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Machine learners are spoiled for data
I’ve been reading lots of machine learning books lately, and let me say, as a relative outsider coming from finance: machine learners sure are spoiled for data.
It’s like, they’ve built these fancy techniques and machines that take a huge amount of data and try to predict an outcome, and they always seem to start with about 50 possible signals and “learn” the right combination of a bunch of them to be better at predicting. It’s like that saying, “It is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail.”
In finance, a quant gets maybe one or two or three time series, hopefully that haven’t been widely distributed so they may still have signal. The effect that this new data on a quant is key: it’s exciting almost to the point of sexually arousing to get new data. That’s right, I said it, data is sexy! We caress the data, we kiss it and go to bed with it every night (well, the in-sample part of it anyway). In the end we have an intimate relationship with each and every time series in our model. In terms of quantity, however, maybe it’s daily (so business days, 262 days per year about), for maybe 15 years, so altogether 4000 data points. Not a lot to work with but we make do.
In particular, given 50 possible signals in a pile of new data, we would first look at each time series by plotting, to be sure it’s not dirty, we’d plot the (in-sample) returns as a histogram to see what we’re dealing with, we’d regress each against the outcome, to see if anything contained signal. We’d draw lagged correlation graphs of each against the outcome. We’d draw cumulative pnl graphs over time with that univariate regression for that one potential signal at a time.
In other words, we’d explore the data in a careful, loving manner, signal by signal, without taking the data for granted, instead of stuffing the kit and kaboodle into a lawnmower. It’s more work but it means we have a sense of what’s going into the model.
I’m wondering how powerful it would be to combine the two approaches.
Is willpower a quantifiable resource?
There’s a fascinating article here about “decision fatigue,” which talks about how people lose the ability to make good decisions after they’ve made a bunch of decisions, especially if those decisions required them to exert willpower. A decision can require willpower either by virtue of being a trade-off or compromise between what one wants versus what one can afford, or by virtue of being a virtuous choice, e.g. eating a healthy snack instead of ice cream.
After making lots of decisions, people get exhausted and go for the easiest choice, which is often not the “correct” one for various reasons- it could be unhealthy or too expensive, for example. The article describes how salespeople can take advantage of this human foible by offering so many choices that, after a while, people defer to the salesperson to help them choose, thus ending up with a larger bill. It also explains that eating sugar is a quick restorative for your brain; if you’ve been exhausted by too many willpower exertions, a sugary snack will get you back on track, if only for a short while.
This all makes sense to me, but what I think is most interesting, and was really only touched on in the article, is how much this concept does or could matter in understanding our culture. For example, it talks about how this could explain why poor people eat badly- they go to the grocery store and are forced to exert willpower the entire time, with every purchase, since they constantly have to decide what they can afford; at the end of that arduous process they are exhausted and end up buying a sugary snack to replenish themselves.
I’m wondering how much of our behavior can be explained by willpower as a quantifiable resource. If we imagine that each person has some amount of stored willpower, that gets replenished through food and gets depleted through decisions, would that explain some amount of variance in behavior? Would it explain why crime gets committed at certain times?
This also reminds me of the experiments they did on kids to see which one of them could postpone reward (in the form of marshmallows) the longest. Turns out the kids who could delay gratification were more likely to get Ph.D.’s (no duh!). It is of course not always appropriate to delay gratification (and it’s certainly not in anyone’s best interest that everyone in the population should want to get a Ph.D.); on the other hand being able to plan ahead certainly is a good thing.
Since delaying gratification is a form of willpower, I’ll put it in the same category and ask, how come even at the age of four some kids can do that and others can’t (or won’t)? Is it genetically wired? Or is it practiced as a family value? Or both? Is it like strength, where some people are naturally strong but then again people can work out and make themselves much stronger?
Here’s another question about willpower, which is kind of the dual to the idea of depletion: can you have too much stored willpower? Is it like sexual energy, that needs to get used or kind of boils up on its own? I’m wondering if, when you’ve been trained all your life to exert a certain amount of willpower, and then you suddenly (through becoming extremely well-off or winning the lottery) don’t need nearly as much as you’re used to, do you somehow boil over with willpower? Does that explain why really rich people join Scientology and constantly go to spas for cleansings? Are they inventing challenges in order to exert their unused, pent-up willpower? I certainly think it’s possible.
As an example, I’ve noticed that people with too little money or with too much money are constantly worrying about money. I’m wondering if this “too much money” is coinciding with “unused willpower” and the result ironically looks similar to “not enough money” in combination with “depleted willpower”. Just an idea, but Sunday mornings are for ridiculous theories after all.
How the Value-Added Model sucks
One way people’s trust of mathematics is being abused by crappy models is through the Value-Added Model, or VAM, which is actually a congregation of models introduced nationally to attempt to assess teachers and schools and their influence on the students.
I have a lot to say about the context in which we decide to apply a mathematical model to something like this, but today I’m planning to restrict myself to complaints about the actual model. Some of these complaints are general but some of them are specific to the way the one in New York is set up (still a very large example).
The general idea of a VAM is that teachers are rewarded for bringing up their students’ test scores more than expected, given a bunch of context variables (like their poverty and last year’s test scores).
The very first question one should ask is, how good is the underlying test the kids are taking? This is famously a noisy answer, depending on how much sleep and food the kids got that day, and, with respect to the content, depends more on memory than on deep knowledge. Another way of saying this is that, if a student does a mediocre job on the test, it could be because they are learning badly at their school, or that they didn’t eat breakfast, or it could be that the teachers they have are focusing more on other things like understanding the reasons for the scientific method and creating college-prepared students by focusing on skills of inquiry rather than memorization.
This brings us to the next problem with VAM, which is a general problem with test-score cultures, namely that it is possible to teach to the test, which is to say it’s possible for teachers to chuck out their curriculums and focus their efforts on the students doing well on the test (which in middle school would mean teaching only math and English). This may be an improvement for some classrooms but in general is not.
People’s misunderstanding of this point gets to the underlying problem of skepticism of our teachers’ abilities and goals- can you imagine if, at your job, you were mistrusted so much that everyone thought it would be better if you were just given a series of purely rote tasks to do instead of using your knowledge of how things should be explained or introduced or how people learn? It’s a fact that teachers and schools that don’t teach to the test are being punished for this under the VAM system. And it’s also a fact that really good, smart teachers who would rather be able to use their pedagogical chops in an environment where they are being respected leave public schools to get away from this culture.
Another problem with the New York VAM is the way tenure is set up. The system of tenure is complex in its own right, and I personally have issues with it (and with the system of tenure in general), but in any case here’s the way it works now. New teachers are technically given three years to create a portfolio for tenure- but the VAM results of the third year don’t come back in time, which means the superintendent looking at a given person’s tenure folder only sees two years of scores, and one of them is the first year, where the person was completely inexperienced.
The reason this matters is that, depending on the population of kids that new teacher was dealing with, more or less of the year could have been spent learning how to manage a classroom. This is an effect that overall could be corrected for by a model but there’s no reason to believe was. In other words, the overall effect of teaching to kids who are difficult to manage in a classroom could be incorporated into a model but the steep learning curve of someone’s first year would be much harder to incorporate. Indeed I looked at the VAM technical white paper and didn’t see anything like that (although since the paper was written for the goal of obfuscation that doesn’t prove anything).
For a middle school teacher, the fact that they have only two years of test scores (and one year of experienced scores) going into a tenure decision really matters. Technically the breakdown of weights for their overall performance is supposed to be 20% VAM, 20% school-wide assessment, and 60% “subjective” performance evaluation, as in people coming to their classroom and taking notes. However, the superintendent in charge of looking at the folders has about 300 folders to look at in 2 weeks (an estimate), and it’s much easier to look at test scores than to read pages upon pages of written assessment. So the effective weighting scheme is measurably different, although hard to quantify.
One other unwritten rule: if the school the teacher is at gets a bad grade, then that teacher’s chances of tenure can be zero, even if their assessment is otherwise good. This is more of a political thing than anything else, in that Bloomberg doesn’t want to say that a “bad” school had a bunch of tenures go through. But it means that the 20/20/60 breakdown is false in a second way, and it also means that the “school grade” isn’t an independent assessment of the teachers’ grades- and the teachers get double punished for teaching at a school that has a bad grade.
That brings me to the way schools are graded. Believe it or not the VAM employs a binning system when they correct for poverty, which is measured in terms of the percentage of the student population that gets free school lunches. The bins are typically small ranges of percentages, say 20-25%, but the highest bin is something like 45% and higher. This means that a school with 90% of kids getting free school lunch is expected to perform on tests similarly to a school with half that many kids with unstable and distracting home lives. This penalizes the schools with the poorest populations, and as we saw above penalized the teachers at those schools, by punishing them for when the school gets a bad grade. It’s my opinion that there should never be binning in a serious model, for reasons just like this. There should always be a continuous function that is fit to the data for the sake of “correcting” for a given issue.
Moreover, as a philosophical issue, these are the very schools that the whole testing system was created to help (does anyone remember that testing was originally set up to help identify kids who struggle in order to help them?), but instead we see constant stress on their teachers, failed tenure bids, and the resulting turnover in staff is exactly the opposite of helping.
This brings me to a crucial complaint about VAM and the testing culture, namely that the emphasis put on these tests, which we’ve seen is noisy at best, reduces the quality of life for the teachers and the schools and the students to such an extent that there is no value added by the value added model!
If you need more evidence of this please read this article, which describes the rampant cheating on test in Atlanta, Georgia and which is in my opinion a natural consequence of the stress that tests and VAM put on school systems.
One last thing- a political one. There is idiosyncratic evidence that near elections, students magically do better on tests so that candidates can talk about how great their schools are. With that kind of extra variance added to the system, how can teachers and school be expected to reasonably prepare their curriculums?
Next steps: on top of the above complaints, I’d say the worst part of the VAM is actually that nobody really understands it. It’s not open source so nobody can see how the scores are created, and the training data is also not available, so nobody can argue with the robustness of the model either. It’s not even clear what a measurement of success is, and whether anyone is testing the model for success. And yet the scores are given out each year, with politicians adding their final bias, and teachers and schools are expected to live under this nearly random system that nobody comprehends. Things can and should be better than this. I will talk in another blog post about how they should be improved.
I.P.O. pops
I’ve decided to write about something I don’t really understand, but I’m interested in (especially because I work at a startup!): namely, how IPO’s work and why there seem to be consistent pops. Pops are jumps in share price from the offering to the opening, and then sometimes the continued pop (or would that be fizz?) for the rest of the trading day. Here’s an article about the pop associated to LinkedIn a few weeks ago. The idea behind the article is that IPO pops are really bad for the companies in question.
The way a standard IPO works is that, when a company decides to go public, they hire an investment company to help them assess their value, i.e. form a sense of how many shares can be sold, and at what price.
A certain number of people (insiders and investors at the investment bank in particular) are then given the chance to buy some shares of the new company at the offering price. This is an obvious way in which the investment bank has an incentive to create a pop- their friends will directly benefit from pops. In fact the existence of pops and their accompanying incentives have inspired some people (like Google) to use Dutch auction methods instead of the standard.
And the myth is that there are consistent pops (here are some examples of truly outrageous pops during the dotcom bubble!). Is this really true? Or is it a case of survivorship bias? Or is there on average a pop the first day which fizzles out over the next week? I actually haven’t crunched the data, but if you know please do comment.
One question I want to know is, assuming that the pop myth is true, why does it keep happening? If it’s good for the investment bank but bad for the business, you’d think that businesses would, over time, train investment banks to stop doing this quite so much- they’d get bad reputations for big pops, or even possibly would get some of their fees removed, by contract, if the pop was too big (which would mean the investment bank hadn’t done its job well). But I haven’t heard of that kind of thing.
So who else is benefitting from pops? Is it possible that the investors themselves have an incentive to see a pop? While it’s true that the investors sell a bunch of their shares into the IPO to provide sufficient “float,” which they’d obviously like to see sold at a high price, they also have the opportunity to buy a restricted number of “directed shares“, which are shares they can buy at the offering price and then immediately sell; these they’d clearly like to buy at a low price and then see a pop. So I guess it depends on the situation for a given insider whether they are selling or buying more – I don’t know what the actual mix typically is, but I imagine it really depends on the situation; for example, there are always shared created out of thin air on an IPO day, so it will depend on how much of the float is coming from the investors and how much is coming from thin air.
The most standard thing though is for someone like an employee is to have common shares (or options to buy common shares) which they can only sell 6 months (or potentially more if the options are vested) after the IPO, which I guess means they are probably somewhat neutral to the pop, depending on its long term effect.
Speaking of long term effects, I think the biggest and most persuasive argument investment banks make to investors about stock evaluation, is that it’s better to underestimate the share price than to overestimate it. The argument is that a pop may hurt the business but it’s great for investors and thus the reputation value is overall good (this argument can obviously go too far if the pop is 50% and sustained), but that an overevaluation could result in not being able to sell the shares and having a sunken ship that never gets enough wind to sail. In other words, the risks are asymmetrical. I’m not sure this is actually true but it’s probably a good scare tactic for the investment banks to use to line their friends’ pockets.
Default probabilities and recovery rates
I’ve been kind of obsessed lately with the “big three” ratings agencies S&P, Moody’s, and Fitch. I have two posts (this one and that one) where I discuss the idea of setting up open source ratings models to provide competition to them and hopefully force them to increase transparency (speaking of transparency, here’s an article which describes how well they cope with one of the transparency rules they already have).
Today I want to talk about a technical issue regarding ratings models, namely what the output is. There are basically two choices that I’ve heard about, and it turns out that S&P and Moody’s ratings have different outputs, as was explained here.
Namely, S&P models the probability of default, which is to say the probability that U.S. bonds will go through a technical default, I believe within the next year; Moody’s, on the other hand, models the “expected loss”, which is to say they model the future value of U.S. bonds by modeling the probability of default combined with the so-called “recovery rate” once the default occurs (the recovery rate is the percent of the face value of the bond that bond-holders can expect to receive after a default).
The reason this matters is that, for U.S. bonds specifically, even if default occurs technically, few people claim that the bonds wouldn’t eventually be worth face value. So S&P is modeling the probability that, through political posturing, we could end up with a technical default (i.e. not beyond the realm of possibilities), whereas Moody’s models what the value of the bond would be if that happened (i.e. face value almost certainly). It makes more sense, considering this, that S&P has downgraded U.S. debt but that Moody’s hasn’t.
This isn’t the only time such issues matter. Indeed, various different “ratings” models claim to model different things, which end up being more or less crucial depending on the situation:
- S&P: probability of default
- Fitch: probability of default
- Moody’s: expected loss
- Altman’s Z-scores: probability of corporate default
- Credit Grades: probability of default of publicly traded companies
- credit default swaps: expected loss
I threw in Credit Grades, which is a product that is offered by MSCI. One of the inputs for the Credit Grades model is the market volatility of the company in question, whereas most of the other models’ inputs are primarily accounting measurements. In particular, if the market volatility of the company is enormous, then the probability of default is increased. I wonder what it is now rating Bank of America at?
Credit default swaps are not ratings models directly- but you can infer the market’s expectation of default and recovery rate from the price of the CDS, since the cashflow of a CDS works like this: the owner of the CDS pays quarterly “insurance payments” for as long as the bond in question hasn’t defaulted, but if and when the bond defaults the writer of the CDS pays the remainder of the face value of the bond after removing the recovery rate. In other words, if the bond defaults and the recovery rate turns out to be 63%, then the CDS writer is liable for 37% of the face value of the bond.
Not to unfairly single out one issue among many that is difficult, but recovery rates are pretty difficult to model- the data is secondary market data, i.e. it’s not traded on directly but rather inferred from market prices like CDSs that are traded, and often people just assume a 40% recovery rate even when there’s no particular reason to believe it.
For that reason it’s not necessarily better information (in the sense of being more accurate) to model default with recovery rate consideration than it is to model straight out default probability, which is already hard. On the other hand, modeling expected loss like Moody’s is probably a more intuitive output, since as we’ve seen with the uproar last week, S&P is getting lots of flak for their ratings change but Moody’s has been sitting pretty.
In fact, U.S. sovereign debt is an extreme example in that we actually know the recovery rate is almost surely 100%, but in general for corporate debt different guesses at the expected recovery rates will drastically change the value of the bond (or associated CDS).
I guess the moral of this story for me is that it’s super important to know exactly what’s being modeled – I am now ready to defend S&P’s ratings change – and it’s also important to choose your model’s output well.
Are Corporations People?
Recently Mitt Romney put his foot in his mouth when trying to deal with a heckler in Iowa. He said, “Corporations are people, my friend.” He’s gotten plenty of backlash since then, even though he attempted a softer follow-up with, “Everything corporations earn ultimately goes to people. Where do you think it goes?”
It makes me wonder two things. First, why is it viscerally repulsive (to me) that he should say that, and second, beyond the gut reaction, to what extent does this statement make sense?
The New York Times summed up the feeling pretty well with the statement, “…he seemed to reinforce another image of himself: as an out-of-touch businessman who sees the world from the executive suite.” Another way to say this is that the remark exposed a world view that I don’t share, and which goes back to this post containing the following:
Conservatives, for example, see business as primarily a source of social and economic good, achieved by the market mechanism of seeking to maximize profit. They therefore think government’s primary duty regarding businesses is to see that they are free to pursue their goal of maximizing profit. Liberals, on the other hand, think that the effort to maximize profit threatens at least as much as it contributes to our societies’ well-being. They therefore think that government’s primary duty regarding businesses is to protect citizens against business malpractice.
Fair enough- Mitt Romney doesn’t claim to be a liberal, after all. He was really doing us a favor by admitting how he sees things; heck, I wish all politicians would be susceptible to heckling and would go off-script and say what they actually mean every now and then.
In this way I can come to terms with the fact that Romney is essentially protective of corporations and their “human rights,” at least as an emotional response (like when discussing tax increases). But is he factually right? Are corporations equivalent to people in a legal or ethical way?
I’m no lawyer but it seems that, in certain ways, corporations are legally treated as persons, and that this has been an ongoing legal question for 200 years. In terms of political contributions, which is somehow easier to understand but maybe less systemically important, they are certainly treated like persons, in that there is no limit to the amount of money they can contribute politically (although this issue has gone back and forth historically).
Ethically, however, there seems to me to be a huge obstacle in considering corporations equivalent to people. Namely, it seems to be much easier to ascribe the rights of people to corporations than to ascribe the responsibilities of people to corporations. In particular, what if corporations behave badly and need to be punished? How do we follow through with that in a way that makes sense? Is there a death penalty for corporations? (This question originally came to me by way of Josh Nichols-Barrer, by the way)
The most obvious direct punishment we have for corporations is fines for accounting fraud or whatever, and the most obvious indirect punishment is market capitalization loss, i.e. the stock price goes down, if it’s a publicly traded company, or if not, reputation loss, which is vague indeed. However, in those cases it’s mostly the shareholders that suffer- the corporation itself, and its management, typically lives on.
Rarely, there is direct legal action against a decision maker at the company, but that certainly can’t count as a death penalty for the corporation itself, since the toxic culture which gave rise to those decisions is left intact. Even if we got serious and closed down a company, it’s not clear what effect that would have since a new legal entity could be re-formed with similar ideals and people (although the nuisance of doing this would be pretty substantial depending on the industry). But maybe that’s the best we can do: “moral bankruptcy” proceedings. Another problem with that idea is that many of the people who were in charge of the bad decisions would be the first to jump ship and go to other corporations to try again with more stealth; that’s certainly what I’ve seen happen in finance.
From my perspective, none of the punishments described above actually deter bad behavior in a meaningful way. If we treat corporations as people, then they would be people with a permanent diplomatic immunity; this doesn’t sit well with my sense of fairness or my sense of how people respond to incentives.
Open Source Ratings Model (Part 2)
I’ve thought more about the concept of an open source ratings model, and I’m getting more and more sure it’s a good idea- maybe an important one too. Please indulge me while I passionately explain.
First, this article does a good job explaining the rot that currently exists at S&P. The system of credit ratings undermines the trust of even the most fervently pro-business entrepreneur out there. The models are knowingly games by both sides, and it’s clearly both corrupt and important. It’s also a bipartisan issue: Republicans and Democrats alike should want transparency when it comes to modeling downgrades- at the very least so they can argue against the results in a factual way. There’s no reason I can see why there shouldn’t be broad support for a rule to force the ratings agencies to make their models publicly available. In other words, this isn’t a political game that would score points for one side or the other.
Second, this article discusses why downgrades, interpreted as “default risk increases” on sovereign debt doesn’t really make sense- and uses as example Japan, which was downgraded in 2002 but still continues to have ridiculously low market-determined interest rates. In other words, ratings on governments, at least the ones that can print their own money (so not Greece), should be taken as a metaphor of their fiscal problems, or perhaps as a measurement of the risk that they will have potentially spiraling inflation when they do print their way out of a mess. An open source quantitative model would not directly try to model the failure of politicians to agree (although there are certainly market data proxies for that kind of indecision), and that’s ok: probably the quantitative model’s grade on sovereign default risk trained on corporate bonds would still give real information, even if it’s not default likelihood information. And, being open-source, it would at least be clear what it’s measuring and how.
I’ve also gotten a couple excellent comments already on my first post about this idea which I’d like to quickly address.
There’s a comment pointing out that it would take real resources to do this and to do it well: that’s for sure, but on the other hand it’s a hot topic right now and people may really want to sponsor it if they think it would be done well and widely adopted.
Another commenter had concerns of the potential for vandals to influence and game the model. But here’s the thing, the point of open source is that, although it’s impossible to avoid letting some people have more influence than others on the model (especially the maintainer), this risk is mitigated in two important ways. First of all it’s at least clear what is going on, which is way more than you can say for S&P, where there was outrageous gaming going on and nobody knew (or more correctly nobody did anything about it). Secondly, and more importantly, it’s always possible for someone to fork the open source model and start their own version if they think it’s become corrupt or too heavily influenced by certain methodologies or modeling choices. As they say, if you don’t like it, fork it.
Update! There’s a great article here about how the SEC is protecting the virtual ratings monopoly of S&P, Moody’s, and Fitch.
The Life Cycle of a Hedge Fund
When people tell me they are interested in working at a hedge fund, I always tell them a few things. First I talk about the atmosphere and culture, to make sure they would feel comfortable with it. Then I talk to them about which hedge fund they’re thinking about, because I think it makes a huge difference, especially how old a hedge fund is.
Here’s the way I explain it. When a hedge fund is new, a baby, it either works or it doesn’t. If it doesn’t, you never even hear about it, a kind of survivorship bias. So the ones you hear about work well, and their founders do extremely well for themselves.
Then the hedge fund hires a bunch of people, and this first round of people also does well, and they start filling up the ranks of MD’s (managing directors). Maybe at this point you’d say the hedge fund is an adolescent. Once you have a bunch of MD’s that are rich and smart, though, they become pretty protective of the pot of money they generate each year, especially if the pot isn’t as big as it once was, because of competition from other hedge funds.
However, this doesn’t always mean they stop hiring. In fact, they often hire people at this stage, young, smart, incredibly hard working people, who are generally screwed in the sense that they have very little chance of being successful or ever becoming MD. This is what I’d term an adult hedge fund. They have complicated rules which make sense for the existing MD’s but which keep new people from ever succeeding.
For example, when you get to a hedge fund, you start being assigned models to work on. You learn the techniques and follow the rules of the hedge fund, like making sure you don’t bet on the market, etc. If your model starts to look promising, they make sure you are not “remaking” an existing model that is currently being used. That is to say, they make sure, either by telling you what to do or asking you to do it yourself, that your bets are essentially orthogonal (in a statistical sense) to the current models. This often has the effect of removing the signal that your model had, or at least removing enough of it that your model no longer is statistically significant to go into production.
In other words, if the existing models are a relatively large collection, that perhaps spans the space of “current models that seem to work in the way we measure models” (I know this is a vague concept but I do think it means something), then you are kind of up shit’s creek to find a new model. By contrast, if you happened to start at a young hedge fund, or start your own hedge fund, then your model couldn’t be redundant, since there wouldn’t be anything to compete with it.
The older hedge funds have lots of working models, so there are lots of ways for your new, good-looking model to be swatted down before it has a chance to make money. And the way things work, you don’t ever get credit for a model that would have worked if there had been fewer models in production. In fact you only get credit if you came up with a new model which made shit tons of money.
Which is to say, under this system, the founders and the guys brought in during the first round of hiring are the most likely to get credit. Even if an MD retires, their working models don’t die, since they are algorithmic and they still work. But the money they generate goes into the company-wide pot, which is to say mostly goes to MD’s. So the MD’s have no incentive to change the system.
It also has another consequence, which is that the people hired in the second or further rounds slowly realize that their models are perfectly good but unused, and that they’ll never get promoted. So they end up leaving and starting their own funds or joining young funds, just so they can run the same models. So another consequence of adult hedge funds is that they spawn their own competition.
The only way I know of for a hedge fund to avoid this aging process is to never hire anyone after the first round. Or maybe to hire very few people, slowly, as the MD’s retire and as the models stop working and you need new ones, to be sure that the people they hire have a chance to succeed.
Wall Street versus us
There have been two articles in the past few days which address the mentality of people working on Wall Street versus the rest of us.
First, we have this article from William Cohen, posted on Bloomberg.com, which is the first part of a series entitled, “Ending the Moral Rot on Wall Street.” This first part doesn’t contain much new; it goes over just how obnoxious and easy to hate the various Goldman Sachs assholes were when they packaged and sold mortgage debris and then emailed their friends about how much money they stood to make. And the second (and perhaps further) parts promise to explain how we are going to address the corruption and greed. My complaint, which is totally unfounded since I haven’t read the next parts, is that this guy is not disagreeing well. In other words, he’s setting up the guys on Wall Street to be monstrous and ethically vapid. This attitude is not going to help really understand the situation, nor will it lend itself to satisfying solutions. Here’s an example of the kind of “they are monsters” prose that probably won’t help:
These crimes are being committed, he said, by people who “have already made more money than could ever be spent in one lifetime and achieved more impressive success than could ever be chronicled in one obituary. And it begs the question, is corporate culture becoming increasingly corrupt?”
Yes, it certainly does raise that question.
Second, we have this blog post by Mark Cuban, which was originally posted in 2010 but is still relevant. In it, an effort is made to understand the actual mentality of the traders on Wall Street. Namely, they are framed as hackers:
Just as hackers search for and exploit operating system and application shortcomings, traders do the same thing. A hacker wants to jump in front of your shopping cart and grab your credit card and then sell it. A high frequency trader wants to jump in front of your trade and then sell that stock to you. A hacker will tell you that they are serving a purpose by identifying the weak links in your system. A trader will tell you they deserve the pennies they are making on the trade because they provide liquidity to the market.
I recognize that one is illegal, the other is not. That isn’t the important issue.
I agree with this characterization, and moreover I applaud the effort to understand the culture. These guys actually do think they are playing fairly within the context of their “game” (and they do care that it’s legal). To change their mindset we need to actually change the rules of the game, not just complain that they are corrupt, because, like in a religious disagreement, they can easily dismiss such talk as irrelevant to their lives.
Going back to the first article, it says:
That Wall Street executives have been able to avoid any shred of responsibility for their actions in the years leading up to the crisis speaks volumes not only about an abject ethical deterioration but also about the unhealthy alliance that exists between the powerful in Washington and their patrons in New York. Our collective failure to demand redress against a Wall Street culture that remains out of control is one of the more troubling facts of life in America today.
I agree that we do need to demand redress, but not against a culture’s ethical deterioration, which is just far too vague, but rather against individual corrupt actions. In other words we need to make the punishments for well-defined evil deeds clear and we need to follow through with the consequences. In order to do this we need to demand transparency so we can start to even define evil deeds. This means some system of understanding the models that are being used, and the risks being taken, and a market consensus that the models are sufficient. It means the actual threat of losing actual money, or even going to jail, if the models being used are crappy or if it turns out you were lying about the risks you were taking – or even if you were ignorant of them.
Adam Smith made me buy a Kindle
When I was pregnant with my third son, and working at D.E. Shaw, I got really into reading Adam Smith’s seminal work “Wealth of Nations” on the subway rides to and from work. Once the baby came, though, the problem was that the book is huge, like 1,200 pages, and impossible to read while breastfeeding. In my frustration, and to combat baby brain-rot, I bought a Kindle to continue my reading through many many exhausting hours those first few months. Totally worth it, an investment in my sanity.
This post got me remembering my personal experience with Adam Smith. Adam Smith has really gotten a bum rap. He is generally known for inventing the concept of the invisible hand, which is the idea that, as long as each person is working as hard as they can to personally profit from their labor, the overall economy will benefit from that self-interest. However, it’s often used is as an excuse for why regulations are unnecessary, because somehow, the feeling goes, the invisible hand is all we need. To tell you the truth, I don’t even remember seeing that in his book. Maybe it was there, and maybe I was getting barfed on during that page, but he definitely didn’t focus on it. He had other fascinating points though which he did reiterate.
Here’s why Wealth of Nations is so amazing. First, Smith really is incredibly good at explaining how markets work and, considering that he was inventing a field as he was writing, did so extremely well (although at times the book can be a bit repetitive, probably because he never invented notation- he just rewrote out entire phrase whenever he wanted to refer to an idea). The most basic goal of the book is to explain that it makes more sense to trade between countries so that things that are relatively cheaper to make or produce in Country A can be traded for things that are easier for Country B to make, and to generalize that to “between towns” or “between people”.
The examples he uses are really interesting, and include various layered considerations such as whether the goods are easily stored. For example, he maintains that cotton and wools should absolutely have free trade, since there is a clear advantage to having the appropriate climate for the growth of the plants, as well as the long storage. By contrast, he talks about the price of meat in England versus Argentina, being non-storable, and mentions that the price of a cow in Argentina is equal to the tip you need to give a village boy to go catch a cow (I’m paraphrasing because it was almost three years ago).
Another fascinating aspect of the book is that, since he wrote it in the 1770’s, economic conditions were really different, and he talks at length of the peasant classes in various countries. One of the most striking descriptions comes when he describes how much healthier the Irish peasants were compared to the Scottish peasants, because they ate potatoes, whereas the Scots ate oatmeal. It took me a few minutes to realize that he meant, that they only ate oatmeal. And he was saying that you could tell, by the way the 20 year olds still had teeth in Ireland, how much better a staple potatoes are than oatmeal.
He also talks about the various economies of South America and Europe and it sounds like they were doing better than Great Britain, especially Holland, which was a huge trading country back then. It’s fascinating just to understand, at the level of the average person, the peasants and the merchants, how incredibly different the world was then, something you don’t get as good a look at reading history books (at least the history books I’ve read).
Adam Smith was certainly pro-business, in the sense that he wanted a functioning and efficient system to work for all of the people in the world. However, he was well aware of the natural tendencies of people in power to abuse that power. He speaks at length against monopolies, which he thinks are a natural tendency, and claims that regulations to prevent such things are absolutely necessary.
He also talks at length about currencies and bank notes and the concept of borrowing money to be paid later. He is a proponent of usury laws- he doesn’t think it’s fair to entrap people into debt that they can’t repay (and back then I believe the consequences for unpaid debt were pretty severe). He also goes into incredible detail in describing the way Scotland went through a credit crisis, caused by a lending bubble, where people were cycling through various banks with different loans, borrowing more money to repay other debts, and which spiraled into a huge mess which caused the banking system to collapse. The Bank of England itself defaulted as well in one of his other historical accounts of lending bubbles.
One really interesting point he made about the credit crises he talks about is that, in those days, if you had money, which were called bank notes, then if you wanted to use them in another country you’d have to exchange them for gold when you left the country, and then you’d have to exchange the gold back into bank notes when you entered the next country. He claims that this system actually limited the scope of the credit crisis from going beyond the shores of Scotland; he used a kind of conservation of money argument, wherein he considered promised money, i.e. bank notes, to be only probabilistically worth something . Of course there are many parallels to be made to our current credit crisis, but that part about containing the crisis inside a country really makes me think about how much China has lent to the United States.
Adam Smith had one huge blind spot, which was the way he talked about slaves. It was a long time ago and times were different but it’s really hard to read those passages where he talks condescendingly about how naturally lazy slaves are, although he also mentions how little motivation they have. It’s totally brutal, but then again if you read the 1911 Encyclopedia Britannica you will find much the same kind of thing and worse.
Why should you care about statistical modeling?
One of the major goals of this blog is to let people know how statistical modeling works. My plan is to explain as much as I can in simple plain English, with the least amount of confusion, and the maximum amount of elucidation at every possible level, so every reader can take at least a basic understanding away.
Why? What’s so important about you knowing about what nerds do?
Well, there are different answers. First, you may be interested in it from a purely cerebral perspective – you may yourself be a nerd or a potential nerd. Since it is interesting, and since there will be I suspect many more job openings coming soon that use this stuff, there’s nothing wrong with getting technical; it may come in handy.
But I would argue that even if it’s not intellectually stimulating for you, you should know at least the basics of this stuff, kind of like how we should all know how our government is run and how to conserve energy; kind of a modern civic duty, if you will.
Civic duty? Whaaa?
Here’s why. There’s an incredible amount of data out there, more than every before, and certainly more than when I was growing up. I mean, sure, we always kept track of our GDP and the stock market, that’s old school data collection. And marketers and politicians have always experimented with different ads and campaigns and kept track of what does and what doesn’t work. That’s all data too. But the sheer volume of data that we are now collecting about people and behaviors is positively stunning. Just think of it as a huge and exponentially growing data vat.
And with that data comes data analysis. This is a young field. Even though I encourage every nerd out there to consider becoming a data scientist, I know that if a huge number of them agreed to it today, there wouldn’t be enough jobs out there for everyone. Even so, there will be, and very soon. Each CEO of each internet startup should be seriously considering hiring a data scientist, if they don’t have one already. The power in data mining is immense and it’s only growing. And as I said, the field is young but it’s growing in sophistication rapidly, for good and for evil.
And that gets me to the evil part, and with it the civic duty part.
I claim two things. First, that statistical modeling can and does get out of hand, which I define as when it starts controlling things in a way that is not intended or understood by the people who built the model (or who use the model, or whose lives are affected by the model). And second, that by staying informed about what models are, what they aren’t, what limits they have and what boundaries need to be enforced, we can, as a society, live in a place which is still data-intensive but reasonable.
To give evidence to my first claim, I point you to the credit crisis. In fact finance is a field which is not that different from others like politics and marketing, except that it is years ahead in terms of data analysis. It was and still is the most data-driven, sophisticated place where models rule and the people typically stand back passively and watch (and wait for the money to be transferred to their bank accounts). To be sure, it’s not the fault of the models. In fact I firmly believe that nobody in the mortgage industry, for example, really believed that the various tranches of the mortgage backed securities were in fact risk-free; they knew they were just getting rid of the risk with a hefty reward and they left it at that. And yet, the models were run, and their numbers were quoted, and people relied on them in an abstract way at the very least, and defended their AAA ratings because that’s what the models said. It was a very good example of models being misapplied in situations that weren’t intended or appropriate. The result, as we know, was and still is an economic breakdown when the underlying numbers were revealed to be far far different than the models had predicted.
Another example, which I plan to write more about, is the value-added models being used to evaluate school teachers. In some sense this example is actually more scary than the example of modeling in finance, in that in this case, we are actually talking about people being fired based on a model that nobody really understands. Lives are ruined and schools are closed based on the output of an opaque process which even the model’s creators do not really comprehend (I have seen a technical white paper of one of the currently used value-added models, and it’s my opinion that the writer did not really understand modeling or at best tried not to explain it if he did).
In summary, we are already seeing how statistical modeling can and has affected all of us. And it’s only going to get more omnipresent. Sometimes it’s actually really nice, like when I go to Pandora.com and learn about new bands besides Bright Eyes (is there really any band besides Bright Eyes?!). I’m not trying to stop cool types of modeling! I’m just saying, we wouldn’t let a model tell us what to name our kids, or when to have them. We just like models to suggest cool new songs we’d like.
Actually, it’s a fun thought experiment to imagine what kind of things will be modeled in the future. Will we have models for how much insurance you need to pay based on your DNA? Will there be modeling of how long you will live? How much joy you give to the people around you? Will we model your worth? Will other people model those things about you?
I’d like to take a pause just for a moment to mention a philosophical point about what models do. They make best guesses. They don’t know anything for sure. In finance, a successful model is a model that makes the right bet 51% of the time. In data science we want to find out who is twice as likely to click a button- but that subpopulation is still very unlikely to click! In other words, in terms of money, weak correlations and likelihoods pay off. But that doesn’t mean they should decide peoples’ fates.
My appeal is this: we need to educate ourselves on how the models around us work so we can spot one that’s a runaway model. We need to assert our right to have power over the models rather than the other way around. And to do that we need to understand how to create them and how to control them. And when we do, we should also demand that any model which does affect us needs to be explained to us in terms we can understand as educated people.
Some R code and a data mining book
I’m very pleased to add some R code which does essentially the same thing as my python code for this post, which was about using Bayesian inference to thing about women on boards of directors of S&P companies, and for this post, which was about measuring historical volatility for the S&P index. I have added the code to those respective posts. Hopefully the code will be useful for some of you to start practicing manipulating visualizing data in the two languages.
Thanks very much to Daniel Krasner for providing the R code!
Also, I wanted to mention a really good book I’m reading about data mining, namely “Data Analysis with Open Source Tools,” by Phillipp Janert, published by O’Reilly. He wrote it without assuming much mathematics, but in a sophisticated manner. In other words, for people who are mathematicians, the lack of explanation of the math will be fine, but the good news is he doesn’t dumb down the craft of modeling itself. And I like his approach, which is to never complicate stuff with fancy methods and tools unless you have a very clear grasp on what it will mean and why it’s going to improve the situation. In the end this is very similar to the book I would have imagined writing on data analysis, so I’m kind of annoyed that it’s already written and so good.
Speaking of O’Reilly, I’ll be at their “Strata: Making Data Work” conference next month here in New York, who’s going to meet me there? It looks pretty great, and will be a great chance to meet other people who are as in love with sexy data as I am.
How do you disagree?
I remember when I was considering moving to New York from Boston, in late 2004. I came to give a number theory seminar at the CUNY Graduate Center, and afterwards we had a very nice dinner and discussion. Bush had just won re-election, and being typical left-wing academics, we were all disappointed by the news. The most startling aspect of that conversation to me was how often the word “crazy” or “stupid” was used to describe this result. In other words, it seemed like the only way we could come to terms with how half the country had voted for Bush was to describe them as feeble-minded one way or the other.
Gary Gutting wrote a wonderful Opinionator article in today’s New York Times which addresses this issue. It talks about the difference between logical argument and rational thought. He first promotes the idea that we each carry around a developed “picture” of the world:
Conservatives, for example, see business as primarily a source of social and economic good, achieved by the market mechanism of seeking to maximize profit. They therefore think government’s primary duty regarding businesses is to see that they are free to pursue their goal of maximizing profit. Liberals, on the other hand, think that the effort to maximize profit threatens at least as much as it contributes to our societies’ well-being. They therefore think that government’s primary duty regarding businesses is to protect citizens against business malpractice.
He then goes on to say that it’s not irrational to have a picture of the world in mind- we all do it, and it’s an important if not essential way to develop moral, political, and religious views. Moreover, we reasonably view other peoples’ opinions in the context of our pictures, looking naturally for evidence that ours is right.
But what does qualify as irrational is when we stick to our picture in light of really good evidence against its consistency:
But although accepting one of these rival pictures is not irrational, inflexible adherence to it can be. Neither picture would be viable without an exception-clause that acknowledges a certain validity to the rival picture. When an issue about regulation comes up, it’s entirely appropriate (and rational) for liberals and conservatives to begin with an inclination to the response generally favored by their picture. But both sides need to attend to the specific facts of the situation at hand and take seriously the possibility that these facts give reason for invoking the exception-clause in their picture. (For example: The risk from that nuclear plant is too big to take for the sake of free market principles, or the severity of our unemployment makes it worthwhile to exempt small businesses from some record-keeping regulations.) When liberals or conservatives become incapable of thinking this way, their positions become irrational.
I’d like to go one step further (because I agree with everything he said) and ask, what can we do to encourage ourselves and the people we disagree with to have this exception-clause out and ready to use?
It seems to me that when you approach a disagreement armed with facts and arguments to prove your point, you may as well concede defeat before you begin – you won’t “win” an argument that way, at least if it’s a deep argument, even if you can leave it feeling like you made the cleverer points, because you will not have persuaded anyone to change their mind. On the other hand, if you approach disagreement genuinely wondering why the other person feels and thinks the way they do, it becomes much easier to hone in on the basic cause for conflict, and for each person in the discussion to take out their exception-clause and listen to logical argument. In fact I don’t think logical argument can be useful until this point of readiness has been reached. I will call this approach, where you are each mutually assured of the exception-clause readiness before delving into logical argument, as “disagreeing well”.
For example, if I had the time, it would be fascinating to get to know sufficiently many people who voted for Bush in 2004 to be not at all surprised that he won the election. It’s a sad fact about the insularity of my life that I don’t know enough people like that.
More generally, I think a key element of developing your ability to disagree well is to expose yourself to lots of opinions. I am glad to have done a few really different jobs – loading trucks for Fair Foods, barista at Coffee Connection, secretary at a corrupt computer hardware store, student, teacher, quant, professor, data scientist – and met enough people of different classes and backgrounds that I feel relatively exposed to the world- but only the world of the Northeast United States, which is primarily composed of Democrats (although my excursions into the Bluegrass community may be the exception to that rule).
Here’s the irony of disagreeing well: you end up not actually believing your own opinion nearly as much as you thought to begin with. That’s probably why it’s hard to do, because it’s scary to put your belief on the line in an attempt to understand someone else’s viewpoint better. It’s way more work, and it’s for the most part a relationship-building event, with the logical discussion coming in after a long time and sporadically. In particular you can’t plan it and you won’t know how long it will take or even if it will work. I think, though, that to have the most interesting and provocative discussions, we need to do it anyway, even though for the most part you end up more confused than convinced, or convincing.
What about you? How do you disagree well? How do you take out your exception clause and how do you convince other people to do the same?
Why didn’t anybody invite me!?
There was an attempt yesterday morning to increase transparency on Wall St.
Three strikes against the mortgage industry
There’s a great example here of mortgage lenders lying through their teeth with statistics. Felix Salmon uncovers a ridiculous attempt to make loans look safe by cutting up the pile of mortgages in a tricky way- sound familiar at all?
And there’s a great article here about why they are lying. Namely, there is proposed legislation that would require the banks to keep 5% of the packaged mortgages on their books.
And finally here’s a great description of why they should know better. A breakdown of what banks are currently doing to avoid marking down their mortgage book.
Is too big to fail a good thing?
I read this blog post a couple of days and it really got me thinking. This guy John Hempton from Australia is advocating the too big to fail model- in fact he things we should merge more big banks together (Citigroup and Wells Fargo) because we haven’t gone far enough!
His overall thesis is that competition in finance increases as a function of how many banks there are out there and is a bad thing for stockholders and for society, because it makes people desperate for profit, and in particular people increase their risk profiles in pursuit of profit and they blow up:
What I am advocating is – that as a matter of policy – you should deliberately give up competition in financial services – and that you should do this by hide-bound regulation and by deliberately inducing financial service firms to merge to create stronger, larger and (most importantly) more anti-competitive entities.
He acknowledges that the remaining banks will be hugely profitable, and perhaps also extremely lazy, but claims this is a good thing: we would, as a culture, essentially be paying a fee for stability. It’s something we do all the time in some sense, when we buy insurance. Insurance is a fee we pay so that disruptions and small disasters in our lives don’t completely wipe us out. So perhaps, as a culture, this would be a price worth paying?
The biggest evidence he has that this setup works well is that it works in Australia- they have four huge incompetent yet profitable banks there, and they don’t blow up. People who work there are sitting pretty, I guess, because they really are just living in a money press. There is no financial innovation because there’s no competition.
I guess I have a few different reactions to this scenario. First, it’s kind of an interesting twist on the too-big-to-fail debate, in that it’s combined with the idea I already talked about here of having a system of banks that are utilities. John is saying that, really, we don’t need to make that official, that as soon as banks are this huge, we are already done, they are essentially going to act like utilities. This is super interesting to me, but I’m not convinced it’s a necessary or even natural result of huge banks.
Second, I don’t buy that what happened in Australia will happen here- perhaps Australia squelched financial innovation through regulations and the existing boring system, but maybe the people who would have been financial innovators all just moved to the U.S. and became innovators here (there are plenty of examples of that!). In other words Australia may have made it just a bit too difficult to be competitive relative to what else is out there- if everyone tried to be that repressive to financial innovation, we may see people moving back into Australia’s financial waters (like sharks).
Third, I think what John is talking about is an example of a general phenomenon, namely that, in the limit as regulations go to infinity, there is only one bank left standing. This is because every additional regulation requires a lawyer to go over the requirements and a compliance person to make sure the rules are being followed continuously. So the more regulation, the more it behooves banks to merge so that they can share those lawyers and compliance officers to save costs. In the end the regulations have defined the environment to such an extent that there’s only one bank that can possibly follow all the rules, and knows how to because of historical reasons. And that one, last bank may as well be a government institution, albeit with better pay, especially for its managers.
But we don’t have that kind of regulatory environment, and hedge funds are alive and well. They have to follow some rules, it’s absolutely true, but it’s still possible to start a smallish hedge fund without a million lawyers.
I guess what I’m concluding is that if we had formed our very few, very huge banks because of a stifling regulatory environment, then maybe we would have an environment that is sufficiently anti-competitive to think that our banks would serve us as slightly overpaid utilities. However, that’s not why we have them – it was because of the credit crisis, and the rules and regulations haven’t changed that much since then.
At the same time, I don’t totally disagree that huge banks do become anti-competitive, just by dint of how long it takes them to make decisions and do things. But I’m not sure anti-competitive is the same thing as low-risk.
Elizabeth Warren: Moses and the Promised Land
This is a guest post by FogOfWar
In Biblical style, Elizabeth Warren (EW) was not nominated to head the CFPB (Consumer Financial Protection Bureau). Having spearheaded the movement to create the institution, pushed to make it part of the otherwise-generally-useless* Dodd Frank “Financial Reform” Bill, and spent the better part of the last two years staffing the actual CFPB and moving it into gear, she has now been deemed too controversial by what passes for a President these days.
One of my favorite EW quotes: “My first choice is a strong consumer agency. My second choice is no agency at all and plenty of blood and teeth left on the floor.” This still remains to be seen, as opposition to the CPFB (and filibuster threats to any appointment to head the Bureau) remains in the face of nominee Richard Cordray. In fact, if one were inclined to be an Obama apologist (I gave up apologizing for Obama right about here), one might view the Warren-Cordray switch as a potentially brilliant tactical maneuver, with the emphasis on “potentially”. If the opposition to the CPFB took its persona in EW, then sidestepping her personally to get the agency up and running would be worthwhile, particularly as Cordray seems at least as assertively pro-consumer as EW (a bank lobbyist described him as “Elizabeth Warren without the charm”).
Barney Frank believes gender bias played a role. Maybe yes, maybe no and the Cordray confirmation will give some evidence to that question. I suspect the Republican opposition isn’t stupid and knows that Cordray will run a good agency. If that’s right then passing over EW doesn’t really serve any purpose.
Hard to tell what a public figure is really like, but my sense is EW doesn’t have any ego attached to running the agency personally. And what she does next is really up to her, I mean who really cares what we think she should do?
Wait—this is a blog! Our Raison d’être is practically making suggestions that no one will listen to, so let’s go…
1. Run for Congress
The biggest idea floated around. Yves Smith thinks it’s a terrible idea. I’m not entirely convinced—there are many ways to make a difference in this world, and being one minority member of a large and powerful body, and thus moving the body incrementally in the right direction can be a very good thing.
Two questions though: can she win (a few early stage polls seemed to indicate no, but do early stage polls really have much predictive value on final election results? Cathy? Fivethirtyeight?), and on which party platform would she run (I vote for running as an Independent)? Any thoughts from the ground from our MA-registered voters?
2. The “Al Gore” option
EW could continue to advocate, lecture and write outside of political office. She’s good television and would be free to speak without the gag order of elected office. Definitely something to be said for this option. Just realized pulling links for this post that EW was the person from the movie “Maxed Out”. Part of me thinks “damn that was effective and she should do more of that because it was so effective” and part of me thinks “wait, that movie came out in 2006 and no one listened and no one will listen”, and then the other part goes “but it can happen—you’ve actually seen social perceptions change in the wake of Al Gore (and yes, lots and lots of other people, but sparks do matter) with real and deep impacts.”
3. The “Colin Powell” option
Y’now, being in the public light kinda sucks ass. Colin Powell passed up a run for President, and largely retired to private life, and doesn’t seem to have any complaints about it. One legitimate option is to say “I did my part, you guys fight the good fight & I’m going to hang out with my grandkids on the beach.”
Any other suggestions?
*-Paul Volker deserves a parallel post of equal length for pushing the Volker Rule through this legislation and similarly receiving the thanks of being sidelined by the TBTF bank-capital-must-increase-even-if-the-peasants-have-to-eat-cake crowd.
The Bad Food Tax
There’s an interesting op-ed article in today’s New York Times. The author, Mark Bittman, is proposing that we tax bad foods to the point where people will naturally select healthy food because they will be subsidized and cheap.
He has lots of statistics to back him up, and if you’re someone like me who reads this kind of thing widely, nothing surprised me. Of course Americans eat crappy food and it’s terrible for our bodies. We know that, it’s old news.
And we all want to know how to fix this- clearly education about nutrition isn’t doing the trick by itself. And I’m the first person who would love to use quantitative methods to solve a really important, big problem. Moreover, if we start to get rid of the evil farm subsidies that are currently creating a ridiculous market for corn sugar (a major reason we have some much soda on the shelves at such low prices to begin with) as well as screwing up the farmers in Africa and other places, that will be a good thing.
Unfortunately, I really think his tax plan stinks. The main problem is something he actually brings up and dismisses- namely:
Some advocates for the poor say taxes like these are unfair because low-income people pay a higher percentage of their income for food and would find it more difficult to buy soda or junk. But since poor people suffer disproportionately from the cost of high-quality, fresh foods, subsidizing those foods would be particularly beneficial to them.
Yes they would, if they could actually buy them in their neighborhood! If he has the idea that the reason poor people buy crappy food is because they go into their neighborhood grocery store with a museum-like display of fresh fruits and vegetables, bypass those foods (because they are too expensive) to go straight to the back and find junk, then I guess his plan would make sense. Unfortunately the truth is, there is no fresh fruit at most of the food stores in poor urban areas – they are typically small and carry long-lasting packaged goods and groceries, from canned evaporated milk to diapers, and don’t have extra space. Moreover, I don’t think a pure price comparison is going to convince them to carry fruit, because it’s not just the higher prices that makes bodegas carry no fruit- it’s also the convenience of packages that don’t go bad. In fact it’s an entirely different business model, which is unfortunately a pretty tough nut to crack, but is essential in this discussion.
In other words, the result of this tax plan would be, for poor people, even higher prices for crappy food, not access to fresh cheap food. Unless the plan has worked out a system for how to get fresh fruit into poor areas, it really is missing the very audience it wishes to target.
Happiest being sad
I’m done with math camp, and I am stopping off in Harvard Square on the way home to New York. I collected my two older sons from their first stint at overnight camp yesterday evening, a two-week middle-of-the-woods experience complete with a cold lake, dirty socks and sticky bunk beds. They were actually happy to see me, I could tell by the way they let me hug them in front of other people. I cried when I realized they had each grown two inches.
The past few days have been incredibly emotional. Somehow I started to pine for the program and for the students at the program before it had ended, and now I seem to miss my kids even though I have them back. I’m a mess of yearning, for a million things at once, and it seems like I’ve set myself up for this.
Of course when I think about it I absolutely have, and I guess the only real question is why I’m surprised. I keep falling in love with people and experiences that often even love me back, and even though I’m an experienced piner it doesn’t get any less painful. And yet it seems like the only alternative, if it is a choice I could even make, would be to close myself off from that openness and compassion and live in a careless void. That is certainly more terrifying to me than the safety of wistful suffering.
My friend Moon came to the program a couple of nights ago and gave a kick-ass talk to the students about the Banach-Tarski paradox. She stuck around that night for dinner and asked the program director, who has been doing this for 40 years, whether I had ever been shy. The director said, “No, Cathy was never shy, but she was memorable for the fact that she always said the same thing whenever someone started a conversation with her.” I had no idea what that could have been, and to tell you the truth I was a little worried what he’d say. So Moon asked, and he said the phrase was, “I love math!” It brought back a clear memory of the passion I had then and still have, and hopefully will always have. I am happy to be this sad.
What tensor products taught me about living my life
When I was a junior in college, I went to the Budapest Semesters in Math. I got really bummed while I was there, and I was thinking of leaving math, when a friend of mine back home sent me Silverman and Tate’s book on elliptic curves. That book restored my faith in math and I decided to become a number theorist. I went back to Berkeley and enrolled in Hendrik Lenstra’s Class Field Theory class, which was the second semester of a grad number theory class, and in Ken Ribet’s second semester grad algebra class. Since I’d missed the first semester of each, I pretty much got my ass kicked. I lived and breathed algebra and p-adics and local-glocal principles for the next three months. It was pretty awesome and incredibly challenging. The moment of my biggest frustration happened when we learned about tensor products over arbitrary rings with zero divisors.
I kept trying to understand these rings, and in particular the elements of these rings. I wasn’t asking much: I just wanted to figure out the most basic properties of tensor products. And it seemed like a moral issue. I felt strongly that if I really really wanted to feel like I understand this ring, which is after all a set, then at least I should be able to tell you, with moral authority, whether an element is zero or not. For fuck’s sake!
I couldn’t do it. In fact all the proofs I came up with involved the universal property of tensor products, never the elements themselves. It was incredibly unsatisfying, it was like I could only describe the outside of an alien world instead of getting to know its inhabitants.
After a few months, though, I realized something. I hadn’t gotten any better at understanding tensor products, but I was getting used to not understanding them. It was pretty amazing. I no longer felt anguished when tensor products came up; I was instead almost amused by their cunning ways.
Every now and then something like that happens in my life. Something that I start out desperately wanting to understand, to analyze, and to own. It’s practically a moral imperative! And I consider myself a person who gets stuff done! How can I let this lie unexplained?
Then after a few days it turns out, no, I still don’t understand it, but it actually makes me like it more. In fact now I look forward to things like that; little puzzles of human existence, where, for perhaps small examples (like when you work over a field) you can understand the issue entirely, but overall you realize it’s harder than that, and moreover you shouldn’t kill yourself over it. You can remain content maybe knowing how to describe some of its properties, while allowing it to maintain its secrets, because life is actually more interesting that way.
mathbabe 