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Super CrunchersWe 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
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Hardcover
272 pages
ISBN: 0553805401
Salesrank: 33076
Weight:1.1 lbs | | Published: 2007 Bantam | | Amazon price $16.50 | | Marketplace:New from $6.20:Used from $3.73 |
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| Buy from Amazon.com |
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| Amazon.ca info
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Hardcover
272 pages
ISBN: 0553805401
Salesrank: 90292
Weight:1.1 lbs | | Published: 2007 Bantam | | Amazon price CDN$ 20.06 | | Marketplace:New from CDN$ 8.23:Used from CDN$ 5.90 |
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| Buy from Amazon.ca |
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| Product Description
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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. |
| Engaging, easy to read book. ***** |
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| This is an easy to read book about how massive amounts of data, gathering of which has exploded with the Internet and advances in storage and computing power, is used to build computer models that are in many ways better than human experts in estimating, evaluating, matching, recommending most anything these days starting from Amazon recommendations and eHarmony matches and ending with medical treatments and government policies. Need to choose a name for a book ? Story line for a script ? Which graphics to use on your web site ? Who to hire ? There is a lot of data out there for free of for a fee that will let you run factor analysis and regressions or build a neural net (well, you may need a PhD for this) that helps you. You can also run your own randomized tests and generate data. Several companies have built their business models around such analytic models. The book does not tell you how to, but it opens your eyes to super crunching and to the extent to which data crunching affects our everyday lives and the value it provides to those who use it. |
| Good stories **** |
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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 **** |
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| 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. *** |
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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 ** |
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| 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. |
| Does not tell the full story - needs an update! ** |
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| This read must have been compelling a couple of years ago. But the 'super crunching' this book advocates - didn't do a good job in predicting the credit crunch. It probably played a role in creating it. The book makes case after case against human intuition - which may work in many instances. Though an updated version of the book - highlighting the role of statistical models in banks lending to sub-prime, credit cards being given to people who can't repay, its failure to predict the decline in home prices - this would allow us to really evaluate the strengths and potential pitfalls of so called Super Crunchers. |
| Persuasive **** |
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This book is not as poor as the previous reviews suggest. It offers a persuasive account of the potential of mass numeric data in coming to better (evidence-based) decisions. The book shows that, on many occasions, the hard data is a better guide than human intuition or even human experience. But it also, quite rightly, points out that numbers are fallible - the outcomes depend on the formulae they are squeezed through, for example.
The most annoying aspect of the text is that it is way too long. Like so many other American books of this ilk (e.g. the Tipping Point, Blink, The Paradox of Choice), it is a 'one-idea book' that would have worked just as well as a lengthy article. There is no need to stretch the idea out to fill a whole book. |
| Good concept, poor execution ** |
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| 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 *** |
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| 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 * |
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| 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. |
| Inside empirical data-crunching ***** |
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| "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. |
| Well done description of the paradigm shift from historical analysis to real-time, prospective number crunching **** |
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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 ***** |
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| 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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