Little Children and The Abstinence Teacher are two complex, sympathetic works. These are the only two Perrotta books I have read, but it is clear to me that he is a generous author, who is able to detail the complex thought chains lying below each of his characters’ surfaces. This generosity turns symbols into living, breathing people, enabling them to transcend simple, thematic opposition and actually interact with one another. The key point is that he does not treat the opposition as punching bags.

Little Children is the lesser work of the two, if only because the plot seems so apparently stilted next to the personalities of the characters. The inclusion of a child-molester in this story seems to serve no purpose other than to enable some opportunities for Brad to get out of the house (as part of a neighborhood watch group) and to provide some tension near the end of novel. It is too clumsy, given that Perrotta’s skill is so evident in his descriptions of the molester, inspiring both repulsion and pity.

There is one misstep in characterization that occurs on the first page, when the women are introduced – except for our protagonist Sarah – as the mother of so-and-so child. It isn’t symbolism: it is a neon sign that states Sarah is the contrarian of the bunch, a lapsed feminist who longs to be defined by anything other than motherhood. For the most part, the other women, who serve more as the Harpies than a Greek chorus, are not fleshed out. There is one little vignette where the shrew’s (Mary Ann’s) unhappy home life is laid bare, but for the rest of the story they serve to remind Sarah of the destiny awaiting her. No conversation is more meaningful than where the offspring is going to preschool, what toys are being recalled, what TV shows one had watched through heavy-lidded eyes.

That alone would drive one to drink, but Sarah chooses adultery instead. She was and is a mousy girl, who wanted to but couldn’t date the popular jock in high-school or college; she achieves this juvenile ambition by eventually sleep with Brad, a househusband who should be studying for his third attempt at passing the bar exam. The affair has great power within the context of the trapped lives both Sarah and Brad feel they lead. The excitement isn’t so much in the illicit nature of sneaking behind their spouses but rather in the fact that they share a common appreciation of one another. Therein lies the trick in Perrotta humanizing the two; certainly, I felt badly for Richard and Kathy, the spurned spouses. But I felt more sadness than anger in Sarah and Brad finding their escape in each other.

The humanization comes because one can identify with the cause of the affair: the perception that one’s spouse doesn’t fully appreciate him as a partner. It is not a matter of reality; it is that one spouse feels put upon and felt the need to seek that appreciation elsewhere. Brad is the simple case: he is going through his mid-life crisis early. He has failed the bar exam twice, but he states he entered law school on a whim. He watches teenage boys skateboarding and longs to join; instead, he winds up with a bunch of cops and ex-cops in a football league. He is satisfied being a house husband, but of course his wife is expecting him to contribute financially. Her moral support of his attempting the bar exam has crossed from wishing him well into an expectation that he will fail and not pull his financial weight. Sarah’s case is just as simple: her husband isn’t interested in her. She wants to be significant. She is intelligent, but decides that the only way to distinguish herself from the pack of mothers is to flirt with Brad. The two hit it off.

It would have been cheap for Perrotta to distance the reader from Richard and Kathy. Instead, Perrotta turns them into people, each with flaws. Kathy is a harried woman, one reaching the limit of her patience with her husband. Fairly or not, she feels too put upon. She works and so doesn’t spend enough time with her son. Although she is following her dream of directing documentaries, it doesn’t pay well. She has been understanding and a cheerleader for her husband – despite his repeated failure. She is tired. Richard is more difficult to describe; he appreciated Sarah’s intelligence when they first met and now provides financial stability for their family. But in the end, he too is tired and desires something less ordinary.

That is what I like about Perrotta’s writing. Sure, he slings barbs at suburban life, but his characters are people like you or me. Under any number of circumstances, we could be Sarah, Richard, Kathy or Brad. Perrotta’s characters in an understandable manner, despite our disapproval. Recently, I had read Pinker’s The Blank Slate, which helped crystallized some ideas about human emotional and cultural baggage for me. Perrotta’s characters strike me as real because he describes the dissonance between basic desires driving action (i.e. nature) and professed desires (the sum of education, environment, and upbringing) so well.

One scene that illustrates this is when Brad notices that his son flat out ignores him as soon as Mom (Kathy) comes home. That scene bundles the flash of Brad’s jealousy of the bond between son and mother, the fact that the boy and mother essentially enter their own world and exclude him, and the fact that he might be feeling both unmanly (for being a house husband) and his efforts not being recognized by his son or appreciated by his wife. Everything about this scene rings of authenticity. Again, without declaring whether there is validity in the perception (although one will be either sympathetic to Brad or not), the sum of all these minor events build up the case that Perrotta is interested in explaining (and thus looking past one’s view of the adulterers), but not excusing , Brad’s and Sarah’s behaviors.

I would guess the moral of the story is that communication only goes so far. Perhaps that is what love means: that a partner thinks enough of the other person to continue talking. If so, then Perrotta must think the world a loveless place.

Conscientious Objections: Stirring Up Trouble About Language, Technology and Education
Conscientious Objections: Stirring Up Trouble About Language, Technology and Education by Neil Postman
My rating: 4 of 5 stars

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Joe Posnanski has written another thoughtful piece on the divide between writers of a statistical bent and those who prefer the evidence of their eyes.  I highly recommend it; Posnanski distills the arguments into one about stories. Do statistics ruin them? His answer is no. Obviously, one should use statistics to tell other stories, if not necessarily better ones. He approached this by examining how one statistic, “Win Probability Added”, helped him look at certain games with fresh eyes.

My only comment here is that, I’ve noticed on his and other sites (such as Dave Berri’s Wages of Wins Journal) that one difficulty in getting non-statisticians to look at numbers is that they tend to desire certainty. What they usually get from statisticians, economists, and scientists are reams of ambiguity. The problem comes not when someone is able to label Michael Jordan as the greatest player of all time*; the problem comes when one is left trying to place merely great players against each other.

* Interestingly enough, it turns out the post I linked to was one where Prof. Dave Berri was defending himself against a misperception. It seems writers such as Matthew Yglesias and King Kaufman had mistook Prof. Berri’s argument using his Wins Produced and WP48 statistics, thinking  that Prof. Berri wrote other players were “more productive” than Jordan. To which Prof. Berri replied, “Did not”, but also gave some nuanced approaches in how one might look at statistics. In summary, Prof. Berri focused on the difference in performance of Jordan above that of his contemporary peers. 

The article I linked to about Michael Jordan shows that, when one compares numbers directly, care should be taken to place them into context. For example, Prof. Berri writes that, in the book Wages of Wins, he devoted a chapter to “The Jordan Legend.” at one point, though, he writes that

 in 1995-96 … Jordan produced nearly 25 wins. This lofty total was eclipsed by David Robinson, a center for the San Antonio Spurs who produced 28 victories.

When we examine how many standard deviations each player is above the average at his position, we have evidence that Jordan had the better season. Robinson’s WP48 of 0.449 was 2.6 standard deviations above the average center. Jordan posted a WP48 of 0.386, but given that shooting guards have a relatively small variation in performance, MJ was actually 3.2 standard deviations better than the average player at his position. When we take into account the realities of NBA production, Jordan’s performance at guard is all the more incredible.

If one simply looked at the numbers, it does seem like a conclusive argument that Robinson, having produced more “wins” than Jordan, should be the better player. The nuance comes when Prof. Berri places that into context. Centers, working closer to the basket, ought to have more, high-percentage shooting opportunities, rebounds, and blocks. His metric of choice, WP48, takes these into consideration. When one then looks at how well Robinson performed above his proper comparison group (i.e. other centers), we see that Robinson’s exceptional performance is something one should expect when comparing against other positions but is not beyond the pale when compared to other centers. However, Jordan’s performance, when compared to other guards, shows him to be in a league of his own.

That argument was accomplished by taking absolute numbers (generated for all NBA players, for all positions) and placing them into context (comparing to a specific set of averages, such as by position.)

This is where logic, math, and intuition can get you. I don’t think most people would have trouble understanding how Prof. Berri constructed his arguments. He tells you where his numbers came from, why there might be issues and going against “conventional wisdom”, and in this case, the way he structured his analysis resolved this difference (it isn’t always the case he’ll confirm conventional wisdom – see his discussions on Kobe Bryant.)

However, I would like to focus on the fact that Prof. Berri’s difficulties came when his statistics generated larger numbers for players not named Michael Jordan. (I will refer people to a recent post listing a top-50 of NBA players on Wages of Win Journal.*)

* May increase blood pressure.

In most people’s minds, that clearly leads to a contradiction: how can this guy, with smaller numbers, be better than the other guy? Another way of putting this is: differences in numbers always matter, and they matter in the way “intuition” tells us.

In this context, it is understandable why people give such significance to 0.300 over 0.298. One is larger than the other, and it’s a round number to boot. Over 500 at-bats, the difference between a 300-hitter and a .298-hitter  translates to 1 hit. For most people who work with numbers, such a difference is non-existent. However, if one were to perform “rare-event” screening, such as for cells in the blood stream that were marked with a probe that “lights” up for cancer cells, then a difference of 1 or 2 might matter. In this case, the context is that, over a million cells, one might expect to see, by chance, 5 or so false-positives in a person without cancer. However, in a person with cancer, that number may jump to 8 or 10.

For another example: try Bill Simmons’s ranking of the top 100 basketball players in his book, The Book of Basketball. Frankly, a lot of the descriptions, justifications, arguments, and yes, statistics that Simmons cites looks similar. However, my point here is that, in his mind, Simmons’s ranking scheme matters.  The 11th best player of all time lost something by not being in the top-10, but you are still better off than the 12th best player. Again, as someone who works with numbers, I think it might make a bit more sense to just class players into cohorts. The interpretation here is that, at some level, any group of 5 (or even 10)  players ranked near one another are practically interchangeable in terms of their practicing their craft. The differences between two teams of such players is only good for people forced to make predictions, like sportswriters and bettors. With that said, if one is playing GM, it is absolutely a valid criterion to put a team of these best players together based on some aesthetic consideration. It’s just as valid to simply go down a list and pick the top-5 players as ordered by some statistic.* If two people pick their teams in a similar fashion, then it is likely a crap shoot as to which will be the better team in any one-off series. Over time (like an 82-game season), such differences may become magnified. Even then, the win difference between the two team may be 2 or 3.

* Although some statistics are better at accounting for variance than others.

How this leads back to Posnanski is as follows. In a lot of cases, he does not just simply rank numbers; partly, he’s a writer and story teller. The numbers are not the point; the numbers illustrate. Visually, there isn’t always a glaring difference between them, especially when one looks at the top performances.

Most often, the tie-breaker comes down to the story, or, rather, what Posnanski wishes to demonstrate. He’ll find other reasons to value them. In the Posnanski post I mentioned, I don’t think the piece would make a good story, even if it highlighted his argument well, had it ended differently.

The Billion Dollar Molecule: One Company's Quest for the Perfect Drug
The Billion Dollar Molecule: One Company’s Quest for the Perfect Drug by Barry Werth
My rating: 3 of 5 stars

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This is exciting; Lev Grossman’s sequel to The Magicians, called The Magician King, will be out tomorrow. There is a small write-up at The Brooklyn Paper. I was looking for news about the novel when I came across an old New Yorker interview, which has a few words about the sequel. I had also written some thoughts about The Magicians.  I guess my essay seemed down on the novel, but I really liked it.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

13 Bankers: The Wall Street Takeover and the Next Financial Meltdown
13 Bankers: The Wall Street Takeover and the Next Financial Meltdown by Simon Johnson
My rating: 3 of 5 stars

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The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It
The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It by Scott Patterson
My rating: 3 of 5 stars

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In a previous post, I wrote about financial models. The point is that a scientific model generally simplifies. At the same time simplification gives models their power, one must also take care to assess whether adapting or transplanting the model to new fields is valid. Hence some disconnects between economic models and the financial tools based off these models.

Here’s another illustration. I was talking with my friend about his thesis. R. is interested in building a model of the olfactory bulb. This structure is interesting; it is well defined anatomically into three layers. The top layer contains neuropil structures called glomeruli. Glomeruli contain the axon projections from the primary sensory neurons and dendritic branches of the neurons in the bulb. Both these “main” neurons and so-called interneurons form  connections within this layer. Since this is where raw signals from the nose arrive, it is called the input layer. Together, these cells form a network and reshapes the responses into new neural activity patterns, relayed to deeper olfactory processing areas of the brain.

The middle layer contains the cell bodies of the olfactory bulb output neurons. As mentioned, these cells, called mitral or tufted cells (usually termed M/T cells), send a main dendrite to the glomerulus. Each cell also sends secondary dendrites laterally, within the middle layer. The third layer, the granule cell layer, contains interneurons that form connections between the laterally spread dendrites in the middle layer. This forms a second point within the olfactory bulb where the raw input from the nose can be reshaped, repatterned, and repackaged for subsequent processing.

OK: my friend spoke of his troubles. He needed to convert the sensory neuron activity (from the nose), which differ for different smells. The features that are important seem to be when the activity begins (onset latency), how long it lasts for (duration), and how intense (basically how often the neuron “fires” an action potential.) There are some other subtleties, naturally. Each smell evokes activities in a great many olfactory neurons, some of which respond with a different set of characteristics. The idea is to build the model so that the responses from bulb output neurons can be calculated, given the set of parameters (i.e. the input activity patterns).  Ultimately, these input neural patterns can be related to the actual behavior that helped shape them (such as the sniffing that an animal might engage in as they hone in on some odorous.)

His trouble came with integrating the Hodgkin-Huxley model of the action potential (this is basically derived from physical/thermodynami first principles), determining how this model would generate action potential “spikes” in a way that mimics what the olfactory bulb neurons would do, given the pattern of input activity and the 2 layers of interneuronal influence within the bulb. It seemed like a set of nested differential equations – that is, the action potentials varied over time, with the degree of influence from the various interneurons also changing in time. That’s a real cluster-eff.

I thought I had a brilliant idea (and I still think it’s nice.) I suggested that he can simply build a phase space to describe all the possible arrangements of his input patterns. Each point in this abstract descriptive space can be correlated to a set of output profiles (i.e. how the bulb neurons eventually respond.) He can, in the end, identify the bulb response most likely to result from a given set of input patterns.

The problem is that this is a descriptive model. The Hodgkin-Huxley model would have the advantage of being an actual, theoretical model. Once this is in place, they can literally predict, down to the number of spikes and when they fire, the output of the olfactory bulb.

So yes, that, in a nutshell, is the difference between data-mining versus something derived from first principles. While one might be able to infer the same conclusions from a descriptive model, the theoretical model might be easier to work with when extending it slightly further than what had been observed by scientists. As Justin Fox warns, such extensions can be perilous if one does not take care to worry about validity.