Tuesday, November 7, 2017

Everybody Lies by Seth Stephens-Davidowitz

Hardcover, 338 pgs, Pub May 9th 2017 by Dey Street Books, ISBN13: 9780062390851

Maybe everyone does lie. But they don’t lie all the time. Stephens-Davidowitz makes the good point that asking people directly doesn’t always, in fact may not often, yield true answers. People have their own reasons for answering pollsters untruthfully, but it is clear that this is a documented fact. People sometimes lie to pollsters.

Stephens-Davidowitz was told by mentors and advisors not to consider Google searches worthwhile data, but the more he looked at it, the more he was convinced that Google searches contained the best data for determining what people are concerned about. He has uncovered some interesting trends that are not apparent through direct questioning because people are sometimes ashamed of their fears, feelings, prejudices, and predilections.

This book was better read rather than listened to, though the production by HarperAudio and the narrator, Tim Andres Pabon, were excellent. Stephens-Davidowitz gives charts, graphs, data points that obviously cannot be represented in the audio version. These usually help me to grasp things easily and maybe bypass pages of material that is not as interesting to me. It wasn’t that his material was hard, it was that I oftentimes did not like what he was talking about. He had a tendency to focus on deviant behavior, e.g., sexual predators, abuse, porn, etc. One might make the argument that these behaviors are important to understand and therefore worth looking at. Possibly. However, if ‘everybody lies,’ one might make the argument that we do not have to look at deviance to find untruthfulness.

What we discover is that to test Stephens-Davidowitz’s thesis that ‘everybody lies,’ we have to spend quite a lot of time with statistics and creating studies, which is fine. Stephens-Davidowitz argues that 'big data' is the source of the insights, not the insights themselves. This is kind of important and may overlooked. The true point he makes about lying is that big data probably irons out discrepancies in the reasons for our Google searches, e.g., that it is not me that is interested in the herpes virus, it is my brother, because in the end it doesn’t matter why we did the search; what matters is that we did the search. Besides, maybe I’m lying about my brother having the virus, but my interest in the topic is not a lie.

Stephens-Davidowitz has made a career so far out of the study of big data, showing us ways to slice and dice it so that it is useful to our view of the world. Only thing is, I am not as interested in what big data tells us as he is. He’d trained as an economist, and towards the end of the book he hit a couple of areas I did find more interesting, like the notion of regression discontinuity, a term used to describe a statistical tool created to measure the outcomes of people very close to some arbitrary cut-off.** S-D talks about using this tool on federal inmates, discovering criminals treated more harshly committed more crimes upon their release. But S-D also studied students on either side of the admissions cut-off for the prestigious Stuyvesant High School: those who attended Stuyvesant did not have a significant performance difference in later life than students who did not.

Apparently Stephens-Davidowitz went into data science because of Freakonomics, the bestselling book by Steven D. Levitt. He believes that many of the next generation of scientists in every field will be data scientists. I did finish the audiobook, another study he took note of in the last pages. Apparently few readers finish ‘treatises’ by economists. He believes this is his big contribution to our knowledge base, and there is no doubt his contrariness did highlight ways big data can be used effectively.

If I may be so bold, I might be able to suggest a reason why many female readers may not be as interested in the material presented, or in Stephens-Davidowitz himself (he was/is apparently looking for a girlfriend). Stay away from the deviant sex stuff, Seth. It may interest you but I can guarantee that fewer women are going to find that appealing or reassuring conversation material.

An interesting corollary to this economists’ data view is the question of whether the truth matters, which is how I came to pick up this book. Recently on PBS’ The Third Rail with Ozy, Carlos Watson asked whether the truth matters. At first blush the answer seems obvious, and two sides debated this question. One side said of course truth matters…but most of us know one man’s truth to be another man’s lie. The other side said ‘everybody lies.’ It got me to thinking…I do think the two ways of coming to the notion of lying dovetail at some point, and one has to conclude that truth may not matter as much as we think. What matters is what we believe to be true.

Finally, it appears Stephens-Davidson agrees to some degree with Cathy O'Neill, author of Weapons of Math Destruction, in that he agrees you best not let algorithms run without human tweaking and interference. The best outcomes are delivered when humans apply their particular observations and knowledge and expertise along with big data.

** S-D describes it this way:
“Any time there is precise number that divides people into two different groups, a discontinuity, economists can compare, or regress, the outcomes of people very very close to the cut off.”

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