I write this post in a state of mild panic. New York Public Library has made a margin call on When Genius Failed: The Rise and Fall of Long-Term Capital Management, and I still have a few pages left in the epilogue. Holding on to this asset is costing me, I’m losing equity, etc., etc.
Having now rushed to finish the book over the past few days, I can’t recommend it highly enough.
The story, in short, is this: Long-Term Capital Management was a hedge fund created by “geniuses.” These included two university mathematicians who could make legitimate claims to being key creators of contemporary mathematical finance. (They won Nobel Prizes for this while working for the fund.) It included key disciples of these mathematicians. There were PhDs and Ivy League degrees up the wazoo.
They quickly made a bajillion dollars. They made this money by developing mathematical models that could help them identify pairs of items that were mispriced, relative to each other. They then bet that those prices would converge. They bet this over and over gain in many, many different situations. They always won.
Then, they didn’t. They very quickly lost it all and had to be bailed out by the NY Fed and a consortium of banks.
This was happening in the ’90s. The people involved with LTCM were (if I understand correctly) early pioneers of using quantitative models to capitalize on the mathematical theories of modern finance. I know that this previous sentence is pretty vague, but my point is that this is a story about mathematics and mathematicians.
While reading about these mathematicians, I recognized an arrogance that I’ve experienced in mathematical cultures waaaay too often:
Hilibrand finally offered to dispatch Scholes [Nobel Prize winner — MP] to give the bank a lesson on option pricing, but Pflug was too smart to go head-to-head with the guy who had invented the formula. “You can overintellectualize these Greek letters,” Pflug reflected, referring to the alphas, betas, and gammas in the option trader’s argot. “One Greek word that ought to be in there is hubris.”
What hubris did Pflug divine? The partners were not arrogant in their mannerisms or even in their speech; it was more deep-seated. It was the arrogance of people who had been to Harvard and MIT — of people who really believed that they were more intelligent than others. “Do you know why we make so much money?” Greg Hawkins once asked an old friend from Salomon. “It’s because we’re smarter.” Once the Hawk even tried to lecture a colleague’s wife about molecular biology, her longtime specialty. “You’re full of shit,” she finally replied.
In particular, it seems to me this is partly the arrogance of believing that math is truly about everything. I see this in books that claim math gives you the tools for being logical, in general, or in somehow not being wrong — again, in general.
I also see it in the belief that math can give students general-purpose reasoning skills, ready to be deployed willy-nilly in any context, whatsoever.
It’s something that comes up in the belief that models can be usefully deployed on any dataset, which is one way that LCTM dug its own grave:
Characteristically, Meriwether encouraged the firm to explore new territory. Even at Salomon, the troops had always sought to extend their turf…In retrospect, such moves had been baby steps, not bold new departures. But the partners’ experience — to them, at least — seemed to belie the adage that it is dangerous to try to transport success to unfamiliar ground. Trusting their models, they simply rebooted their computers in virgin terrain.
This is an attitude I even saw in something like Cathy O’Neill’s Weapons of Math Destruction. I don’t have the book in front of me (so apologies if I get this a bit wrong) but there are various moments when she suggests that the same algorithms that are currently deployed oppressively could simply be turned towards more socially useful causes.
I don’t think that, on reflection, O’Neill would deny that deep knowledge of particular context matters, but a reader of her book could be forgiven for thinking that it’s just a matter of where you aim your weapon.
(Hannah Fry’s Hello World does a better job thinking aloud about how deep knowledge of context is necessary for making mathematical models work. So, for example, medical experts might use a trained AI to help identify likely patches of cancerous cells. But the human is the crucial bit here; AI is the tool.)
I’m rambling now, so let’s wrap this up. When Genius Failed: I recommend it. And in general I recommend looking at finance as a major site in the contemporary mathematical landscape. If we want to understand what math is in the world these days, we have to look at finance.
7 thoughts on “Mathematicians Behaving Badly”
Funny that you started with the phrase Margin Call, speaking of mathematicians. Have you ever seen that movie? It’s quite funny, in that the mathematicians are the low level dweebs, and the higher up you go the less you know about math. My kids love this movie.
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I would very much like to watch this movie. Thank you!
You’ll enjoy it. It’s on either Amazon or Netflix right now. Quite short–just 90 minutes. After you read it, check out Noah Millman’s review.
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It was on Amazon, and it’s very good. So well acted.
Though I’d say the “Wall Street people don’t care about anything but money” take seems a bit too easy.
Just noticed you’d seen it.
I didn’t get the impression they cared about anything but money. The Jeremy Irons decision was only about money in the sense that money = survival. In fact, the guy who seems most wrongheaded is Kevin Spacey’s character, who is worried more about customers than the business.
But I love the “So you’re a rocket scientist.” “I was, yeah.” And all the guys doing math in their heads, like when Will is tallying up how he spends the 2.5 million–” Yeah, well I did spend 76,520 dollars on hookers, booze and dancers. But mainly hookers.” It’s also great from a history perspective, when Jeremy Irons goes through all the crashes throughout history.
Or the famous line: “Speak to me as if I were a small child. Or a golden retriever.”
By the way, this is by far the best explanation I’ve seen of the dynamics of the movie and why it works so well: http://theamericanscene.com/2011/11/09/no-margin-for-error
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What a terrific review. Here’s my favorite part:
“You’re not there to serve the clients. You’re there to serve the shareholders.”
This was the only thing he said that I thought worth quibbling over, if only because the notion that these firms had shareholders was a relatively new thing. But then, I realize, in the old world this almost never would have happened.
When I show this movie to my kids (pre-calc only), I tell them to be on the lookout for the only woman in the movie. It’s easy to argue, given what happened, that this shows how sexist the world is. I tell them (before we start) to pretend the woman was a man, and ask them why it happened.
They all realize, with that prompt, that she got fired because, of her and Cohen, she was the one obsessed with finding blame and covering her ass, while Cohen was at “sell” less than 5 minutes after he learned of the risk.