Super Crunchers: Why Thinking-By-Numbers is the New Way To Be Smart

Super Crunchers: Why Thinking-By-Numbers is the New Way To Be Smart

Super Crunchers: Why Thinking-By-Numbers is the New Way To Be Smart

An international sensation—and still the talk of the relevant blogosphere—this Wall Street Journal and New York Times business bestseller examines the “power” in numbers. Today more than ever, number crunching affects your life in ways you might not even imagine. Intuition and experience are no longer enough to make the grade. In order to succeed—even survive—in our data-based world, you need to become statistically literate.

Cutting-edge organizations are already crunching increasingly larger databases to find the unseen connections among seemingly unconnected things to predict human behavior with staggeringly accurate results. From Internet sites like Google and Amazon that use filters to keep track of your tastes and your purchasing history, to insurance companies and government agencies that every day make decisions affecting your life, the brave new world of the super crunchers is happening right now. No one who wants to stay ahead of the curve should make another keystroke without reading Ian Ayres’s engrossing and enlightening book.

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3 comments

  1. 284 of 318 people found the following review helpful
    2.0 out of 5 stars
    CRUNCHING on Empty, CRUNCHING Blind (Apologies to Jackson Browne), November 11, 2007
    By 
    Steve Koss (New York, NY United States) –
    (VINE VOICE)
      
    (REAL NAME)
      

    Is it a new brand of cereal? Or maybe it’s a granola bar, or a chunky peanut butter spread? Then again, could it be the latest infomercial exercise device designed to give you the six pack abs you’ve always dreamed of but know in your heart of hearts you’ll never achieve? Actually, it’s a book – the title a product of the very methods the book describes. Here’s what SUPER CRUNCHERS says.

    (1) Mathematical regression models generated from large datasets often generate better predictions than human experts, and they provide supporting information on the predictive weight and reliability of each explanatory variable.
    (2) Well-crafted experiments using randomized trials and control groups provide good market research and behavioral analysis results.
    (3) Technological advances – the Internet, massive data storage devices, rapid computation, broadband telecommunication – are making it possible to share more sources of information and create ever-larger databases for analysis.
    (4) Today’s companies engage in multiple forms of market research by creating and using large databases and large-scale randomized trials.
    (5) Many phenomena conform to normal distributions in which 95% of the population will be found within two standard deviations of the mean, the5% balance generally divided evenly in the two tails.

    That’s it. I just saved you $25.00 U.S. and a half-dozen or more hours learning how a guy from Yale named Ian Ayres collected a bit of information about applied mathematical techniques that have been in practical use for decades, packaged them up, palmed them off as something new, and cooked up the ridiculous name Super Crunching to describe an ostensibly new technological development. Yet “Super Crunching” is nothing more than the author’s marketing hype for a couple of standard mathematical methodologies, a creation of nothing from something. There’s no new breakthrough here, no new paradigm.

    Yes, the anecdotal information about the future prices of wine vintages, Capital One’s teaser offerings, and evidence-based medical diagnosis are interesting (hence the two stars rating). The rest, however, is neither prescriptive nor sufficiently critically analytical. Should we go out shopping for a Super Cruncher tomorrow? Should we delight in the increased accuracy of data-driven modeling and prediction, or should we fear the implied manipulation of our desires and the incessant, single-minded drive toward maximum profit at the expense of creativity? Do we really want movies and books to be developed from mathematical models like Epagogix? Do we really want our every keystroke on the Internet to be fodder for market research that manipulates us in response? John Kenneth Galbraith, among others, warned of exogenous, manufactured demand decades ago.

    SUPER CRUNCHERS is part business tome, part econometric paean, and part sociology book, but not fully any of the three. No matter how many time the author uses words like “cool” and “humongous” and “amazing,” it’s still regrettably a “No Sale” even for someone like me who enjoys reading about applied mathematics.

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  2. 242 of 295 people found the following review helpful
    1.0 out of 5 stars
    Disappointing, October 6, 2007
    I read a blurb on this book in the Economist and bought it for that reason. When I read it however, it failed to deliver. It is similar to the Tom Peter’s “Search of Excellence” type book with anecdotal stories with little substance. It is overgeneralized and overhypes the models it discusses. The models Ayres discusses are also NOT NEW. I personnally have been creating these types of system for nearly 30 years. What has changed over the years, of course, is greater accessibility of data and a greater capacity to process that data economically. But we still struggle with quality of data issues and appropriateness of model issues — especially when the models begin to be used by people other than the model creators. The book glosses over this, only providing an example of how Choicepoint used a poor matching algorithm when eliminating felons from Florida’s voting roles and even then the author minimizes the problem.

    There is no discussion of how these models become abused when implemented as tools where the user of the tool has no knowledge of its limitations, when the model provides suboptimal solutions or what “outliers” are and how to deal with them (although you know immediately when you ARE the outlier and are trapped dealing with a company using a model designed for a population you don’t belong to).

    This leads us to becoming a nation of people who read off a screen and do what the computer says to do, while turning off our brain. Any wonder you can get outsourced in that scenario? But it must be right — we Super Crunched it!

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  3. 52 of 63 people found the following review helpful
    1.0 out of 5 stars
    Super Disappointing, January 3, 2008
    By 
    The Professor “Bruce” (LosAngeles, CA) –

    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.

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