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| 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
Buy New: $7.99
You Save: $17.01 (68%)
New (44) Used (30) Collectible (2) from $5.99
Avg. Customer Rating:  73 reviews
Sales Rank: 7684
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
ASIN: 0553805401
Publication Date: August 28, 2007
Availability: Usually ships in 1-2 business days
Condition: New. In stock now.
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| Customer Reviews:
 Not impressed September 19, 2007
35 out of 48 found this review helpful
I picked up this book after seeing it in Wired magazine. Being a computer programmer specializing in databases, I figured this was a great book to read.
Right from the introduction I had issues. The first 2 words of the intro are "Orley Ashenfelter", who we learn loves wine and has done statistics on wine. Right away, he's called Orley, then Ashenfelter, then Orley again, then Ashenfelter... When Robert Parker is mentioned, he's just referred to as "Parker" after that. Reading through that section of the book: Orley, Parker, Ashenfelter, Orley, Parker, Ashenfelter, it feels like he's talking about 3 people. The next section of the intro is about Bill James. After finding out his first name, curiously he's only "James" there on out. I guess "Bill" is not as exciting as "Orley"... When you disregard the common writing style of introducing a person by first and last name, then referring to them by last name only after that, it's only going to confuse the reader and remove them from just being able to enjoy the book.
The next thing I noticed is that the terms "Super Crunchers", "Super Crunching", etc are used about once a paragraph. OK. We get it. You want to coin a new word. Really, really, really badly. But while you're spending all this time bashing this word into my head, you're losing me from enjoying your book again. There's no need to have it repeated ad nauseam. It's distracting.
If I wasn't distracted enough, he spends a few pages talking about John Lott, who's also a number cruncher. There's no love lost between the two of them, and the whole section comes across as "Ha ha. I'm writing about you in my book and everyone can see it. Nanny nanny, boo boo." Maybe that's not what was intended, but that's how I came away from that section.
The last reason that made me write this review was that I wish there was more meat in the book. A lot of the time I felt I was watching a review of a movie, not watching the movie itself. (Hopefully that makes sense.) He does get into specific details once in a while, but most of the time it seems like he's talking around the topic, not about it.
 Add-On Review: No more than PRESELECTED statistical examples ... September 19, 2007
27 out of 46 found this review helpful
A comment on my review prompts me to add some more detail and explain my disagreement. I apologize up front for my abrasiveness, but fortunately I have not substituted my emotional strength with a lifeless 'super crunching' machine, despite being a software architect. As a professor at a law school, Ayres should know about what it means to have proof. Ayres does however as statisticians do: PRESELECT the input data until the outcome is as desired!
This book initially promotes with a few cleverly selected examples the idea that our education, healthcare and government and our life in general is being improved by 'experts' who use statistical analysis and prediction. But worse is yet to come when in chapter 8 Ayres writes about statistical standard deviation and how it applies it to human IQ, student grades and the stock market. I propose that IQ measurements and student grades are DEHUMANIZING and tell us nothing about how OUR CHILDREN will fare in life! When will we finally stop to discriminate humans this way?
To understand the ignorance ruling the stock markets I suggest to read Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets by Nassim Nicholas Taleb. If the 'experts' using Super Crunching are so smart Mr. Ayres, why do we have a crisis with bad real estate loans in the US?
Ayres uses numerous mostly disjointed examples that are supposed to show how powerful and accurate statistics are. He mixes statistical data mining and interactive random testing and other methods and calls it Super Crunching. None of these methods provide better decision making (a.k.a. as being SMART), rather the opposite.
The key problem is that the 'experts' who now 'Super Crunch' are not only remote and disconnected from the real world, they do not know how accurate the data they use for decisions actually is. The iterative decision-making round trip through selecting the 'right' data is therefore just used to justify the opinion of the 'super cruncher'.
Good intuitive decisions can be made in real-time by simply exposing the decision maker to a subset of that information in the real world and I am not talking about just-in-time warehousing logistics as used by the likes of Walmart. The most successful methods to improve decision making limit information gathering to the RELEVANT detail.
Humans solve this problem of decision-making on inaccurate data by use of an emotionally weighted pattern-matching ability of the brain - INTUITION. Read Gut Feelings: The Intelligence of the Unconscious by Gerd Gigerenzer. Emotions play an important part in that process. Are not strong managers and entrepreneurs mostly very emotional, even unreasonable people? Antonio Damasio - today Professor of Neuroscience at the University of Southern California - has long researched neural systems for memory, language, emotion, and decision-making. In his 1994 book, Descartes' Error: Emotion, Reason, and the Human Brain he documents his discovery that "humans with dysfunctional emotional centers face grave difficulties in decision-making."
To improve decision making with the help of computing we need to invent technology to model emotional human decision-making far beyond logical rules. While Ayres mentions Neural Networks and Pattern Matching he fails to understand that both do not need masses of data but REAL decision-points linked to real data. It is not important how many people took a certain decision, but what data pattern was used by each individual to come to the decision. The Google-Ad testing Ayres did for the book title is very basic decision point sampling but he misses to distinguish it. 50 relevant decision patterns are usually enough to learn.
Ayres asks a few critical questions but then claims that Super Crunching improves or aids our intuition. Decision making based on abstract statistical data illusions is however one of the reasons why governments and large corporations are so inept. The sizes of organizations in business and government have simply become too large to be managed well. The reason is greed for money and power and not the wished for economies of scale. Statistical data analysis is no more than a desperate try to solve that. The 'expert elite' claims they know what they are doing. Read Thomas Sowell's Knowledge and Decisions and The Vision of the Anointed: Self-Congratulation As a Basis for Social Policy to understand that it is doomed to fail.
Weak Book, No discussion of problems with the empirical work that he pushes September 23, 2007
19 out of 27 found this review helpful
Just look at the first chapter, where Ian Ayres touts his research on lojack devices, There is no discussion why almost all the insurance companies oppose giving any discount on the devices being installed. Presumably there are too few purchases of the device because if I hide a lojack on my car, even those without lojack benefit because car theives can't tell if a car is protected before they take it. Even with free-riding problems, if people got their cars back in generally one piece, why shouldn't the insurance companies want to give some discount? If there is a free-riding problem, it could be solved by car companies putting the device on all their cars. For example, if Porche put lojacks on its cars, Porche is protected without any beneficial spillover for others. Yet, no one (not Porche, BMW, Cadillac, etc.) follows this policy. Couldn't Ayres discuss these problems? Couldn't he even mention them? What about the empirical work that confirms these car or insurance companies might not be as stupid as Ayres claims that they are? If he has a response, why not even mention these problems?
He touts research reportedly showing that more abortion reduces crime, but he fails to note that if one actually did what the authors said should be done to conduct the tests, the effect went away (see "Oops-onomics." Economist Magazine, December 1, 2005). Again, why not mention these problems?
Other parts of the book also have problems. Ayres' empirical work on discrimination has also been extensively criticized, but no one would ever know from his discussion about these problems.
The book would have given readers a better feel for what empirical work entails if instead of just making accertions about findings (even when those findings have been proven to be wrong), he had spent even a little time showing how people learn from these debates over his research. A book touting the importance of empirical work would gain some credibility if Ayres acknowledged the objections raised to his and his friend's work and explained why their results still held.
The personal attacks that Ayres makes in the book are also filled with inaccuracies.
Super Disappointing January 3, 2008
17 out of 18 found this review helpful
Like "Freakonomics," this book over-relies on a catchy phrase as a substitute for a thorough exploration of the concepts and issues. The list of concerns includes:
1.Vague definition of the term "supercrunching." Is it "super" because the author wants us to think all statistics are super, or (what I had hoped) is there something about the type of statistics to which he refers that are in fact different from statistics in decision making for the last 40 years? All the talk of large datasets implies that supercrunching is a matter of size, but then why does the very first example of regression involve a model that has only 2 predictors? No need for large data sets for this kind of a model, right? Unless the effect size is tiny, but then, what good is the model? Tell us how things really are new and different now.
2.The book reads like a list of (mostly internet) companies and how fabulous and smart they are for using statistics. Actuarial science has been around for many, many years and again we see little discussion of how the actuarial tradition has become more available outside of the insurance industry. The whole book seems more like a stream of consciousness than an organized conceptual framework about the emergence of statistics as a guide to commercial, medical, and policy making over time.
3.While perhaps an excellent lawyer and professor, the author makes so many misleading or inaccurate remarks about statistics that it could be difficult for someone with a statistics background to enjoy the book. For example, regression is discussed as a technique that is different from the "randomized test," when in fact the randomized test is a design, and the regression (more commonly the "general linear model," including regression, analysis of variance, linear and structural modeling) is the inferential statistical technique used to evaluate the results of the test design. Early in the book, the author talks about how amazing regression is, and then gives and example of how a bank evaluates probability of future actions on the phone based on past behaviors on the phone. This very first example after introducing regression does not involve regression as a prediction technique, but rather actuarial base rates! In fact, I found it very disappointing that actuarial science, probability, and Bayes' theorem (all at least as relevant to data-driven decision-making as the randomized trial) were given so little attention in the book.
4.Finally, the great irony--and part of the "this book is so bad I have to finish it" quality--is that the author writes in an incredibly intuitive manner. The book is full of cognitively biased representation of the topic, owing mainly to "availability" heuristics, for example, the authors' excessive attention to the work of his friends, his roommates, his enemies, his daughter, or the companies he shops from. Better scholarship (or at least more rational) would have involved an initial sampling of all the relevant examples and final selection of the ones that would best illustrate the concepts (which I never really understood--see points 1 and 2). As other reviewers have pointed out, there is also "confirmatory bias" all over the place (presenting only the facts that fit with one's idea)--why aren't the counter arguments and counter-evidence better presented? The author must know that people buying a book on statistics will actually be smart enough to weigh the different sides of an issue. Like I said, I read to the end just to see if there was a "punch line" where the author confesses about his unapologetically intuitive approach to writing--that the book was a joke on the reader.
I would recommend this only to people who know very little about statistics and are unaware how companies like amazon.com use statistics to improve business. Such readers will be impressed. For everyone else...there are so many better books out there. Paul Meehl would be super-disappointed in this work.
Incomplete and Misleading October 16, 2007
15 out of 22 found this review helpful
In his very first example about predicting wine quality, the author touts multiple regression. He omits the following: the R square and the Standard Error of the Estimate. Consequently the reader has no way of evaluating the goodness of the forecasts. Judging by the smallness of the coefficients for the independent variables (very close to zero), the whole equation appears to be worthless.
A very poor beginning and it does not get any better. A waste of time. Fortunately for me, I read a library copy, so I did not waste any money on this very weak book.
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