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Reviews elsewhere on the web:
New York Times
Craig Newmark
Eugene Asahara
Brian Ivanovick
Motley Fool
Steve Miller

Ian Ayres

Super Crunchers

We all know that computers are becoming ubiquitous in our society. But you may not realise that they are doing more than just assisting current decision processes. In Super Crunchers Ian Ayres shows how much they are bringing about a huge change in the way decisions are made. The book starts with an example of how the future value of wine may be predicted using a simple formula based on the nature of the growing season - and how this is often beating the expert opinion. Now that so much data is available, the number crunching approach is taking hold in many important areas of our life, such as medicine and government as well as most large businesses.

Naturally the experts resist their place being usurped by a machine, but Ayres shows that often the supposed expert intuition is just no match for data crunching. I felt however, that Ayres could have given more consideration to the other problems of a number crunching approach. For instance, he shows how simple metrics might be used to predict which books will be bestsellers, but even before the use of computers many in the publishing industry deplored the tendency towards a few large firms fighting over a few bestsellers. And Ayres is too dismissive of the idea that as people get to know the algorithms they will try to 'cheat the system' - something against which Google have to fight a continuous battle. But although the book is lacking in such analysis, it's certainly worth reading in order to be prepared for the ever increasing involvement of such data crunching in our lives.

Amazon.com info
Hardcover 272 pages  
ISBN: 0553805401
Salesrank: 8785
Weight:0.95 lbs
Published: 2007 Bantam
Amazon price $16.50
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Amazon.co.uk info
Hardcover 272 pages  
ISBN: 0719564638
Salesrank: 96725
Weight:1.19 lbs
Published: 2007 John Murray
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Amazon.ca info
Hardcover 272 pages  
ISBN: 0553805401
Salesrank: 35504
Weight:0.95 lbs
Published: 2007 Bantam
Amazon price CDN$ 19.96
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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.
 
Entertaining, but far from super ***

This is an easy and mostly entertaining read. The author uses many anecdotes to
persuade us that statistics can be a useful tool for decision making. Some of
the described applications use lots of data and multiple regression. Those are
easier to do now than they used to be, because more data is collected and kept.
Some are trivial. If your company hurts a customer, apologize. You might get
some ideas of thing to do that might help your organization. You will not get
any detailed help about how to implement the improvement, but there is a good
chance there is enough information that some systems person can figure out what
other skills are needed to make the idea work.

There is some discussion of limitations on the methods, and some warnings about
potential abuse, but not enough. Ayres seems to confuse correlation with causation.
He also frequently assumes the sample is representative of the population.
Even when trying to make the sample representative, it often is not. He also
assumes the answer is in the data. Sometimes it is not. Ayres reports a study
concluding widespread point shaving in college basketball because a distribution
at game end did not match the distribution five minutes earlier when a highly
favored team was ahead by about the spread. I have no opinion about the conclusion,
but the simpler explanation of the coach thinking it was late enough to safely
let the weaker players participate more was not considered.

Regression is a powerful tool, but it is easy to misuse. For an ongoing
survey of misuses, see junkfoodscience dot com, a blog. Many of the entries show
the flaws in statistical claims of medical trials. Also try stats dot org.
 
What you can do with large datasets ***
The answer is of course: a lot.
And Ian Ayres' book will tell you a little about it.

Supercrunchers are those who use lage datasets
to find patterns in human behaviour, and
predict the future based on these large datasets.

The book informs us that super crunching is on the verge of being
used all over. E.g.
Chess grandmaster Kasparov was no match
for IBMs Deep Blue chess computer,
that stored some 700.000 grandmaster chess games to help find the
winning move.
The IRS could use its data to tell a small business,
if it is spending too much or too little on advertising.
Indeed, the IRS probably has enough data to
make good estimates on whether business, marriages, etc. etc.
will fail - based only on comparison with its existing dataset.

For the paranoid, it is a horror that supermarkets could map your life cycle and predict your next purchases pretty accurately (based on
what other similar customers did).
For the optimist data mining is a good thing and we'll all lead better lives because of it.

Want to write a bestseller about it? Compare your title and some key words with data from a database of books, titlescore.com, containing millions of bestsellers and flops, and you will get your answer.

It all seems pretty straight forward, and the book has some nice examples of what we can expect in the coming years.

-Simon
 
Weak Book, not original material *
This is new? The notion that empirical research is useful has been dealt with in book after book. The book not only recycles stories word for word without quote marks from the New York Times and other publications. There are hundreds of books that show that empirical work can help understand the world. What is new? What is interesting that is new here?
 
comme ci, comme ça **
It comes on the heals of some really great non-fiction analytical books. Unfortunately, this book is all anecdotal and lacks real substance. It is good for non-mathematical, non-analytical people, but not good for people with solid educations in math, statistics, and data analysis.
 
Freakonomics 2: enjoyable survey of interesting research with real-world impacts ****
Ayres demonstrates how statistical analysis of large datasets is affecting the way the world works in a broad range of applications: credit card companies, sports teams, wine critics, development economists, medical practitioners,* law enforcement agencies, schools, etc. "Freakonomics didn't talk much about the extent to which quantitative analysis is impacting real-world decisions. In contrast, this book is about just that - the impact of number crunching" (p13).

As an economist, some of the work is familiar (for example, the research Ayres and Steve Levitt did on the value of the vehicle-recovery device LoJack or the Poverty Action Lab), but Ayres gives a good introduction for the uninitiated. And he covers such a broad range of applications that I learned a great deal.

Like other research surveys (Freakonomics, The Tipping Point, Blink, Stumbling on Happiness), I view these books mostly as surveys of interesting research. Each has a central thesis (Ayres' is that traditional intuition and expertise will be - or already has been - replaced by computing power and will have to learn to complement that power rather than compete with it) which may or may not be convincing, but the books tend to be good rides because so much of the surveyed research is interesting. (For example, I'll be studying more about Direct Instruction - a scripted way of teaching reading that may be useful in my own work - based on this book; and the model Ayres expounds of how private firms learn from iterative experimental trials may apply well to some of the agencies I engage.)

As far as Ayres' thesis goes, I find him relatively convincing (computers with lots of data do predict many things better than people**) but despite his many caveats, the tone should probably have been more humble. He doesn't - for example - explore the issues brought by Taleb in The Black Swan: The Impact of the Highly Improbable, how traditional statistics may be worse than useless in financial markets where a single, completely unpredictable bad shock can wipe out years of carefully predicted investments.

This book was lots of fun to listen to, not least (unintentionally) because Ayres loves giving irrelevant but amusing descriptions of his researchers. The examples below are all economists:

"Ashenfelter is a tall man with a bushy mane of white hair and a booming, friendly voice... No milquetoast he" (p2).

"Even now, in his forties, Larry [Katz] still looks more like a wiry teenage than a chaired Harvard professor (which he actually is)" (p65).

"Esther [Duflo] has endless energy. A wiry mountain climber..." (p73).

And of course you know this is the Freakonomics family because of the Levitt-love scattered here and there: "There is a new breed of innovative Super Crunchers - people like Steve Levitt - who toggle between their intuitions and number crunching to see farther than either intuitivists or gearheads ever could before" (p17).

I listened the unabridged audiobook narrated by Michael Kramer (not Michael Kremer - quoted in this book on p74), published by Books on Tape (6 CDs). Kramer does a good job except when he tries an Australian or British accent.

* For an excellently written description of evidence-based medicine and more, read Atul Gawande's Better: A Surgeon's Notes on Performance.

** One of the most striking findings comes from the meta-analysis (1996) of two psychologists, Meehl & Grove, who look at 136 studies comparing human judgment to equation-based judgment. In only 8 of the 136 studies was expert prediction found to be appreciably more accurate than statistical prediction." Overall, experts got the predictions right 66% of the time whereas Super Crunchers got them right 73% of the time. And the 8 in which experts did better weren't concentrated in any particular field. From looking at the paper myself, I found that 64 of the studies favored the Super Crunchers whereas 64 found the two methods roughly equal. Noteworthy. [In the book, p111 and p232.]
 
Good concept, poor execution **
The concept was reasonable: to show how statistical analysis was changing the way in which the businesses and governments operated. The author also set out the relevant topics sensibly; chapters covered such issues as why this is occurring now, random samples, the reaction of experts to such analysis (with an extra focus on the medical profession) and the legal/ moral issues that this type of analysis raises. The trouble with this book is that the writing style is too simplistic. It's not obvious whether this is the fault of the editor or the author or a combination of the two. Various examples of data analysis are used without it being clear what point the author is trying to make. The result is that at the end of many chapters it's unclear what the author's intent was. There are two other aspects of the writing which are irritating. First, every person whose work is described in the book is given a brief biography which reads as if they were some form of matinee idol. Secondly, the author uses the opportunity to air some personal professional disputes which for anyone outside the argument is tedious. Overall, it's a book you may buy at an airport to read on a flight and throw away
 
overconfident, but worth it ***
What this book does well is to show how valuable numerical information can be if you know how to analyse it properly, and how unreliable human judgment can be compared to what the data can tell us. The examples are well chosen and telling. Where it goes too far is in expressing almost unqualified belief that the number stuff trumps everything else, all the time. Contrary to the title, not everything can be predicted. Specifically, the sudden turns of events, in markets or social behaviour (which are often what we are most interested in) are not predictable on the basis of past patterns. By defintion, surprises are not predictable. And there always has to be a human doing the modelling, working out the dynamics of any set of causal relationships. Sometimes, these people get it wrong. Then it's the old case of "rubbish in, rubbish out." But though he has pushed the argument too far, he is interesting, imaginative and persuasive that the balance could be tipped a little further than it now is, towards making better use of statistical data.
 
Anecdotal Assembly *
Tiresome. One lengthy anecdote after the other -- albeit, some of interest. No conceptual depth. Surprising that such work can come from an associate of Yale University. However, just as the author "supercrunched" the choice of the title, he may have used the same approach in order to determine what may best sell to a mass of undiscerning readers.
 
Well done description of the paradigm shift from historical analysis to real-time, prospective number crunching ****
Very timely book, with some very good examples how "super chrunching" is already affecting our lives. Even those familiar with the topic may not realize all the new ways statistical number chrunching are being applied. A thoughtul discussion of some of the key issues.

I agree with the author's argument that statistical thinking is of increasing importance for all of us. Although the later portion of the book is probably the weakest, as more practical examples of how to hone your skills would have been helpful. For example, moving backwards and forwards to derive a probability of interest from given data is an important concept - but so is making sure you haven't invalidated key assumptions along the way (e.g. normality?). If so, you risk drawing severly erroneous conclusions, which is the antithesis of proper statistical thinking.

A worthwhile read.
 
A Well-Written Eye-Opener *****
The information presented in this book is truly astounding. But when one thinks about it, computers have become so fast and powerful that the manipulation of gigantic databases should come as no surprise - yet a few years ago, this was the subject of science fiction. But what does this mean for the average person? The author of this book very ably explains this by giving many examples of what can now be done with these huge databases. His main thesis is that the statistical analysis of a large amount of data can be used to make predictions that are more accurate than those of human experts who base their opinions on experience and intuition. But in addition, accurate predictions can be made about the behavior, e.g., shopping habits, of particular individuals, once some basic data specific to these individuals is entered into a computer. In other words, these computers can "know" more about us than we know about ourselves! I find this absolutely fascinating. The book is written in a style that is very clear and friendly yet authoritative. It should be of great interest to absolutely everyone.
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