Who's Doing Your Thinking for You?

Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart
by Ian Ayres

Why would a casino try and stop you from losing? How can a mathematical formula find your future spouse? Would you know if a statistical analysis blackballed you from a job you wanted?

Today, number crunching affects your life in ways you might never imagine. In this lively and groundbreaking new book, economist Ian Ayres shows how today's best and brightest organizations are analyzing massive databases at lightening speed to provide greater insights into human behavior. They are the Super Crunchers. From internet sites like Google and Amazon that know your tastes better than you do, to a physician's diagnosis and your child's education, to boardrooms and government agencies, this new breed of decision makers are calling the shots. And they are delivering staggeringly accurate results. How can a football coach evaluate a player without ever seeing him play? Want to know whether the price of an airline ticket will go up or down before you buy? How can a formula outpredict wine experts in determining the best vintages? Super crunchers have the answers. In this brave new world of equation versus expertise, Ayres shows us the benefits and risks, who loses and who wins, and how super crunching can be used to help, not manipulate us.

Gone are the days of solely relying on intuition to make decisions. No businessperson, consumer, or student who wants to stay ahead of the curve should make another keystroke without reading Super Crunchers.

Chapter 1

Recommendations make life a lot easier. Want to know what movie to rent? The traditional way was to ask a friend or to see whether reviewers gave it a thumbs-up.

Nowadays people are looking for Internet guidance drawn from the behavior of the masses. Some of these "preference engines" are simple lists of what's most popular. The New York Times lists the "most emailed articles." iTunes lists the top downloaded songs. Del.icio.us lists the most popular Internet bookmarks. These simple filters often let surfers zero in on the greatest hits.

Some recommendation software goes a step further and tries to tell you what people like you enjoyed. Amazon.com tells you that people who bought The Da Vinci Code also bought Holy Blood, Holy Grail. Netflix gives you recommendations that are contingent on the movies that you yourself have recommended in the past. This is truly "collaborative filtering," because your ratings of movies help Netflix make better recommendations to others and their ratings help Netflix make better recommendations to you. The Internet is a perfect vehicle for this service because it's really cheap for an Internet retailer to keep track of customer behavior and to automatically aggregate, analyze, and display this information for subsequent customers.

Of course, these algorithms aren't perfect. A bachelor buying a one-time gift for a baby could, for example, trigger the program into recommending more baby products in the future. Wal-Mart had to apologize when people who searched for Martin Luther King: I Have a Dream were told they might also appreciate a Planet of the Apes DVD collection. Amazon.com similarly offended some customers who searched for "abortion" and were asked "Did you mean adoption?" The adoption question was generated automatically simply because many past customers who searched for abortion had also searched for adoption.

Still, on net, collaborative filters have been a huge boon for both consumers and retailers. At Netflix, nearly two-thirds of the rented films are recommended by the site. And recommended films are rated half a star higher (on Netflix's five-star ranking system) than films that people rent outside the recommendation system.

While lists of most-emailed articles and best-sellers tend to concentrate usage, the great thing about the more personally tailored recommendations is that they diversify usage. Netflix can recommend different movies to different people. As a result, more than 90 percent of the titles in its 50,000-movie catalog are rented at least monthly. Collaborative filters let sellers access what Chris Anderson calls the "long tail" of the preference distribution. The Netflix recommendations let its customers put themselves in rarefied market niches that used to be hard to find.

The same thing is happening with music. At Pandora.com, users can type in a song or an artist that they like and almost instantaneously the website starts streaming song after song in the same genre. Do you like Cyndi Lauper and Smash Mouth? Voila, Pandora creates a Lauper/Smash Mouth radio station just for you that plays these artists plus others that sound like them. As each song is playing, you have the option of teaching the software more about what you like by clicking "I really like this song" or "Don't play this type of song again."

It's amazing how well this site works for both me and my kids. It not only plays music that each of us enjoys, but it also finds music that we like by groups we've never heard of. For example, because I told Pandora that I like Bruce Springsteen, it created a radio station that started playing the Boss and other well-known artists, but after a few songs it had me grooving to "Now" by Keaton Simons (and because of on-hand quick links, it's easy to buy the song or album on iTunes or Amazon). This is the long tail in action because there's no way a nerd like me would have come across this guy on my own. A similar preference system lets Rhapsody.com play more than 90 percent of its catalog of a million songs every month.

MSNBC.com has recently added its own "recommended stories" feature. It uses a cookie to keep track of the sixteen articles you've most recently read and uses automated text analysis to predict what new stories you'll want to read. It's surprising how accurate a sixteen-story history can be in kickstarting your morning reading. It's also a bit embarrassing: in my case American Idol articles are automatically recommended.

Still, Chicago law professor Cass Sunstein worries that there's a social cost to exploiting the long tail. The more successful these personalized filters are, the more we as a citizenry are deprived of a common experience. Nicholas Negroponte, MIT professor and guru of media technology, sees in these "personalized news" features the emergence of the "Daily Me"-news publications that expose citizens only to information that fits with their narrowly preconceived preferences. Of course, self-filtering of the news has been with us for a long time. Vice President Cheney only watches Fox News. Ralph Nader reads Mother Jones. The difference is that now technology is creating listener censorship that is diabolically more powerful. Websites like Excite.com and Zatso.net started to allow users to produce "the newspaper of me" and "a personalized newscast." The goal is to create a place "where you decide what's the news." Google News allows you to personalize your newsgroups. Email alerts and RSS feeds allow you now to select "This Is the News I Want." If we want, we can now be relieved of the hassle of even glancing at those pesky news articles about social issues that we'd rather ignore.

All of these collaborative filters are examples of what James Surowiecki called "The Wisdom of Crowds." In some contexts, collective predictions are more accurate than the best estimate that any member of the group could achieve. For example, imagine that you offer a $100 prize to a college class for the student with the best estimate of the number of pennies in a jar. The wisdom of the group can be found simply by calculating their average estimate. It's been shown repeatedly that this average estimate is very likely to be closer to the truth than any of the individual estimates. Some people guess too high, and others too low-but collectively the high and low estimates tend to cancel out. Groups can often make better predictions than individuals.

On the TV show Who Wants to Be a Millionaire, "asking the audience" produces the right answer more than 90 percent of the time (while phoning an individual friend produces the right answer less than two-thirds of the time). Collaborative filtering is a kind of tailored audience polling. People who are like you can make pretty accurate guesses about what types of music or movies you'll like. Preference databases are powerful ways to improve personal decision making.

Copyright © 2007 by Ian Ayres

About the Author

Ian Ayres ,an econometrician and lawyer, is the William K. Townsend Professor at Yale Law School, and a professor at Yale's School of Management. He is a regular commentator on public radio's Marketplace and a columnist for Forbes magazine. He is currently the editor of the Journal of Law, Economics and Organization, and has written eight books and more than a hundred articles.