Stephen Baker of BusinessWeek has just published a book entitled The Numerati, and has a blog related to the book. The purpose of the book is to look how mathematicians are using data to to profile people in their shopping, voting, and even dating habits.
I am not exactly an unbiased reader of the book. I talked with Stephen during the writing of the book, and he asked me to review the two pages he wrote about “operations research” (I made a couple suggestions which didn’t make it into the final version: I guess this is my “cutting room floor” experience). He was kind enough to send me a review copy of the book, which I received a few weeks ago. He also accepted my invitation to speak here at CMU to the Tepper School Faculty and doctoral students.
The book is divided into chapters corresponding to the different uses of data: “Worker”, “Shopper”, “Voter”, “Terrorist”, “Patient” and “Lover”. For instance, in the “Voter” section, the emphasis is on predicting voter behavior. In the past (perhaps), geography and economics were very good predictors of voting behavior. Now, people seem much more in flux as to their behavior. Perhaps there are better predictors. Or perhaps there are useful clusterings of like-minded people that would respond to a particular pitch. If Barack Obama were to identify a cluster of “people who blog about obscure but important mathematical modeling methods” and would send a mailer (or email more likely) showing his deep understanding of operations research and a promise to use that phrase in his acceptance speech, then perhaps he would gain a crucial set of voters. Barack, are you listening?
I greatly enjoyed reading the book, and did so in one sitting. For someone like me who perhaps could be seen as one of the Numerati, there is not much technical depth to the book, but there are a number of good examples that could be used in the classroom or in conversation. There is a bit too much “The Numerati know much about you and can use it for good or EEEVVVIILLLL” for my taste, but perhaps I take comfort in understanding how poorly data mining and similar methods work in predicting individual behavior. The book is very much about modeling people, so essentially ignores the way operations research is used to automate business decisions and processes. This is a book primarily about what I would call data mining and clustering, so there are wide swathes of the “numerati” field that are not covered. But for a popular look on how our mathematics is used to characterize and predict human behavior, The Numerati is an extremely interesting book.
5 thoughts on “The Numerati”
I’m not sure I agree with the concept that my behavior can be predicted with math. I consider myself a pretty unique individual, and don’t think any of my behaviors can be explained by anything, much less mathematics.
At any rate, it sounds like a great book and seems interesting enough, I might have to pick it up sometime.
I would highly recommend reading Baker’s book immediately before or after reading How to Measure Anything: Finding the Value of “Intangibles” in Business by Douglas Hubbard. Baker would probably consider Hubbard one of the “numerati”. Both authors talk about some of the specifics of the analysis methods (but moreso Hubbard) and both talk about the general trends and impacts (but moreso Baker).
Like his table of contents (which is simply worker, shopper, voter, blogger, terrorist, patient, lover), Baker’s book is sweeping if a bit terse in places. As a quant, I find Numerati an easy read with virtually no math but still enlightening even for the most quantitatively adept reader. There were several examples in Baker’s book where I already knew of the mathod but had not heard of that application. He did some great research and covered a lot of topics in this giant and elaborate field of work.
My main concern for many management-level readers of this book is that in some cases Baker gives a reader just enough information to think they can apply it to a similar problem they have, falling into the “a little knowledge is a dangerous thing” trap. Again, this can be offset with a read of Hubbard’s book. It might also have been helpful to talk about the rise of “crackpot rigour” in a world with lots of data and relatively few competent mathematical analysts (various “data mining” experts come to mind).
In all, its one of my favorite reads of the year. I felt like someone was finally casting light on my own obscure field.