Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart | 
 enlarge | Author: Ian Ayres
Publisher: Bantam
Category: Book
List Price: $25.00
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Rating:  70 reviews
Sales Rank: 10134
Media: Hardcover
Edition: 1
Number Of Items: 1
Pages: 272
Shipping Weight (lbs): 1
Dimensions (in): 9.1 x 6.1 x 1.1
ISBN: 0553805401
Dewey Decimal Number: 519.5
EAN: 9780553805406
Publication Date: August 28, 2007
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Condition: May have small remainder mark on bottom. 100% money back guarantee. All books shipped from Strand Bookstore, New York City, USA.
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Product Description
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.
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| Customer Reviews: Read 65 more reviews...
 Outstanding Information and Very Interesting! August 30, 2007
65 out of 77 found this review helpful
"Super Crunchers" provides a very readable summary of what can be done to improve performance using the incredible volumes of data accumulated in business, government, health care, and education. Why now? One reason is that the massive amounts of data now available make randomization (essential to valid conclusions) much more achievable than in the past; the other is the low and continually falling costs of computers and storage media.
The bulk of Ayres' work consists of examples (names both companies and the software involved) within each of the sectors previously mentioned. Capital One has been running randomized tests since at least 1995 - tests include page layout, and type and size of offers. Google uses data analysis to fuel its web accelerator (uses your past browsing history to predict pages to be called up next), Wal-Mart's analysis of responses to various employment questions is used to rank potential employees, and Continental Airlines follows up on its own data to design follow-up programs for complaining fliers. Capital One's approach has also been used to evaluate various charity donation-matching programs, and could also be used to evaluate potential billboard and magazine ads. (Similarly, TiVo is now being used to evaluate various TV ads, using the same approach and measuring the relative frequency with which various ads are fast-forwarded through.)
"Offermatica" software not only automates randomization (format, type of offer) for a number of firms, it also analyzes the responses in real time, dramatically cutting the cost of experiments. Thus, no more waiting for hyper-controlled experiments in universities and laboratories that conclude, ALL OTHER THINGS BEING EQUAL (that never happens), eg. red is preferred to blue.
Randomized tests are also increasingly being used to evaluate various government programs, finding eg. that additional job location assistance more than paid for itself for those receiving unemployment benefits, guiding HeadStart programs to target those most likely to benefit.
"Super Crunchers'" health care examples were the most impressive. Don Berwick's "100,000 lives" campaign saved 122,342 lives in an 18 month period through persuading about 3,000 hospitals representing 75% of all beds to focus on six areas of improvement identified through data analyses. These included antiseptic placement of central line catheters in ICUs, elevating heads and washing the mouths of those on respirators, adoption of the latest heart attack treatments, and rapid response teams to patent beds.
Bottom Line: "Super Crunchers" is an exciting vision of what is already possible!
petty Amazon reviewer vendetta on a well-reviewed book November 25, 2007
3 out of 9 found this review helpful
The majority of the 30-something positive reviewers, many of whom are clearly insightful and judicious, have been spammed with "not useful" feedbacks. Such misuse of the Amazon review process undermines its primary purpose - objective reviews with unbiased comments.
Some perspective - The Economist cites SuperCrunchers as one of the 10 best books of the year. Every professional review I've found says this book is replete with interesting developments in business and economics. Google it yourself. So take accusations of lack of new material as a reflection of petty reviewers rather than the book.
My review:
Super-crunchers explores the way sophisticated decision-makers are increasingly running the world.
Decision makers now collect information, find the important factors, and try improvements. It sounds simple, but requires a paradigm shift to add a slew of new computerized and database-based tools to supplement our intuition.
The book surveys dozens of business decisions, policy decisions, and sports issues, and shows how we can understand to forces at work better. The changes have taken dedaces, and are great. In the cases for which I know the details, the book is on the money. I work at data-mining for science, and found the technical parts all old hat, but the many case studies fascinating.
Ian has a remarkable track record of uncovering facts we should have known and need to fix, with efficient real-life experiments, and again here his savvy yet lucid exposition reveals the goings-on necessary to see the state of the union.
Super book for those who are curious March 17, 2008
0 out of 2 found this review helpful
The author has a two Bachelors (Russian Studies and Economics) from Yale, PhD (Economics) from MIT, JD from Yale. He is a bonifide genius and he sprinkles this book with much of his intellectual horsepower.
He provides multiple historical and empirical examples of how humans are very bad at making decisions. Human are even worse in determining the quality of their decisions (most people tends to exaggerate the correctness of their answers). Statistics, on the other hand, are mathetically sound way of predicting future results. Better yet, it provides a highly reliable way of determining the quality of the prediction. Generally speaking, the better the data, the more predictive statistics can become.
Recently, however, there has been a great convergence of statistics and technology. More specifically, informational technology which allows number crunching of multiple terabytes of data in a relatively short period of time has become available at a practical price.
The author believes whoever can take advantage of this relatively new phenomenon has a great competitive and informational edge. Having reliable data analysis tools that use statistical regression analysis enables an entitity to make better decisions (and also know the probability of making a bad decision).
This has become a game changing convergence. Indeed, it is changing the world and will become even more important in the future.
What should you do to be better prepared?
Learn statistics and get the best data mining/analysis tools and employees your company can buy. As the author states, we are seeing only the beginning of this mostly positive trend.
Superexcellent September 8, 2007
6 out of 13 found this review helpful
This short book, which can be read in a few hours, could be considered an apology or even a manifesto for mathematical and statistical modeling. Even those readers, such as this reviewer, who have been involved in "supercrunching" for many years will find some interesting anecdotes in this book that illustrate its power and limitations. But even more importantly, it discusses the reactions of many (and typically highly insecure) individuals against the practice of mathematical modeling, some of these bordering on the absurd but with most content with ridiculing its practice. There is no question that the "supercrunching" that the author describes will continue to have greater influence in the manner in which it is currently practiced, but it is also true that much more sophisticated approaches to modeling will arise, some of these using machine intelligence and highly advanced mathematics. Indeed, in the past decade the use of artificial intelligence has exploded in areas such as finance, network engineering, bioinformatics, and Internet security. The author has just scratched the surface of the vast number of tasks that are now being done by machine, sometimes without any intervention or supervision by humans.
As the author details in the book, many businesses and public institutions have jumped on the bandwagon of supercrunching, and their successes in doing so he documents well, with references given for readers who want more of the details. But many businesses that could profit greatly from this approach have refrained from its use, because of skepticism or distrust of quantitative reasoning. To paraphrase the author, they want to stay with the horse-and-buggy, while others are getting around in locomotives. There is still a great reliance on "experts" whose track record is weak and when compared with statistical modeling falls very short. It is an open question whether these businesses will find themselves in bankruptcy court because of their rejection of statistical modeling, but they are going to have to face stiff competition from the businesses that do.
For those involved in the supercrunching that the author describes, it is not surprising to hear that many important business decisions are being made based on the results of statistical modeling. Humans are to a large degree still "in the loop", but the author describes instances where the machines "are actually in charge." The author still wants both human and machine to be in a mutual symbiosis, with considerably more weight given to machine predictions. And along these same lines, he brings up the canonical question as to what place humans are to have as more responsibility for decision-making is given to the machines. Many may find their social and employment status shrink, becoming white elephants (or "potted plants" to use the author's terminology) in the process. The author tries to alleviate these anxieties by pointing to the need for humans to still do the groundwork that enables supercrunching to take place. Humans must still "hypothesize" he asserts, in that they must still make the decisions as to what variables are going to be used when the machines actually perform the statistical analysis.
Certainly if one remains within the statistical modeling paradigm, as the author does throughout the book, there will still be need for humans to "hypothesize." But if one chooses to go beyond this paradigm, the landscape changes considerably. The author gives a brief glimpse into how this is done in his discussion on neural networks. But he leaves out any discussion of the research in automated scientific and mathematical discovery that has taken place in the last two decades, some of this research showing remarkable progress. If this trend continues, and there is every reason to think that it will, then the machines will be able to hypothesize, theorize, and analyze in a manner that is similar to humans but may be vastly superior. In addition to these developments, significant progress has been made in artificial intelligence that allow machines to use defeasible, abductive, and inductive reasoning patterns in order to operate in domains unheard of just a few years ago. These domains include automated legal reasoning, computational creativity, rumor detection and propagation, virus recognition in data networks, musical composition, and automated mortgage underwriting. With these and their supercrunching abilities, they will certainly instill both admiration and fear, and using them will require extreme confidence to a degree that goes far beyond what the author describes in this book. It is disquieting to some that the machines will have this degree of intelligence and autonomy, but to others it is a source of pure exhilaration, and proof again that this is the best time ever to be alive.
Inside empirical data-crunching March 3, 2008
0 out of 1 found this review helpful
"There are three kinds of lies," said Benjamin Disraeli, "lies, damned lies and statistics." But, like it or not, the world is becoming more quantitative every day and no one can afford to be statistically innumerate. If you live in Excel and use quantitative techniques daily, this may come as no surprise. What may be surprising, even to data-heads, is the extent to which statistical methods are illuminating areas of human life hitherto relegated to "experts." Call it the new age of empiricism or the rise of numerical "super crunchers," but, whatever the name, the trend is real. In this book, Yale law professor and econometrician Ian Ayres provides an unbiased sample of entertaining anecdotes showing how quantitative thinkers are taking over and why the trend is unlikely to abate. The caveat: as the world and its feedback loops get increasingly complex, is regression less useful? If so, Ayers is a bit optimistic. Yet, getAbstract finds that his book, as well as being entertaining and vigorously written, offers a painless review of important statistical ideas that even Disraeli would've found hard to challenge.
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