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FCW : March 15, 2014
fraudulent credit card usage. It also serves the U.S. military by examining photographic images on a real-time basis to spot suspicious objects that might be roadside bombs. San Francisco-based Climate Corp. gathers years of data about temperature and rainfall across the country to run weather simulations and help farmers decide what to plant and when. Better risk management and improved crop yields are the result. Other applications border on the humorous. Garth Sundem and John Tierney devised a model to shed light on what they described, tongue rmly in cheek, as one of the world s great unsolved mysteries: How long will a celebrity marriage last? By gathering all sorts of facts and feeding them into a computer, they came up with the Sundem/Tierney Uni ed Celebrity Theory. With only a handful of variables, the model did a very good job of predicting the fate of celebrity marriages over the next few years. Models have shown remarkable power in elds that are usually considered the domain of experts. Two political scientists, Andrew Martin and Kevin Quinn, developed a model to explain recent Supreme Court decisions --- whether the nine justices would uphold or overturn a lower court ruling --- based on just six variables. To see whether the model could actually predict decisions, University of Pennsylvania law professor Ted Ruger applied it to the upcoming Supreme Court term. Separately, he asked a panel of 83 legal experts for their predictions about the same cases. At the end of the year, he compared the two sets of predictions and found that the model was correct 75 percent of the time, compared to 59 percent for the experts. It wasn t even close. Models can even work well for seemingly subjective tasks. Which would you think does a better job of predicting the quality of wine: a connoisseur with a discerning palate and years of experience, or a statistical model that can neither taste nor smell? Most of us would put our faith in the connoisseur, but the facts tell a different story. Using data from France s premier wine-producing region, Bordeaux, Princeton economist Orley Ashefelter devised a model that predicted the quality of a vintage based on just three variables: winter rainfall, harvest rainfall and average growing season temperature. To the surprise of many and embarrassment of a few, the model outperformed the experts --- and by a good margin. These last two examples were described by Yale law professor Ian Ayres in "Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart." Ayres explained that models do so well because they avoid common biases. Not surprisingly, he mentioned overcon dence, noting that people are "damnably overcon dent about our predictions and slow to change them in the face of new evidence." Decision models, of course, don t suffer from such biases. They weigh all data objectively and evenly. No wonder they do better than humans. So are decision models really "the new way to be smart"? Absolutely. At least for some kinds of decisions. But look back over our examples. In every case, the goal was to make a prediction about something that could not be directly in uenced. A model can estimate whether a loan will be repaid but can t change the likelihood that a given loan will be repaid on time. It won t give the borrower any greater capacity to pay or make sure he doesn t squander his money the week before payment is due. A model can predict the rainfall and days of sunshine on a given farm in central Iowa but can t change the weather. A model can estimate the quality of a wine vintage but won t make the wine any better. It can t reduce the acidity, improve the balance, or add a hint of vanilla or a note of cassis. For the sorts of situations in which our aim is to make an accurate estimate of something we cannot in uence, models can be enormously powerful. But when we can in uence, the story changes. Our task isn t to predict what will happen but to make it happen. ■ Phil Rosenzweig, a former Hewlett- Packard executive and Harvard Business School faculty member, is now a professor at IMD in Lausanne, Switzerland. Models can even work well for seemingly subjective tasks. Which would you think does a better job of predicting the quality of wine: a connoisseur with a discerning palate and years of experience, or a statistical model that can neither taste nor smell? March 15, 2014 FCW.COM 29
March 30, 2014