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Super Crunchers: Why Thinking-By-Numbers is the New Way To Be Smart [Englisch] [Taschenbuch]

Ian Ayres
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Produktbeschreibungen

Pressestimmen

"In the past, one could get by on intuition and experience. Times have changed. Today, the name of the game is data. Ian Ayres shows us how and why in this groundbreaking book Super Crunchers. Not only is it fun to read, it just may change the way you think."—Steven D. Levitt, author of Freakonomics

"Data-mining and statistical analysis have suddenly become cool.... Dissecting marketing, politics, and even sports, stuff this complex and important shouldn't be this much fun to read."—Wired

"[Ayres's] thesis is provocative: Complex statistical models could be used to market products more intelligently, craft better movies, and solve health-care problems—if only we could get past our statistics phobia."—Portfolio

"When statistics conflict with expert opinion, bet on statistics....Businesses, consumers, and governments are waking up to the power of analyzing enormous tracts of information."—Discover

"Super Crunchers shows that data-driven decisionmaking is not just revolutionizing baseball and business; it's changing the way that education policy, health care reimbursements, even tax regulations are crafted.  Super Crunching is truly reinventing government.  Politicians love to tout policy proposals, but they rarely come back and tell you which ones succeeded and which ones failed.  Data-driven policy making forces government to ask the bottom line question of 'What works.'  That's an approach we can all support."—John Podesta, President of the Center for American Progress

"A lively and yet rigorously careful account of the use of quantitative methods for analysis and decision-making.... Both social scientists and businessmen can profit from this book, while enjoying themselves in the process."—Dr. Kenneth Arrow, Nobel Prize winning economist, and Professor Emeritus at Stanford University

“Ayres’ point is that human beings put far too much faith in their intuition and would often be better off listening to the numbers.... The best stories in the book are about Ayres and other economists he knows, whether they are studying wine, the Supreme Court or jobless benefits.... Ayres himself is one of the [statistical] detectives. He has done fascinating research.”—The New York Times Book Review

"Ian Ayres [is] a law-and-economics guru."—Chronicle of Higher Education

“Lively and enjoyable.... Ayres skillfully demonstrates the importance that statistical literacy can play in our lives, especially now that technology permits it to occur on a scale never before imagined.... Edifying and entertaining."—Publishers Weekly

"Super Crunchers presents a convincing and disturbing vision of a future in which everyday decision-making is increasingly automated, and the role of human judgment restricted to providing input to formulae."—The Economist

"Insightful and delightful!" —Forbes



From the Hardcover edition.

Synopsis

When would a casino stop a gambler from playing his next hand? How could a company use statistical analysis to blackball you from the job you want? Why should you worry when customer services pay attention to your needs? Beginning with examples of the mathematician who out-predicted wine buffs in determining the best vintages, and the sports scouts who now use statistics rather than intuition to pick winners, Super Crunchers exposes the hidden patterns all around us. No businessperson, academic, student, or consumer (statistically that's everyone) should make another move without getting to grips with thinking-by-numbers -- the new way to be smart, savvy and statistically superior. -- Dieser Text bezieht sich auf eine andere Ausgabe: Taschenbuch .

Über den Autor und weitere Mitwirkende

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.


From the Hardcover edition.

Leseprobe. Abdruck erfolgt mit freundlicher Genehmigung der Rechteinhaber. Alle Rechte vorbehalten.

Chapter One


Who's Doing Your Thinking for You?


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.

eHarmony Sings a New Tune


There is a new wave of prediction that utilizes the wisdom of crowds in a way that goes beyond conscious preferences. The rise of eHarmony is the discovery of a new wisdom of crowds through Super Crunching. Unlike traditional dating services that solicit and match people based on their conscious and articulated preferences, eHarmony tries to find out what kind of person you are and then matches you with others who the data say are most compatible. eHarmony looks at a large database of information to see what types of personalities actually are happy together as couples.
Neil Clark Warren, eHarmony's founder and driving force, studied more than 5,000 married people in the late 1990s....
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