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. 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 of the time. A rule that says “over the long run, 50% of coin tosses will be heads, and 50% 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 predict the outcome of a single coin toss any more than 50% of the time, no matter what strategy we adopt. 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.
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