Everything Is Obvious_ _Once You Know the Answer - Duncan J. Watts [73]
So far, so good. But there are also many areas of business—as well as of government and policy—that rely on predictions that do not quite fit into this supercrunching mold. For example, whenever a book publisher decides how much of an advance to offer a potential author, it is effectively making a prediction about the future sales of the proposed book. The more copies the book sells, the more royalties the author is entitled to, and so the more of an advance the publisher should offer to prevent the author from signing with a different publisher. But if in making this calculation, the publisher overestimates how well the book will sell, it will end up overpaying the author—good for the author but bad for the publisher’s bottom line. Likewise when a movie studio decides to green-light a project, it is effectively making a prediction about the future revenues of the movie, and thus how much it can afford to spend making and marketing it. Or when a drug company decides to proceed with the clinical testing stage of a new drug, it must justify the enormous expense in terms of some prediction about the likely success of the trial and the eventual market size for the drug.
All these lines of business therefore depend on predictions, but they are considerably more complicated predictions than predictions about the number of flu cases expected in North America this winter, or the probability that a given user will click on a given ad online. When a publisher offers an advance for a book, the book itself is typically at least a year or two away from publication; so the publisher has to make a prediction not only about how the book itself will turn out but also what the market will be like for that kind of book when it is eventually published, how it will be reviewed, and any number of other related factors. Likewise predictions about movies, new drugs, and other kinds of business or development projects are, in effect, predictions about complex, multifaceted processes that play out over months or years. Even worse, because decision makers are constrained to making only a handful of such decisions every year, they do not have the luxury of averaging out their uncertainty over huge numbers of predictions.
Nevertheless, even in these cases, decision makers often have at least some historical data on which to draw. Publishers can keep track of how many copies they have sold of similar books in the past, while movie studios can do the same for box office revenues, DVD sales, and merchandising profits. Likewise, drug companies can assess the rates with which similar drugs have succeeded in reaching the market, marketers can track the historical success of comparable products, and magazine publishers can track the newsstand sales of previous cover stories. Decision makers often also have a lot of other data on which to draw—including market research, internal evaluations of the project in question, and their knowledge of the industry in general. So as long as nothing dramatic changes in the world between when they commit to a project and when it launches, then they are still in the realm of predictions that are at least possible to make reliably. How should they go about making them?
MARKETS, CROWDS, AND MODELS
One increasingly popular method is to use what is called a prediction market—meaning a market in which buyers and sellers can trade specially designed securities whose prices correspond to the predicted probability that a specific outcome will take place. For example, the day before the 2008 US presidential election, an investor could have paid $0.92 for a contract in the Iowa Electronic Markets—one of the longest-running and best-known