Ian Ayres

Ultimately kind of vacuous. A few useful tidbits to keep you on your guard. For example, those coupons that get printed out at the register can be tailored to your spending habits and may offer you a lesser or greater discount than is offered to the next person in line. When you get a nice offer it is because somebody is pretty sure that they are making money off you.

I'm well aware that Amazon has a pretty good idea of what I like and of what I've been interested in recently.

But, after show more reading all those excellent books about bad statistics, I find that this book comes off as far too credulous. Its basic thesis seems to be that any statistical or machine learning analysis will ultimately do better than the experts. That opinion is much better explained by the unfortunate existence of fake expertise in many areas rather than anything else. The author does back-pedal a bit toward the end of the book, pointing out that mistakes can be made with numbers and data, and that there is room for smart people to intuit relationships that may exist, but it is too little, too late.

Ultimately, this book is so mathematics free as to seem more like a confidence trick than an exposition.

Some particular examples have been discussed in other books with a much different message. One of Joel Best's books claims to show that most of these deaths in hospital due to mistakes by staff result in a person dying about six hours earlier than they would if the mistake hadn't been made. Viewed in that light these mistakes seem less significant, and maybe in some cases a mercy. show less

Last year the law professor and Balkinization blogger Ian Ayres published a popular book on statistical reasoning that focused mainly on regressions. There's necessarily a tension between being popular and being about statistics, and so I should say up front that about a third of the book is not going to be comprehensible if you don't have a handle on what a probability distribution like a bell curve is all about. But even in that case, I think it might be worth a read just to get swept up show more in the excitement about the future and get in on the expert-bashing.

And expert-bashing is a big and convincing part of the book. Wine, baseball, and medical professionals resist the idea that their intuition and discretion are in many cases outmoded. Ayres gently relates some of their lame and self-serving arguments for intuitive expertise, and that's fun to hear. In other cases the experts are simply inept at using the new techniques, and I think many readers will be scandalized by the statistical illiteracy and nearly superstitious traditionalism of some physicians. Readers of Robin Hanson's or Bryan Caplan's blogs will have heard this before, but it bears repeating since it's literally a life-or-death matter.

The book is especially impressive for the large number of applications and implications Ayres discusses. I think it would have been made better by two additions, however. First, it's apparent that Ayres is economically literate, and so I think he should have anticipated and rebutted the readers' natural fears about jobs being lost. He suggests that humans will still have a role to play in the future of the revolutionized professions but only as intuitive guessers of variables to program the computers with. That sort of half-way answer stokes rather than extinguishes the make-work bias that most people will bring to the book.

The second addition I'd make to the book is an explicit consideration of the moral assumptions that lie behind some of the medical and legal equations. Ayres briefly discusses how a doctor cannot just do the math to advise a pregnant mother to have or skip an intrusive test for Down Syndrome when the test also increases the risk of miscarriage. To do that he would have to know how much she fears a miscarriage relative to rearing a mentally disabled child. As crude as it may be to consider, the simple fact is that the doctor cannot advise, and his probabilistic equations will tell him nothing, until the patient gives some indication that she fears one outcome three times, for example, more than the other. (And of course the equation is not calculable if you think the child's (unknowable) preferences ought to figure in.)

Ayres does even worse in his discussions of gun control, capital punishment, and violent criminal recidivism. At one point he says that a panel's decision to release a violent offender against the advice of an probability equation of recidivism was an "error." Only In a narrow, technical sense was it an "error," and to leave it at that is to sweep many moral judgments under the rug: does his freedom count for anything? is it the role of the law to minimize violence before it is committed? how many false positives are too many? etc, etc.

Similarly his discussion of capital punishment's effects on the murder rate equates lives lost to state-sanctioned killing and acts of murder: It's contentious to suggest these things are even commensurable, let alone comparable at a one-to-one ratio. The economist Deirde McCloskey has fussed about these hidden moral judgments at length, so I am surprised Ayres couldn't manage to find room for even one sentence on the matter.

For all its moral-blindness, it's still an entertaining and informative book. Highly recommended. show less

Through extremely entertaining anecdotes and stories, Ayres provides a wonderful glimpse at some of the ways in which statistics and big data improve our ability to make intelligent decisions. Ayres tends to stay far away from the actual math or technical aspects and simply focuses on the concepts, making it a great read for anyone unfamiliar with probability, statistics, machine learning, and similar. For someone with some experience behind this, it was a little galling to hear, for show more example, linear regression described as an advanced machine learning technique that not only provided the answers, but provided the probability that the answer was correct (Heh. I wish life were that simple!). However, I was quickly caught up in some of the wonderful real-world examples that Ayres deftly narrates. Some of the stories are absolutely inspiring, such as the way that Mexico used randomized trials to determine the best way of helping people achieve more and rise out of the welfare system, to the entertaining, such as the case in which fine wine aficionados had their noses put out of joint by a computer which could beat them at their own game. An overall brilliant book and a great read for anyone who wants to get a glimpse at the potential of big data. show less

Ironically, I'm a bit of a super cruncher at work. For years, I had come at peace that my company can aggregate all the numbers they want, but I am impossible to market to. I am beyond a number. I can not be categorized. Or am I?

Can that spark of human inspiration make better decisions more than an evolving formula? I'm leaning toward the idea that numbers can better make at least as decision as humans when it comes to clear rule sets (like chess) and large amounts of data. Seriously, show more people, can you really understand what $100 billion is and how to appropriate it "fairly"?

Perhaps the more interesting battle would be humans vs. computer playing poker (which computers lost).

When it comes to meaningful questions about .. the meaning of life or does he/she love me? How should I raise my child? Or why do bad things happen to good people? The formula falls short.

Still, a very good read. show less