When We Lose Control

Good news! A computer has solved the longstanding Erdős discrepancy problem! Trouble is, we have no idea what it’s talking about — because the solution, which is as long as all of Wikipedia’s pages combined, is far too voluminous for us puny humans to confirm.

A few years ago, the mathematician Steven Strogatz predicted that it wouldn’t be too much longer before computer-assisted solutions to math problems will be beyond human comprehension. Well, we’re pretty much there. In this case, it’s an answer produced by a computer that was hammering away at the Erdős discrepancy problem.

more here

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Are Markets Rigged?

I haven’t been paying too close attention to the Michael Lewis furor but I hear it’s all quite entertaining. Here’s a somewhat different perspective:

Information changes markets.  The reason that I mention bond trading, even though my target is stock trading, is that it was a *far* more rigged market because it was dealer-driven, and voice-to-voice.  It was far easier to lose to more skilled brokers, than trading stocks online today.

More here. The key takeaway for me is that in financial markets, ‘rigging’ is indistinguishable from ‘competitive advantage’ and is mostly a question of time and resources.

Tread carefully when incredibly sophisticated market participants accuse each other of cheating and remember the parable of the baptists and bootleggers.

updated: here’s some pro-HFT stuff.

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How do Olympic Medals Affect Longevity?


This paper investigates how status affects health by comparing mortality between Gold medalists in Olympic Track and Field and other finalists. Due to the nature of Olympic competition, analyzing performance on a single day provides a way to cut through potential endogeneity between status and health. I first document that an athlete’s longevity is affected by whether he wins or loses and then detail mechanisms driving the results. Winning on a team confers a survival advantage, with evidence that higher mortality among losers may be due to poor performance relative to one’s teammates. However, winning an individual event is associated with an earlier death. By analyzing the best performances of each athlete before the Olympics, I demonstrate that an athlete’s performance relative to his expectations partly explains the earlier death of winners in individual events: on average, Olympic Gold medalists expected to win, but losers exceeded their expectations. Conversely, athletes considered “favorites” but who fail to win die earlier than other athletes who also lost. My results are robust to estimating a range of parametric and semi-parametric survival models that make different assumptions about unobserved heterogeneity. My central estimates imply lifespan differentials of a year or more between winners and losers. The findings point to the importance of expectations, relative performance, surprise, and disappointment in affecting health, which are not highlighted by standard models of health capital, but are consistent with reference-dependent utility. I also discuss potential implications for employment contracts in terms of a trade-off between ex post health and ex ante incentives for productivity.

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Geography of Fame

WHERE do the most successful Americans come from? I was curious. So I downloaded Wikipedia…

Why do some parts of the country appear to be so much better at churning out American movers and shakers? I closely examined the top counties. It turns out that nearly all of them fit into one of two categories.

First, and this surprised me, many of these counties consisted largely of a sizable college town. Just about every time I saw a county that I had not heard of near the top of the list, like Washtenaw, Mich., I found out that it was dominated by a classic college town, in this case Ann Arbor, Mich. The counties graced by Madison, Wis.; Athens, Ga.; Columbia, Mo.; Berkeley, Calif.; Chapel Hill, N.C.; Gainesville, Fla.; Lexington, Ky.; and Ithaca, N.Y., are all in the top 3 percent.

Why is this? Some of it is probably the gene pool: Sons and daughters of professors and graduate students tend to be smart. And, indeed, having more college graduates in an area is a strong predictor of the success of the people born there.

But there is most likely something more going on: early exposure to innovation. One of the fields where college towns are most successful in producing top dogs is music. A kid in a college town will be exposed to unique concerts, unusual radio stations and even record stores. College towns also incubate more than their expected share of notable businesspeople.

The success of college towns does not just cross regions. It crosses race. African-Americans were noticeably underrepresented on Wikipedia in nonathletic fields, especially business and science. This undoubtedly has a lot to do with discrimination. But one small county, where the 1950 population was 84 percent black, produced notable baby boomers at a rate near those of the highest counties.

Of fewer than 13,000 boomers born in Macon, Ala., 15 made it to Wikipedia — or one in 852. Every single one is black. Fourteen of them were from the town of Tuskegee, home of Tuskegee University, a historically black college founded by Booker T. Washington. The list included judges, writers and scientists. In fact, a black kid born in Tuskegee had the same probability of becoming a notable nonathlete as a white kid born in some of the highest-scoring, majority-white college towns.

More here, fascinating. Including an implicit criticism of my own personal migration pattern (having a kid in NYC then moving out to Bergen County, NJ). Apparently being a child of an immigrant is a big plus. Got that covered for my kids.

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Cochrane on Higher Education

A few thoughts. Why does a university simultaneously borrow $3.6 billion but have $6.7 billion Invested? If borrowing is such a big deal, why not just spend the endowment on new buildings?

Answer: universities can borrow at municipal rates, free of federal tax, if they are building something. Borrowing tax-free makes financial sense, even you just stuff the marginal dollar into endowment. Of course the endowment is not invested in Treasuries — universities don’t do simple tax arbitrage. So the model is more that of a leveraged hedge fund — borrow at low tax-free rates, up to the limit imposed by tax law, and invest in high risk, (hopefully) high-return projects like hedge funds, private equity, real estate etc. The fact that investment returns are also not taxed makes this a doubly advantageous strategy. Donors: if you give now, your gift grows tax-free, while if you earn the rate of return and then give the money to the university, you pay taxes.

Ah, yes, but some donate to universities to raise status and put their names on walls. That requires big nominal (as opposed to Present Value) checks.

Here is what I think is happening: The U of C’s leaders think there will be about 5 big, global, high-prestige, science-oriented, big-idea-generating research universities left in 20 years. The gap between those and second-rate schools will grow, especially as the top 5 educational content goes online. Who wants to take an online class from the #11 university? We want to be one of the big 5. We’re behind, especially on the transition from arts and humanities to science and engineering. And if research funding moves from government to billionaires, scale and rank will be even more important. This is the “ambitious program to improve campus life while bolstering highly regarded academic programs.” Harvard (5.7) and Stanford (4.8) have more debt than us (3.6) and Yale (3.6) the same, an indication of who is in this race, and that they’re ahead of us.

Lots more to read here

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Review: Salido vs Lomachenko

Vasyl Lomachenko’s amateur boxing record is 396-1, including two Olympic gold medals (2008 and 2012). And this is in the lighter weight divisions where boxing is at its most competitive. The guy might be the greatest amateur boxer ever. He’s the most anticipated prospect in years.

It’s conventional wisdom, though, that boxers should ‘move up the ranks’ (fight progressively stronger opposition) slowly no matter how much amateur experience. That means we’d normally have to wait a while to see him fight the best.

But Vasyl’s a ballsy guy and thought he’d challenge this tradition. It makes sense: why, might ask a decorated amateur boxer, should I wait around and fight bums? Because some old trainer says so? Because some promoter’s trying to keep me down?

Well, last Saturday night Vasyl Lomachenko found out why when he lost his second pro bout to a well known but distinctly non-elite fighter in Orlando Salido. Does this mean that Lomachenko is not the real deal? It might, though it’s probably more likely that conventional wisdom is so named for a reason.

The fight started out very slow. I think Salido was intimidated by Loma’s reputation while the latter probably deliberately kept the brakes on to pace himself (he’d never been past six rounds in his career and this was to go twelve). Eventually we learned some lessons in the differences between amateur and professional boxing.

1. Body blows don’t matter in the amateurs. That point is made both literally (points aren’t awarded) and figuratively. An investment in body work will never pay off inside of a three-round fight so why bother worrying about your body? Loma’s body defense is deplorable.

2. Size matters. Salido weighed 147 pounds to Loma’s 136. Is that against the rules? In this case, yes: Salido missed weight. Yet the fight went on. When you’re fighting in a 5-day tournament or whatever, you need to actually be your weight. Strategic rehydration is a real part of professional fighting. And when money is on the line, sometimes you ignore transgressions.

3. Life ain’t fair. The refereeing was atrocious. Salido landed a LOT of low blows. And to be fair, Loma’s holding was just as egregious. Eventually Salido, hurt in the 12th, held Loma for all he was worth and stayed on his feet. Painful to watch.

4. Game plans matter in longer fights. For a guy with poor body defense, moving in and holding is not an awesome strategy. Loma didn’t trust his speed and movement for some reason and felt he needed to lock down Salido. What an immensely weak play for a guy with stratospheric physical gifts. Loma should be able to dance around just about any fighter at any level.

So Lomachenko lost. Maybe he figures these things out and fulfills his potential. He’s 26 so it’s possible he can only learn so much in time to rise to the top. He does need a lot of work.

For no particularly good reason, though, I hope he sticks to his plan of learning on the job. Here’s to an immediate rematch!



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Links: Fedcoins and Actuaries As Technical Stock Analysts

First, the idea that the fed might issue FedCoins, which would be exchangeable for USD. It would make QE much easier to pull off. Amazing thought:

It is interesting to think about why this is so implausible.  There are a few reasons:

1. YellenCoin would be a means of payment but not the medium of account.  This would move the economy into a currency substitution model, a’la Girton and Roper, but would not have the effects of a straightforward monetary expansion.

2. Cryptocurrencies are much more likely to be used for some kinds of transactions than others.  So this act of “monetary policy” would be very much non-neutral.

3. Central banks are not supposed to be seen as taking major risks or overturning the established order of things.  They are highly risk-averse when it comes to their public reputations, and their very much prefer sins of omission to sins of commission.  If the Fed established Fedcoin and something went wrong with the idea, they would be subject to especially heavy blame.  In the meantime, few people (are there exceptions?) are blaming them for not establishing a cryptocurrency.

Next, a cas podcast where the idea is floated that P&C actuaries are more like technical stock analysts than fundamental stock analysts. Actuaries are a scientific bunch and no double would freak out at being lumped in with the ‘stupid’ stock pickers, though there is some merit to the comparison. We don’t even an attempt to analyze the fundamental drivers of claims activity when valuing insurance liabilities: we mostly do voodoo extrapolation of recent trends.

I can imagine a world where the cure to such a problem is even less comprehensible than the disease. We’d need data sources too immense to understand and apply to them tools that we can’t comprehend.

The day isn’t far off…

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