Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart |  | Author: Ian Ayres
Publisher: Bantam
List Price: $25.00
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Rating:  88 reviews
Sales Rank: 170050
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Media: Hardcover
Edition: 1
Pages: 272
Number Of Items: 1
Shipping Weight (lbs): 1
Dimensions (in): 9.1 x 6 x 1.1
Dewey Decimal Number: 519.5
ASIN: B0027IQB60
Publication Date: August 28, 2007
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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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Showing reviews 1-5 of 88
 Good stories November 17, 2009
Jessie Kuo (Houston, TX)
From wine tasting to baseball player scouting, statistics is getting more and more prevalent. It is applied to not only business performance but government policies. I found some of the examples very intriguing, such as the Progresa program which tested the concept of giving cash to women who can keep their children in school. The idea of the program is to break the poverty cycle if children in poor villeges can become more educated. The researchers carefully designed the test program, randomized the samples (which is the key topic the author was trying to elaborate). The result was so convincing that the concept has been widely adopted by many countries now. Another example given in the book is the evidence-based medicine. I noticed some of the reviews said it's not a good example and an inappropriate chapter...,etc. I personally find this chapter interesting and a good example to prove that how statistics and probability should be applied in medical field. I am a CPA and I am seeing statistics and data mining got applied to more and more areas day by day. It's actually making my job easier and more efficient (and may be evntually in danger...) However, I think it's also important to keep in mind that no matter how advanced technology is and how logical it is to rely on statistics, you still need a human mind to catch mistakes or a design error....and not to mention human creativity no machine can compete. Sometimes, inituition is not a bad thing.
My husband is a PhD in Education and he absolutely hates the story about Direct Instruction and the idea of having teachers teach classes based on scripts just because the statistical evidence says that it improves children's learning. He even suspects the dataset was wrong in the first place. I, personally, trust the numbers. But, I don't want my kids to read like a robot either. I would prefer more interaction with teachers in classrooms because the value a teacher can add in a classroom setting is more than just reading and writing. There's social skill, interpersonal skill and so on a child needs to learn. And that's just not something statistics can measure.
I think this book is a good entry level reading for those who are not aware of the data revolution that is happening every day now. For those who is working on data day in and day out, it's also a good read to know what else can it be applied to. For those experts, this book is probably too elementary.
Eye Opener September 4, 2009
Devon Selae (Trinidad)
This book is for the new economist and strategic thinkers. It stimulates the mind and reiterates the importance of statistics in the decision making process.
 This book should have been written, but not quite like this. August 31, 2009
Chris Edwards (San Deigo, CA USA)
To be sure, the mixture of statistics with the increasingly large data sets of the Internet Age have created a new reality in the way humans plan and think. That is definitely important and a whole book on that topic isn't unreasonable. This book however, despite being readable, fell short in some ways.
I personally, found the title and its constant use to be kind of annoying. "Super Crunch" sounds like a sugary breakfast cereal I would not want to eat. I was therefore surprised to learn that the title had been statistically tested against better (in my opinion) alternatives. To me, making up a deliberately catchy name for the book's topic distracts from how interesting the topic really is in the same way that popular depictions of computer experts in movies detracts from how interesting computer science can be. But hey, what do I know?
The chapters were 24 pages on average (standard deviation of 5.8) which sometimes felt kind of long. Maybe the ideal chapter length could be explored with statistical analysis next time.
Several times I was a little annoyed with the conclusions and inferences the author made. Perhaps the worst was the discussion of how advanced statistical analysis with large data sets (no, *I* won't be using the verb "to Super Crunch") could cause racism. This was, in my opinion, completely sensationalized. The author basically concluded that racist practices at service businesses would move from a personalized affair to one where the computer model was the offender. This implies that the computer models at banks and insurance companies would be set up to specifically make life difficult for certain racial groups. This is just not credible. Rather than many closet racist CEOs looking for ways to safely persecute certain racial groups, it is far more likely that companies are thinking of discriminating against the same group their shareholders have always wanted them to treat badly, people from whom the company can not extract the maximum amount of money. I believe that statistical techniques could create large racial discrepancies to be sure, but that is not racism nor even necessarily a bad thing. It might just objectively indicate a problem. Consider a hypothetical algorithm that looked at video camera images from a cop car and advised the cop how to treat the person based on how they were driving, type of car, etc. If it turns out that cops are advised to be extra alert for people who turn out to be from a certain racial group, that is interesting, but much less likely to be racist than if the cop just has a hunch. That's really the whole point of the book and I'm surprised he missed this.
The last chapter seems a hodgepodge of page filler. He half-heartedly tells us about Bayes Theorem and some arbitrary rule of thumb for linking standard deviation to confidence level in percent. Even the final paragraph shows a bit of a lack of imagination with respect to the topic at hand. He says, "I doubt that we will see quantitative studies on the best way to... peel a banana." The whole point of the book is that you just might. Sure enough, type "peel a banana" into Youtube's search and you'll get a definite oversupply of videos, all with a quantitative study called ratings.
 Not worth your time August 25, 2009
Ninakix (Portola Valley, CA United States)
Ayres book is downright, pretty terrible. While some of the stories he tells are mildly interesting (but most of it is likely to be stuff the well-read person has already at least heard of before), by and large, he spends the entire the book repeating his argument. But even his argument shows a misunderstanding and lack of finesse of the situation. Though he tries to admit that there are situations when humans are better suited, or the idea that algorithms rely on humans, even these seem half-hearted attempt to show some level of depth in his thinking. All in all, this book is not worth your time.
Obvious thesis August 21, 2009
D. Chow (Swarthmore, PA USA)
I am a mathematics major at Swarthmore College, and, thinking of going into statistics in graduate school and in my professional career, and impressed by Professor Stephen Levitt's praise, I took it home with me.
Ayres's thesis is that in many situations today, human intuition should be subordinated, or at least in part, to statistical predictions. Complex statistical models can be used to quite literally calculate the best way to teach children to read, for example, or which books will appear in your recommended list on Amazon. One example Ayres uses involves predicting the decisions of the Supreme Court justices in various court hearings. People's intuition proved less accurate than did a simple algorithm. The point is, human intuition is often unreliable.
But, isn't that obvious? It is no suprise, to me at least, that many times it is better, when faced with a problem, to do what has been proven to work, and not necessarily that which makes intuitive sense. One example that I can readily come up with is deciding which restaurant to go to. People no longer so easily pick a restaurant based on outward appearance. Now it is more common to look for reviews on-line from people who have dined at that particular restaurant. And, has it not struck many, that statistics is used to find books "you might be interested in" on websites like Amazon or Barnes & Noble? Or that statistics and not human intuition is used to predict the weather?
Ayres concludes that statistics should not be the sole determinant of finding solutions to problems and that advanced statistics should be complemented with human intuition. But that is not a surprise either. Data cannot predict with certainty -- anyone who has a basic understanding of statistics knows that.
Really, Ayres's book did not prove to be as impressive as it seemed. His argument and examples are at once apparent and unoriginal.
Showing reviews 1-5 of 88
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