Everything Is Obvious_ _Once You Know the Answer - Duncan J. Watts [50]
CHAPTER 5
History, the Fickle Teacher
The message of the previous three chapters is that commonsense explanations are often characterized by circular reasoning. Teachers cheated on their students’ tests because that’s what their incentives led them to do. The Mona Lisa is the most famous painting in the world because it has all the attributes of the Mona Lisa. People have stopped buying gas-guzzling SUVs because social norms now dictate that people shouldn’t buy gas-guzzling SUVs. And a few special people revived the fortunes of the Hush Puppies shoe brand because a few people started buying Hush Puppies before everyone else did. All of these statements may be true, but all they are really telling us is that what we know happened, happened, and not something else. Because they can only be constructed after we know the outcome itself, we can never be sure how much these explanations really explain, versus simply describe.
What’s curious about this problem, however, is that even once you see the inherent circularity of commonsense explanations, it’s still not obvious what’s wrong with them. After all, in science we don’t necessarily know why things happen either, but we can often figure it out by doing experiments in a lab or by observing systematic regularities in the world. Why can’t we learn from history the same way? That is, think of history as a series of experiments in which certain general “laws” of cause and effect determine the outcomes that we observe. By systematically piecing together the regularities in our observations, can we not infer these laws just as we do in science? For example, imagine that the contest for attention between great works of art is an experiment designed to identify the attributes of great art. Even if it’s true that prior to the twentieth century, it might not have been obvious that the Mona Lisa was going to become the most famous painting in the world, we have now run the experiment, and we have the answer. We may still not be able to say what it is about the Mona Lisa that makes it uniquely great, but we do at least have some data. Even if our commonsense explanations have a tendency to conflate what happened with why it happened, are we not simply doing our best to act like good experimentalists?1
In a sense, the answer is yes. We probably are doing our best, and under the right circumstances learning from observation and experience can work pretty well. But there’s a catch: In order to be able to infer that “A causes B,” we need to be able to run the experiment many times. Let’s say, for example, that A is a new drug to reduce “bad” cholesterol and B is a patient’s chance of developing heart disease in the next ten years. If the manufacturer can show that a patient who receives drug A is significantly less likely to develop heart disease than one who doesn’t, they’re allowed to claim that the drug can help prevent heart disease; otherwise they can’t. But because any one person can only either receive the drug or not receive it, the only way to show that the drug is causing anything is to run the “experiment” many times, where each person’s experience counts as a single run. A drug trial therefore requires many participants, each of whom is randomly assigned either to receive the treatment or not. The effect of the drug is then measured as the difference in outcomes between the “treatment” and the “control” groups, where the smaller the effect, the larger the trial needs to be in order to rule out random chance as the explanation.
In certain everyday problem-solving situations, where we encounter more or less similar circumstances over and over again, we can get pretty close to imitating the conditions of the drug trial. Driving home from work every day, for example, we can experiment with different routes or with different departure times. By repeating these variations many times, and assuming that traffic on any given day