Everything Is Obvious_ _Once You Know the Answer - Duncan J. Watts [65]
The fatal flaw in Laplace’s vision, therefore, is that his demon works only for simple systems. Yet pretty much everything in the social world—from the effect of a marketing campaign to the consequences of some economic policy or the outcome of a corporate plan—falls into the category of complex systems. Whenever people get together—in social gatherings, sports crowds, business firms, volunteer organizations, markets, political parties, or even entire societies—they affect one another’s thinking and behavior. As I discussed in Chapter 3, it is these interactions that make social systems “social” in the first place—because they cause a collection of people to be something other than just a collection of people. But in the process they also produce tremendous complexity.
THE FUTURE IS NOT LIKE THE PAST
The ubiquity of complex systems in the social world is important because it severely restricts the kinds of predictions we can make. In simple systems, that is, it is possible to predict with high probability what will actually happen—for example when Halley’s Comet will next return or what orbit a particular satellite will enter. For complex systems, by contrast, the best that we can hope for is to correctly predict the probability that something will happen.10 At first glance, these two exercises sound similar, but they’re fundamentally different. To see how, imagine that you’re calling the toss of a coin. Because it’s a random event, the best you can do is predict that it will come up heads, on average, half the time. A rule that says “over the long run, 50 percent of coin tosses will be heads, and 50 percent will be tails” is, in fact, perfectly accurate in the sense that heads and tails do, on average, show up exactly half the time. But even knowing this rule, we still can’t correctly predict the outcome of a single coin toss any more than 50 percent of the time, no matter what strategy we adopt.11 Complex systems are not really random in the same way that a coin toss is random, but in practice it’s extremely difficult to tell the difference. As the Music Lab experiment demonstrated earlier, you could know everything about every person in the market—you could ask them a thousand survey questions, follow them around to see what they do, and put them in brain scanners while they’re doing it—and still the best you could do would be to predict the probability that a particular song will be the winner in any particular virtual world. Some songs were more likely to win on average than others, but in any given world the interactions between individuals magnified tiny random fluctuations to produce unpredictable outcomes.
To understand why this kind of unpredictability is problematic, consider another example of a complex system about which we like to make predictions—namely, the weather. At least in the very near future—which generally means the next forty-eight hours—weather predictions are actually pretty accurate, or as forecasters call it, “reliable.” That is, of the days when the weather service says there is a 60 percent chance of rain, it does, in fact, rain on about 60 percent of them.12 So why is it that people complain about the accuracy of weather forecasts? The reason is not that they aren’t reliable—although possibly they could be more reliable than they are—but rather that reliability isn’t the kind of accuracy that we want. We don’t want to know what is going to happen 60 percent of the time on days like tomorrow. Rather, we want to know what is actually going to happen tomorrow—and tomorrow, it will either rain or it will not. So when we hear “60 percent chance of rain tomorrow,” it’s natural to interpret the information as the weather service telling us that it’s probably going to rain tomorrow. And when it fails to rain almost half the