decisions.
Decision making is the last frontier in which science has been locked out of government and business. We live in a high-tech age with archaic guesswork guiding life and death decisions. The time for peering into tea leaves or reading astrological charts should be long over. We should leave entertaining divinations to storefront psychics and open the door to science as the new basis for the big decisions of our time.
Curious about how this can be done? The chapters to follow explain how precise predictions can be made. We will see, through illustrative examples from the worlds of national security and business and everyday life, that the problems of war and peace, mergers and acquisitions, litigation, legislation, and regulation—and just about anything else that does not rely on the hidden hand of market forces—can be reliably predicted.
We will see ways to use science, mathematics, and, in particular, the power of game theory to sort out behavior and improve the future. I hope to share with you this cutting-edge world of thought, whose potential, to many, may seem to verge on the mystical. But there is no mystery or mysticism in good prediction. To demonstrate this for you, I will suggest, in Chapter One, how a modest amount of strategic reasoning can help you save hundreds and maybe thousands of dollars the next time you buy a car.
1
WHAT WILL IT TAKE TO PUT YOU IN THIS CAR TODAY?
“GAME THEORY” IS a fancy label for a pretty simple idea: that people do what they believe is in their best interest. That means they pay attention to how others might react if they choose to do one thing or another. Those “others” are anyone believed to be a prospective supporter or opponent. Looking at how their interests intersect or collide is basic to assessing potential outcomes of decision making. To get a good grip on what people are likely to do requires first approximating what they believe about the situation and what they want to get out of it. By estimating carefully people’s wants and beliefs, anyone can make a reliable forecast of what each and every one of them will do. And if you can predict what will happen, then you can also predict what will happen if you alter what people believe about a situation. This is, in short, how we can use the same logic both for prediction and for engineering the future.
I’ll provide a more detailed examination of game theory in the next two chapters, but first, as promised, let me illustrate what I’m talking about with the example of how best to buy a new car.
A new car purchase is a costly bargaining experience for most of us because most of us are bad at it. A little strategic reasoning can go a long way to improve the experience. If you follow the ideas here, you will not only have a happier time buying a car, you’ll also pay a lot less money.
New cars are mostly bought in one of two ways. Most of us go to a dealership, test-drive a vehicle, maybe fall in love with it, and engage in a most unpleasant negotiation over the price. A smaller number of us hate that experience enough that we buy cars through the Internet. This usually means getting price bids from a few local dealers and then going to one of them to get the car. That’s slightly better, but there’s yet a better way to buy cars that I urge you to try.
What’s wrong with the most popular means of car shopping? Pretty much everything. To start with, you, the buyer, invest your time, and probably the time of some family members as well, at a car dealership. The salesperson knows that few people enjoy dealing with them. They know that they rate near the bottom of anybody’s scale of occupational trustworthiness. (The Jobs Rated Almanac weights occupations by numerous factors and reports that auto sales ranks 220th out of 250 occupations.1 Apparently it’s worse to be a taxi driver, cowboy, or roustabout, but not by much.) But there you are subjecting yourself to the salesperson’s pitch, standing before a car dealer in his or her place of business, feeling compelled to haggle over the price, probably to your embarrassment and certainly to your disadvantage. The whole time you’re talking to the dealer you’re revealing information that sets you up to pay too much.
Being there is what game theorists call a “costly signal.” It’s a costly signal because your expenditure of time and energy announces that you want to buy, that there’s a good chance you’ll buy from the dealership you’re visiting rather than go elsewhere, and,
especially if you have kids with you, that you want to get out of there as quickly as possible. That first step, then, your simply being there, translates into strengthening the salesperson’s hand in getting a good price. They believe you’re ready to buy, and you’ve done precious little to dissuade them. Score one for the dealer, none for you.
Now as it happens, costly signals are usually good things for you. They show you’re serious about what you are saying and doing. That can give you credibility, as we will see in later chapters. Unfortunately, costly signals can have just the opposite effect when you’re a shopper. They announce your eagerness to buy, and that makes it tough to get a good deal.
The situation was bad enough when you walked in the dealership door, but it only gets worse once a conversation begins. Although you’ve probably done homework online and know something about the invoice price of the car you want, there’s a great deal you don’t know that the dealer does know. When you say you want a gray car, or a blue one, or yellow, you don’t know whether you’ve picked the hottest color in the greatest demand or a color that hardly anyone wants. You may not even know that there can be a price difference of many hundreds of dollars between choosing, say, red or yellow because the dealer treats that choice just the way options are treated—as one more place to pile on costs. You don’t know enough about the local supply and demand to have a good retort when Pat, the salesperson, tells you the vehicle you want is in short supply. Translation: The invoice price on the Internet? Forget it—that’s not going to happen for the car you want. Score another point for the dealer, and still none for you.
The Predictioneer’s Game: Using the Logic of Brazen Self-Interest to See and Shape the Future Page 2