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Systematized racism in online advertising, part 1
There is no regulation of how internet ad models are built. That means that quants can use any information they want, usually historical, to decide what to expect in the future. That includes associating arrests with african-american sounding names.
In a recent Reuters article, this practice was highlighted:
Instantcheckmate.com, which labels itself the “Internet’s leading authority on background checks,” placed both ads. A statistical analysis of the company’s advertising has found it has disproportionately used ad copy including the word “arrested” for black-identifying names, even when a person has no arrest record.
Luckily, Professor Sweeney, a Harvard University professor of government with a doctorate in computer science, is on the case:
According to preliminary findings of Professor Sweeney’s research, searches of names assigned primarily to black babies, such as Tyrone, Darnell, Ebony and Latisha, generated “arrest” in the instantcheckmate.com ad copy between 75 percent and 96 percent of the time. Names assigned at birth primarily to whites, such as Geoffrey, Brett, Kristen and Anne, led to more neutral copy, with the word “arrest” appearing between zero and 9 percent of the time.
Of course when I say there’s no regulation, that’s an exaggeration. There is some, and if you claim to be giving a credit report, then regulations really do exist. But as for the above, here’s what regulators have to say:
“It’s disturbing,” Julie Brill, an FTC commissioner, said of Instant Checkmate’s advertising. “I don’t know if it’s illegal … It’s something that we’d need to study to see if any enforcement action is needed.”
Let’s be clear: this is just the beginning.
The ABC Conjecture has not been proved
As I’ve blogged about before, proof is a social construct: it does not constitute a proof if I’ve convinced only myself that something is true. It only constitutes a proof if I can readily convince my audience, i.e. other mathematicians, that something is true. Moreover, if I claim to have proved something, it is my responsibility to convince others I’ve done so; it’s not their responsibility to try to understand it (although it would be very nice of them to try).
A few months ago, in August 2012, Shinichi Mochizuki claimed he had a proof of the ABC Conjecture:
For every 
The manuscript he wrote with the supposed proof of the ABC Conjecture is sprawling. Specifically, he wrote three papers to “set up” the proof and then the ultimate proof goes in a fourth. But even those four papers rely on various other papers he wrote, many of which haven’t been peer-reviewed.
The last four papers (see the end of the list here) are about 500 pages altogether, and the other papers put together are thousands of pages.
The issue here is that nobody understands what he’s talking about, even people who really care and are trying, and his write-ups don’t help.
For your benefit, here’s an excerpt from the very beginning of the fourth and final paper:
The present paper forms the fourth and final paper in a series of papers concerning “inter-universal Teichmuller theory”. In the first three papers of the series, we introduced and studied the theory surrounding the log-theta-lattice, a highly non-commutative two-dimensional diagram of “miniature models of conventional scheme theory”, called Θ±ell NF-Hodge theaters, that were associated, in the first paper of the series, to certain data, called initial Θ-data. This data includes an elliptic curve EF over a number field F , together with a prime number l ≥ 5. Consideration of various properties of the log-theta-lattice led naturally to the establishment, in the third paper of the series, of multiradial algorithms for constructing “splitting monoids of LGP-monoids”.
If you look at the terminology in the above paragraph, you will find many examples of mathematical objects that nobody has ever heard of: he introduces them in his tiny Mochizuki universe with one inhabitant.
When Wiles proved Fermat’s Last Theorem, he announced it to the mathematical community, and held a series of lectures at Cambridge. When he discovered a hole, he enlisted his former student, Richard Taylor, in helping him fill it, which they did. Then they explained the newer version to the world. They understood that it was new and hard and required explanation.
When Perelman proved the Poincare Conjecture, it was a bit tougher. He is a very weird guy, and he’d worked alone and really only written an outline. But he had used a well-known method, following Richard Hamilton, and he was available to answer questions from generous, hard-working experts. Ultimately, after a few months, this ended up working out as a proof.
I’m not saying Mochizuki will never prove the ABC Conjecture.
But he hasn’t yet, even if the stuff in his manuscript is correct. In order for it to be a proof, someone, preferably the entire community of experts who try, should understand it, and he should be the one explaining it. So far he hasn’t even been able to explain what the new idea is (although he did somehow fix a mistake at the prime 2, which is a good sign, maybe).
Let me say it this way. If Mochizuki died today, or stopped doing math for whatever reason, perhaps Grothendieck-style, hiding in the woods somewhere in Southern France and living off berries, and if someone (M) came along and read through all 6,000 pages of his manuscripts to understand what he was thinking, and then rewrote them in a way that uses normal language and is understandable to the expert number theorist, then I would claim that new person, M, should be given just as much credit for the proof as Mochizuki. It would be, by all rights, called the “Mochizuki and M Theorem”.
Come to think of it, whoever ends up interpreting this to the world will be responsible for the actual proof and should be given credit along with Mochizuki. It’s only fair, and it’s also the only thing that I can imagine would incentivize someone to do such a colossal task.
Update 5/13/13: I’ve closed comments on this post. I was getting annoyed with hostile comments. If you don’t agree with me feel free to start your own blog.
Medical research needs an independent modeling panel
I am outraged this morning.
I spent yesterday morning writing up David Madigan’s lecture to us in the Columbia Data Science class, and I can hardly handle what he explained to us: the entire field of epidemiological research is ad hoc.
This means that people are taking medication or undergoing treatments that may do they harm and probably cost too much because the researchers’ methods are careless and random.
Of course, sometimes this is intentional manipulation (see my previous post on Vioxx, also from an eye-opening lecture by Madigan). But for the most part it’s not. More likely it’s mostly caused by the human weakness for believing in something because it’s standard practice.
In some sense we knew this already. How many times have we read something about what to do for our health, and then a few years later read the opposite? That’s a bad sign.
And although the ethics are the main thing here, the money is a huge issue. It required $25 million dollars for Madigan and his colleagues to implement the study on how good our current methods are at detecting things we already know. Turns out they are not good at this – even the best methods, which we have no reason to believe are being used, are only okay.
Okay, $25 million dollars is a lot, but then again there are literally billions of dollars being put into the medical trials and research as a whole, so you might think that the “due diligence” of such a large industry would naturally get funded regularly with such sums.
But you’d be wrong. Because there’s no due diligence for this industry, not in a real sense. There’s the FDA, but they are simply not up to the task.
One article I linked to yesterday from the Stanford Alumni Magazine, which talked about the work of John Ioannidis (I blogged about his work here called “Why Most Published Research Findings Are False“), summed the situation up perfectly (emphasis mine):
When it comes to the public’s exposure to biomedical research findings, another frustration for Ioannidis is that “there is nobody whose job it is to frame this correctly.” Journalists pursue stories about cures and progress—or scandals—but they aren’t likely to diligently explain the fine points of clinical trial bias and why a first splashy result may not hold up. Ioannidis believes that mistakes and tough going are at the essence of science. “In science we always start with the possibility that we can be wrong. If we don’t start there, we are just dogmatizing.”
It’s all about conflict of interest, people. The researchers don’t want their methods examined, the pharmaceutical companies are happy to have various ways to prove a new drug “effective”, and the FDA is clueless.
Another reason for an AMS panel to investigate public math models. If this isn’t in the public’s interest I don’t know what is.
The definitive visualization for Hurricane Sandy, if you’re a parent of small children
Two small quibbles: it should be centered a much larger area, and “wine” should be replaced by “vodka”.
An AMS panel to examine public math models?
On Saturday I gave a talk at the AGNES conference to a room full of algebraic geometers. After introducing myself and putting some context around my talk, I focused on a few models:
- VaR,
- VAM,
- Credit scoring,
- E-scores (online version of credit scores), and
- The h-score model (I threw this in for the math people and because it’s an egregious example of a gameable model).
I wanted to formalize the important and salient properties of a model, and I came up with this list:
- Name – note the name often gives off a whiff of political manipulation by itself
- Underlying model – regression? decision tree?
- Underlying assumptions – normal distribution of market returns?
- Input/output – dirty data?
- Purported/political goal – how is it actually used vs. how its advocates claim they’ll use it?
- Evaluation method – every model should come with one. Not every model does. A red flag.
- Gaming potential – how does being modeled cause people to act differently?
- Reach – how universal and impactful is the model and its gaming?
In the case of VAM, it doesn’t have an evaluation method. There’s been no way for teachers to know if the model that they get scored on every year is doing a good job, even as it’s become more and more important in tenure decisions (the Chicago strike was largely related to this issue, as I posted here).
Here was my plea to the mathematical audience: this is being done in the name of mathematics. The authority that math is given by our culture, which is enormous and possibly not deserved, is being manipulated by people with vested interests.
So when the objects of modeling, the people and the teachers who get these scores, ask how those scores were derived, they’re often told “it’s math and you wouldn’t understand it.”
That’s outrageous, and mathematicians shouldn’t stand for it. We have to get more involved, as a community, with how mathematics is wielded on the population.
On the other hand, I wouldn’t want mathematicians as a group to get co-opted by these special interest groups either and become shills for the industry. We don’t want to become economists, paid by this campaign or that to write papers in favor of their political goals.
To this end, someone in the audience suggested the AMS might want to publish a book of ethics for mathematicians akin to the ethical guidelines that are published for the society of pyschologists and lawyers. His idea is that it would be case-study based, which seems pretty standard. I want to give this some more thought.
We want to make ourselves available to understand high impact, public facing models to ensure they are sound mathematically, have reasonable and transparent evaluation methods, and are very high quality in terms of proven accuracy and understandability if they are used on people in high stakes situations like tenure.
One suggestion someone in the audience came up with is to have a mathematician “mechanical turk” service where people could send questions to a group of faceless mathematicians. Although I think it’s an intriguing idea, I’m not sure it would work here. The point is to investigate so-called math models that people would rather no mathematician laid their eyes on, whereas mechanical turks only answer questions someone else comes up with.
In other words, there’s a reason nobody has asked the opinion of the mathematical community on VAM. They are using the authority of mathematics without permission.
Instead, I think the math community should form something like a panel, maybe housed inside the American Mathematical Society (AMS), that trolls for models with the following characteristics:
- high impact – people care about these scores for whatever reason
- large reach – city-wide or national
- claiming to be mathematical – so the opinion of the mathematical community matters, or should,
After finding such a model, the panel should publish a thoughtful, third-party analysis of its underlying mathematical soundness. Even just one per year would have a meaningful effect if the models were chosen well.
As I said to someone in the audience (which was amazingly receptive and open to my message), it really wouldn’t take very long for a mathematician to understand these models well enough to have an opinion on them, especially if you compare it to how long it would take a policy maker to understand the math. Maybe a week, with the guidance of someone who is an expert in modeling.
So in other words, being a member of such a “public math models” panel could be seen as a community service job akin to being an editor for a journal: real work but not something that takes over your life.
Now’s the time to do this, considering the explosion of models on everything in sight, and I believe mathematicians are the right people to take it on, considering they know how to admit they’re wrong.
Tell me what you think.
We’re not just predicting the future, we’re causing the future
My friend Rachel Schutt, a statistician at Google who is teaching the Columbia Data Science course this semester that I’ve been blogging every Thursday morning, recently wrote a blog post about 10 important issues in data science, and one of them is the title of my post today.
This idea that our predictive models cause the future is part of the modeling feedback loop I blogged about here; it’s the idea that, once we’ve chosen a model, especially as it models human behavior (which includes the financial markets), then people immediately start gaming the model in one way or another, both weakening the effect that the model is predicting as well as distorting the system itself. This is important and often overlooked when people build models.
How do we get people to think about these things more carefully? I think it would help to have a checklist of properties of a model using best practices.
I got this idea recently as I’ve been writing a talk about how math is used outside academia (which you guys have helped me on). In it, I’m giving a bunch of examples of models with a few basic properties of well-designed models.
It was interesting just composing that checklist, and I’ll likely blog about this in the next few days, but needless to say one thing on the checklist was “evaluation method”.
Obvious point: if you have a model which has no well-defined evaluation model then you’re fucked. In fact, I’d argue, you don’t really even have a model until you’ve chosen and defended your evaluation method (I’m talking to you, value-added teacher modelers).
But what I now realize is that part of the evaluation method of the model should consist of an analysis of how the model can or will be gamed and how that gaming can or will distort the ambient system. It’s a meta-evaluation of the model, if you will.
Example: as soon as regulators agree to measure a firm’s risk with 95% VaR on a 0.97 decay factor, there’s all sorts of ways for companies to hide risk. That’s why the parameters (95, 0.97) cannot be fixed if we want a reasonable assessment of risk.
This is obvious to most people upon reflection, but it’s not systemically studied, because it’s not required as part of an evaluation method for VaR. Indeed a reasonable evaluation method for VaR is to ask whether the 95% loss is indeed breached only 5% of the time, but that clearly doesn’t tell the whole story.
One easy way to get around this is to require a whole range of parameters for % VaR as well as a whole range of decay factors. It’s not that much more work and it is much harder to game. In other words, it’s a robustness measurement for the model.
Philanthropy can do better than Rajat Gupta
Last night I was watching a YouTube video in between playoff games (both of which disappointed). Conan O’Brien was accepting an honorary patronage at the philosophical society of the University of Dublin. His speech was hilarious, and there was an extended, intimate Q&A session afterwards.
One thing he mentioned was an amended version of the (to me, very moving) words he had closed his last NBC Tonight Show with, “If you work really hard and you’re kind then amazing things will happen.” Namely, he wanted to add this sentence: “If you work really hard and you’re a huge asshole, then you can make tons of money on Wall Street.”
These wise words came back to me this morning when I read about Bill Gates and Kofi Annan’s letters to Judge Jed Rakoff regarding Goldman Sachs insider trader Rajat Gupta. The letters were intended to reduce sentencing, considering how unbelievably philanthropical Gupta had been as he was stealing all this money.
I’m not doubting that the dude did some good things with his ill-gotten gains. After all, I don’t have a letter from Bill Gates about how I helped remove malaria from the world.
But wait a minute, maybe that’s because I didn’t steal money from taxpayers like he did to put myself into the position of spending millions of dollars doing good things! Because I’m thinking that if I had the money that Gupta had, I might well have spent good money doing good things.
And therein lies the problem with this whole picture. He did some good (I’ll assume), but then again he had the advantage of being someone in our society who could do good, i.e. he was loaded. Wouldn’t it make more sense for us to set up a system wherein people could do good who are good, who have good ideas and great plans?
Unfortunately, those people exist, but they’re generally poor, or stuck in normal jobs making ends meet for their family, and they don’t get their plans heard. In particular they aren’t huge assholes stealing money and then trying to get out of trouble by hiring hugely expensive lawyers and leaning on their philanthropy buds.
The current system of grant-writing doesn’t at all support the people with good ideas: it doesn’t teach these “social inventors” how to build a charitable idea into a business plan. So what happens is that the good ideas drift away without the important detailed knowledge of how to surround it with resources. And generally the people with really innovative ideas aren’t by nature detail-oriented people who can figure out how to start a business, they’re kind of nerdy.
I’m serious, I think the government should sponsor something like a “philanthropy institute” for entrepreneurial non-revenue generating ideas that are good for society. People could come to open meetings and discuss their ideas for improving stuff, and there’d be full-time staff and fellows, with the goal of seizing upon good ideas and developing them like business plans.
Dissolve the SEC
A few days ago I wrote about the $5 million fine the SEC gave to NYSE for allowing certain customers prices before other customers. I was baffled that the fine is so low- access like that allows the customers to make outrageous profits, and it seems like the resulting fine should be more along the lines of those profits, since kickbacks are probably in terms of percentages of take. The lawyer fees from this case on both sides is much higher than $5 million, for christ’s sakes.
But now I’m even more outraged by the newest smallest fine, this time an $800,000 fine for a dark pool trading firm eBX. From the Boston.com article:
Federal securities regulators on Wednesday charged Boston-based eBX LLC, a “dark pool” securities exchange, with failing to protect confidential trading information of customers and for failing to disclose that it let an outside firm use their trading data.
The Securities and Exchange Commission said eBX, which runs the alternative trading system LeveL ATS, agreed to settle the charges and to pay an $800,000 penalty.
You know that if I can actually consider paying the fine myself, then the fine is too small. It’s along the lines of the cost of college for my kids.
Look, I don’t care what it’s for: if the SEC finds you guilty of fraud, it should threaten to put you out of business. Otherwise why should they waste their time doing it?
On the one hand, I’m outraged that these fraudulent practices are being so lightly punished. Indeed it’s worse than no punishment at all to get such a light punishment, because it establishes precedent. Now exchanges know how much it costs to let certain traders get better access to data than others, and as long as they charge sufficiently, they’ll be sure to make profit on it. Similarly dark trading pools know how much to charge third-party data vendors for their clients’ “confidential trading information.” Awesome.
On the other hand, I’m outraged at the SEC for not picking their fights better and for general incompetence. Here they are nabbing firms for real fraud, and they can’t get more than $800,000? At the same time, they’ve decided to go into high frequency trading but what that seems to mean to them is that they’ll finally collect some tick data. I’ve got some news for them: it’s gonna take more than a little bit of data to understand that world.
The SEC needs to concentrate more on not trying to keep up with the HFT’ers of the world, since it’s a lost cause, and spend more time thinking through what policy changes they’d need to actually do their job well – for example, what would they need to get Citigroup and Bank of America to admit wrongdoing when they defraud their customers? Instead of wasting their time trying to keep up with HFT quants, what would they need to institute a transaction tax, or some other policy to slow down trading? What would they need to be able to shut down firms who sell confidential client trading information?
The SEC needs to write a list of policy demands, pronto.
And if the political pressure the SEC receives to not actually get anyone in trouble is too strong for them to do their job well, they should either quit in protest or make a huge stink about being kept from completing their mission.
I get it, I’ve talked to people inside the SEC who want to do a better job but feel like they aren’t being given the power to. But I say, enough with the resigned shrugs already, this stuff is out of control! Continuing in this way is giving the public the false impression that there’s someone on the case. Well, there’s someone on the case, all right, but they aren’t being allowed to or don’t see the point of doing their work. It’s bullshit.
I say dissolve the SEC so that people will no longer have any false hopes of meaningful financial reform.
I’ve been reading Sheila Bair’s book Bull by the Horns, and it’s really good. Maybe by the end of it I’ll have changed my mind and I’ll see a place for the SEC. Maybe I’ll have hope that these things have natural cycles and the SEC will have another day in the power position, like it had in the 1980’s. But right now I’m in the part of the book where the regulators, apart from the FDIC, are taking orders directly from financial lobbyists, and it makes me completely crazy.
High frequency trading: how it happened, what’s wrong with it, and what we should do
High frequency trading (HFT) is in the news. Politicians and regulators are thinking of doing something to slow stuff down. The problem is, it’s really complicated to understand it in depth and to add rules in a nuanced way. Instead we have to do something pretty simple and stupid if we want to do anything.
How it happened
In some ways HFT is the inevitable consequence of market forces – one has an advantage when one makes a good decision more quickly, so there was always going to be some pressure to speed up trading, to get that technological edge on the competition.
But there was something more at work here too. The NYSE exchange used to be a non-profit mutual, co-owned by every broker who worked there. When it transformed to a profit-seeking enterprise, and when other exchanges popped up in competition with it was the beginning of the age of HFT.
All of a sudden, to make an extra buck, it made sense to allow someone to be closer and have better access, for a hefty fee. And there was competition among the various exchanges for that excellent access. Eventually this market for exchange access culminated in the concept of co-location, whereby trading firms were allowed to put their trading algorithms on servers in the same room as the servers that executed the trades. This avoids those pesky speed-of-light issues when sitting across the street from the executing servers.
Not surprisingly, this has allowed the execution of trades to get into the mind-splittingly small timeframe of double-digit microseconds. That’s microseconds, where from wikipedia: “One microsecond is to one second as one second is to 11.54 days.”
What’s wrong with it
Turns out, when things get this fast, sometimes mistakes happen. Sometimes errors occur. I’m writing in the third-person passive voice because we are no longer talking directly about human involvement, or even, typically, a single algorithm, but rather the combination of a sea of algorithms which together can do unexpected things.
People know about the so-called “flash crash” and more recently Knight Capital’s trading debacle where an algorithm at opening bell went crazy with orders. But people on the inside, if you point out these events, might counter that “normal people didn’t lose money” at these events. The weirdness was mostly fixed after the fact, and anyway pension funds, which is where most normal people’s money lives, don’t ever trade in the thin opening bell market.
But there’s another, less well known example from September 30th, 2008, when the House rejected the bailout, shorting stocks were illegal, and the Dow dropped 778 points. The prices as such common big-ticket stocks such as Google plummeted and, in this case, pension funds lost big money. It’s true that some transactions were later nulled, but not all of them.
This happened because the market makers of the time had largely pulled their models out of the market after shorting became illegal – there was no “do this algorithm except make sure you’re never short” button on the algorithm, so once the rule was called, the traders could only turn it all of completely. As a result, the liquidity wasn’t there and the pension funds, thinking they were being smart to do their big trades at close, instead got completely walloped.
Keep this in mind, before you go blaming the politicians on this one because the immediate cause was the short-sighted short-selling ban: the HFT firms regularly pull out of the market in times of stress, or when they’re updating their algorithms, or just whenever they want. In other words, it’s liquidity when you need it least.
Moreover, just because two out of three times were relatively benign for the 99%, we should not conclude that there’s nothing potentially disastrous going on. The flash crash and Knight Capital have had impact, namely they serve as events which erode our trust in the system as a whole. The 2008 episode on top of that proved that yes, we can be the victims of the out-of-control machines fighting against each other.
Quite aside from the instability of the system, and how regular people get screwed by insiders (because after all, that’s not a new story at all, it’s just a new technology for an old story), let’s talk about resources. How much money and resources are being put into the HFT arena and how could those resources otherwise be used?
Putting aside the actual energy consumed by the industry, which is certainly non-trivial, let’s focus for a moment on money. It has been estimated that overall, HFT firms post about $80 billion in profits yearly, and that they make on the order of 10% profit on their technology investments. That would mean that there’s in the order of $800 billion being invested in HFT each year. Even if we highball the return at 25%, we still have more than $300 billion invested in this stuff.
And to what end?
Is that how much it’s really worth the small investor to have decreased bid-ask spreads when they go long Apple because they think the new iPhone will sell? What else could we be doing with $800 billion dollars? A couple of years of this could sell off all of the student debt in this country.
What should be done
Germany has recently announced a half-second minimum for posting an share order. This is eons in current time frames, and would drastically change how trading is done. They also want HFT algorithms to be registered with them. You know, so people can keep tabs on the algorithms and understand what they’re doing and how they might interact with each other.
Um, what? As a former quant, let me just say: this will not work. Not a chance in hell. If I want to obfuscate the actual goals of a model I’ve written, that’s easier than actually explaining it. Moreover, the half-second rule may sound good but it just means it’s a harder system to game, not that it won’t be gameable.
Other ideas have been brought forth as to how to slow down trading, but in the end it’s really hard to do: if you put in delays, there’s always going to be an algorithm employed which decides whose trade actually happens first, and so there will always be some advantage to speed, or to gaming the algorithm. It would be interesting but academically challenging to come up with a simple enough rule that would actually discourage people from engaging in technological warfare.
The only sure-fire way to make people think harder about trading so quickly and so often is a simple tax on transactions, often referred to as a Tobin Tax. This would make people have sufficient amount of faith in their trade to pay the tax on top of the expected value of the trade.
And we can’t just implement such a tax on one market, like they do for equities in London. It has to be on all exhange-traded markets, and moreover all reasonable markets should be exchange-traded.
Oh, and while I’m smoking crack, let me also say that when exchanges are found to have given certain of their customers better access to prices, the punishments for such illegal insider information should be more than $5 million dollars.
Telling people to leave finance
I used to work in finance, and now I don’t. I haven’t regretted leaving for a moment, even when I’ve been unemployed and confused about what to do next.
Lots of my friends that I made in finance are still there, though, and a majority of them are miserable. They feel trapped and they feel like they have few options. And they’re addicted to the cash flow and often have families to support, or a way of life.
It helps that my husband has a steady job, but it’s not only that I’m married to a man with tenure that I’m different. First, we have three kids so I actually do have to work, and second, there are opportunities to leave that these people just don’t consider.
First, I want to say it’s frustrating how risk-averse the culture in finance is. I know, it’s strange to hear that, but compared to working in a start-up, I found the culture and people in finance to be way more risk-averse in the sense of personal risk, not in the sense of “putting other people’s money at risk”.
People in start-ups are optimistic about the future, ready for the big pay-out that may never come, whereas the people in finance are ready for the world to melt down and are trying to collect enough food before it happens. I don’t know which is more accurate but it’s definitely more fun to be around optimists. Young people get old quickly in finance.
Second the money is just crazy. People seriously get caught up in a world where they can’t see themselves accepting less than $400K per year. I don’t think they could wean themselves off the finance teat unless the milk dried up.
So I was interested in this article from Reuters which was focused on lowering bankers’ bonuses and telling people to leave if they aren’t happy about it.
On the one hand, as a commenter points out, giving out smaller bonuses won’t magically fix the banks- they are taking massive risks, at least at the too-big-to-fail banks, because there is no personal risk to themselves, and the taxpayer has their back. On the other hand, if we take away the incentive to take huge risks, then I do think we’d see way less of it.
Just as a thought experiment, what would happen if the bonuses at banks really went way down? Let’s say nobody earns more than $250K, just as a stab in the arm of reality.
First, some people would leave for the few places that are willing to pay a lot more, so hedge funds and other small players with big money. To some extent this has already been happening.
Second, some people would just stay in a much-less-exciting job. Actually, there are plenty of people who have boring jobs already in these banks, and who don’t make huge money, so it wouldn’t be different for them.
Finally, a bunch of people would leave finance and find something else to do. Their drug dealer of choice would be gone. After some weeks or months of detox and withdrawal, they’d learn to translate their salesmanship and computer skills into other industries.
I’m not too worried that they’d not find jobs, because these men and women are generally very smart and competent. In fact, some of them are downright brilliant and might go on to help solve some important problems or build important technology. There’s like an army of engineers in finance that could be putting their skills to use with actual innovation rather than so-called financial innovation.
Two rants about hiring a data scientist
I had a great time yesterday handing out #OWS Alternative Banking playing cards to press, police, and protesters all over downtown Manhattan, and I’m planning to write a follow-up post soon about whether Occupy is or is not dead and whether we do or do not wish it to be and for what reason (spoiler alert: I wish it were because I wish all the problems Occupy seeks to address had been solved).
But today I’m taking a break to do some good and quick, old-fashioned venting.
——-
First rant: I hate it when I hear business owners say they want to hire data scientists but only if they know SQL, because for whatever reason they aren’t serious if they don’t learn something as important as that.
That’s hogwash!
If I’m working at a company that has a Hive, why would I bother learning SQL? Especially if I’ve presumably got some quantitative chops and can learn something like SQL in a matter of days. It would be a waste of my time to do it in advance of actually using it.
I think people get on this pedestal because:
- It’s hard for them to learn SQL so they assume it’s hard for other smart people. False.
- They have only worked in environments where a SQL database was the main way to get data. No longer true.
By the way, you can replace “SQL” above with any programming language, although SQL seems to be the most common one where people hold it against you with some kind of high and mighty attitude.
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Second rant: I hate it when I hear data scientists dismiss domain expertise as unimportant. They act like they’re such good data miners that they’ll find out anything the domain experts knew and then some within hours, i.e. in less time than it would take to talk to a domain expertise carefully about their knowledge.
That’s dumb!
If you’re not listening well, then you’re missing out on the best signals of all. Get over your misanthropic, aspy self and do a careful interview. Pay attention to what happens over time and why and how long effects take and signals that they have begun or ended. You will then have a menu of signals to check and you can start with them and move on to variations of them as appropriate.
If you ignore domain expertise, you are just going to overfit weird noisy signals to your model in addition to finding a few real ones and ignoring others that are very important but unintuitive (to you).
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I wanted to balance my rants so I don’t appear anti-business or anti-data scientist. What they have in common is understanding the world a little bit from the other person’s point of view, taking that other view seriously, and giving respect where it’s due.
Why are the Chicago public school teachers on strike?
The issues of pay and testing
My friend and fellow HCSSiM 2012 staff member P.J. Karafiol explains some important issues in a Chicago Sun Times column entitled “Hard facts behind union, board dispute.”
P.J. is a Chicago public school math teacher, he has two kids in the CPS system, and he’s a graduate from that system. So I think he is qualified to speak on the issues.
He first explains that CPS teachers are paid less than those in the suburbs. This means, among other things, that it’s hard to keep good teachers. Next, he explains that, although it is difficult to argue against merit pay, the value-added models that Rahm Emanuel wants to account for half of teachers evaluation, is deeply flawed.
He then points out that, even if you trust the models, the number of teachers the model purports to identify as bad is so high that taking action on that result by firing them all would cause a huge problem – there’s a certain natural rate of finding and hiring good replacement teachers in the best of times, and these are not the best of times.
He concludes with this:
Teachers in Chicago are paid well initially, but face rising financial incentives to move to the suburbs as they gain experience and proficiency. No currently-existing “value added” evaluation system yields consistent, fair, educationally sound results. And firing bad teachers won’t magically create better ones to take their jobs.
To make progress on these issues, we have to figure out a way to make teaching in the city economically viable over the long-term; to evaluate teachers in a way that is consistent and reasonable, and that makes good sense educationally; and to help struggling teachers improve their practice. Because at base, we all want the same thing: classes full of students eager to be learning from their excellent, passionate teachers.
Test anxiety
Ultimately this crappy model, and the power that it yields, creates a culture of text anxiety for teachers and principals as well as for students. As Eric Zorn (grandson of mathematician Max Zorn) writes in the Chicago Tribune (h/t P.J. Karafiol):
The question: But why are so many presumptively good teachers also afraid? Why has the role of testing in teacher evaluations been a major sticking point in the public schools strike in Chicago?
The short answer: Because student test scores provide unreliable and erratic measurements of teacher quality. Because studies show that from subject to subject and from year to year, the same teacher can look alternately like a golden apple and a rotting fig.
Zorn quotes extensively from Math for America President John Ewing’s article in Notices of the American Mathematical Society:
Analyses of (value-added model) results have led researchers to doubt whether the methodology can accurately identify more and less effective teachers. (Value-added model) estimates have proven to be unstable across statistical models, years and classes that teachers teach.
One study found that across five large urban districts, among teachers who were ranked in the top 20 percent of effectiveness in the first year, fewer than a third were in that top group the next year, and another third moved all the way down to the bottom 40 percent.
Another found that teachers’ effectiveness ratings in one year could only predict from 4 percent to 16 percent of the variation in such ratings in the following year.
The politics behind the test
I agree that the value-added model (VAM) is deeply flawed; I’ve blogged about it multiple times, for example here.
The way I see it, VAM is a prime example of the way that mathematics is used as a weapon against normal people – in this case, teachers, principals, and schools. If you don’t see my logic, ask yourself this:
Why would a overly-complex, unproved and very crappy model be so protected by politicians?
There’s really one reason, namely it serves a political function, not a mathematical one. And that political function is to maintain control over the union via a magical box that nobody completely understands (including the politicians, but it serves their purposes in spite of this) and therefore nobody can argue against.
This might seem ridiculous when you have examples like this one from the Washington Post (h/t Chris Wiggins), in which a devoted and beloved math teacher named Ashley received a ludicrously low VAM score.
I really like the article: it was written by Sean C. Feeney, Ashley’s principal at The Wheatley School in New York State and president of the Nassau County High School Principals’ Association. Feeney really tries to understand how the model works and how it uses data.
Feeney uncovers the crucial facts that, on the one hand nobody understands how VAM works at all, and that, on the other, the real reason it’s being used is for the political games being played behind the scenes (emphasis mine):
Officials at our State Education Department have certainly spent countless hours putting together guides explaining the scores. These documents describe what they call an objective teacher evaluation process that is based on student test scores, takes into account students’ prior performance, and arrives at a score that is able to measure teacher effectiveness. Along the way, the guides are careful to walk the reader through their explanations of Student Growth Percentiles (SGPs) and a teacher’s Mean Growth Percentile (MGP), impressing the reader with discussions and charts of confidence ranges and the need to be transparent about the data. It all seems so thoughtful and convincing! After all, how could such numbers fail to paint an accurate picture of a teacher’s effectiveness?
(One of the more audacious claims of this document is that the development of this evaluative model is the result of the collaborative efforts of the Regents Task Force on Teacher and Principal Effectiveness. Those of us who know people who served on this committee are well aware that the recommendations of the committee were either rejected or ignored by State Education officials.)
Feeney wasn’t supposed to do this. He wasn’t supposed to assume he was smart enough to understand the math behind the model. He wasn’t supposed to realize that these so-called “guides to explain the scores” actually represent the smoke being blown into the eyes of educators for the purposes of dismembering what’s left of the power of teachers’ unions in this country.
If he were better behaved, he would have bowed to the authority of the inscrutable, i.e. mathematics, and assume that his prize math teacher must have had flaws he, as her principal, just hadn’t seen before.
Weapons of Math Destruction
Politicans have created a WMD (Weapon of Math Destruction) in VAM; it’s the equivalent of owning an uzi factory when you’re fighting a war against people with pointy sticks.
It’s not the only WMD out there, but it’s a pretty powerful one, and it’s doing outrageous damage to our educational system.
If you don’t know what I mean by WMD, let me help out: one way to spot a WMD is to look at the name versus the underlying model and take note of discrepancies. VAM is a great example of this:
- The name “Value-Added Model” makes us think we might learn how much a teacher brings to the class above and beyond, say, rote memorization.
- In fact, if you look carefully you will see that the model is measuring exactly that: teaching to the test, but with errorbars so enormous that the noise almost completely obliterates any “teaching to the test” signal.
Nobody wants crappy teachers in the system, but vilifying well-meaning and hard-working professionals and subjecting them to random but high-stakes testing is not the solution, it’s pure old-fashioned scapegoating.
The political goal of the national VAM movement is clear: take control of education and make sure teachers know their place as the servants of the system, with no job security and no respect.
STEM jobs and the economy
STEM jobs
You know how we’re always hearing that not enough people major in science, technology, math, and engineering? The STEM subjects? That our country is losing pace in the competition with other countries for technology and such?
True and false. True that there are plenty of jobs for people with very strong skills in these areas. On the other hand we don’t want everyone to suddenly become a scientist/engineer/mathematician/computer nerd, because the truth is we don’t really have that many jobs. It’s not like the factory jobs of yesteryear or the agricultural jobs of yesteryesteryear.
Why? These jobs by nature are idiosyncratic and typically conclude with hugely scalable results. There’s only so many social media systems we need created, only so many air traffic control programs that need to work. After a while we might actually be done with some of this. An Detroit-sized army of engineers would not be the right tool for the job, actually, we wouldn’t know what to do with them.
So when you hear calls for more people like this, take it with a grain of salt. The truth is, they are rare now, will probably stay relatively rare, and the reason there’s so much emphasis on STEM professionals is this: having skills like that is a ticket to the elite. Let me explain why I say “elite”, which is a loaded term.
The Economy
There has been plenty of documentation of the following phenomenon: instead of lots of middle class job creation, we’ve been seeing technology-driven high-paying job creation, on the one hand, and a bunch of low-paying, person-to-person jobs like working in health care as home health aides on the other hand.
Be a nerd with me and extrapolate our current system out fifty years. What do you see happening?
Here’s what I see. Continued loss of classic middle-class jobs, continued efficiency gains with highly scaled industries run by a few super techno-savvy billionaire elites. Lots of people either jobless or working in the remaining jobs that can’t be done by computers or taken off-shore, mostly involving food and healthcare. Society has been hollowed out, once and for all.
I actually believe in this, and I don’t think it’s really avoidable. On the other hand, it could either end well or badly, depending on how we deal with it, and depending on what the standard of living is for people who have been edged out of a living by the enormous technological gains we’ve made.
Do they get well-paid for the work they do find? Do they have access to healthcare? Do they have to worry about feeding themselves and their kids? Do they get told by some hypocritical blowhard politician to man up and get a job when no jobs exist? Are they in irretrievably hopeless student debt?
Women, marriages, and the rat-race
There were two articles in the Economist a couple of issues ago which involved women. First, there was an article about marriage rates, saying they’re down all across the world, and showing this graph:
As an explanation, the Economist suggests some possibilities:
First, women are often marrying later as their professional opportunities improve. Second, thanks to increased longevity, bereaved spouses are outliving their partners for longer than the widows and widowers of yesteryear. And third, changing social attitudes in many countries mean that the payoffs of marriage—financial security, sexual relations, a stable relationship—can now often be found outside the nuptial bed.
Let’s call that last possibility the “payoff” reason for not getting married, and rephrase it like this: women are saying, I’d rather not, thanks.
The second Economist article talks about why women don’t rise to the top of companies. It gives us some numbers:
America’s biggest companies hire women to fill just over half of entry-level professional jobs. But those women fail to advance proportionally: they occupy only 28% of senior managerial posts, 14% of seats on executive committees and just 3% of chief-executive roles, according to McKinsey & Company, a consultancy.
Again, as explanation, the Economist suggests some possibilities:
Several factors hold women back at work. Too few study science, engineering, computing or maths. Too few push hard for promotion. Some old-fashioned sexism persists, even in hip, liberal industries. But the biggest obstacle (at least in most rich countries) is children.
Do you know what I’m not seeing? I’m not seeing the payoff reason listed. I’m not seeing the possibility that women decide I’d rather not, thanks.
Considering what we know about internal culture at places like McKinsey & Company and other consultancies, or finance firms, or technology firms, etc., I’m wondering why that wasn’t listed.
Remember, these are educated, smart women being hired at these companies. They have lots of options in general, so I’m not willing to to assume they are all just going home to take care of their kids once they leave their corporation. More likely, they’re leaving because they decide it’s just not going to be their best option.
And yeah, it is hard to have kids and work, but that’s not the only reason to leave a large corporation. Take for example the heroine of the article, Marissa Mayer, the new CEO of Yahoo! (emphasis mine):
Ms Mayer of Yahoo! is an inspiration to many, but a hard act to follow. She boasts of putting in 90-hour weeks at Google. She believes that “burn-out” is for wimps. She says that she will take two weeks’ maternity leave and work throughout it. If she can turn around the internet’s biggest basket case while dandling a newborn on her knee it will be the greatest triumph for working women since winning the right to wear trousers to the office (which did not happen until 1994 in California).
WTF?! She’s an inspiration to who, HR at her company? Who does that? She’s gotta be psychotic – but wait, that’s what’s selected for. I’d like to see another article come out where the Economist asks the question, Why are smart men willing to spend their lives in the quest of leading these companies, considering how awful the conditions are?
In any case, I personally would like to go on record saying Marissa Mayer is not a role model for me.
You know who is, though? This woman I met when she was 80, who had just learned to be a professional potter, and had had various totally fascinating careers before that, including as a ship-builder. She had five kids. She ran away with her current husband at 40. Since I met her she became a writer. My god, this woman is amazing.
Women, and some men, have the power to re-invent themselves, to become more and more interesting and creative as they grow older. That is, to me, inspiring. They are my role models. Keep learning! Keep exploring!
I’m not asking you to agree with me on what is inspiring, but I am asking the Economist to be consistent. If we can manage to believe that not all women see the point in getting married, then can’t we stretch ourselves, just a bit, and imagine that not all women can see the point in staying inside a corporate machine for their entire lives, slowly losing their identity and their ambition in the petty internal rat-races of the idiosyncratic culture of whatever firm they happen to belong to, just so, at the end, they can have too much money and not enough time? Sheesh.
Citigroup’s plutonomy memos
Maybe I’m the last person who’s hearing about the Citigroup “plutonomy memos”, but they’re blowning me away.
Wait, now that I look around, I see that Yves Smith at Naked Capitalism posted about this on October 15, 2009, almost three years ago, and called for people to protest the annual meetings of the American Bankers Association. Man, that’s awesome.
So yeah, I’m a bit late.
But just in case you didn’t hear about the plutonomy memos (h/t Nicholas Levis), which were featured on Michael Moore’s “Capitalism: a Love Story” as well, then you’ll have to read this post immediately and watch Bill Moyer’s clip at the end as well.
The basic story, if you’re still here, is that certain “global strategists” inside Citigroup drafted some advice about investing based on their observation that rich people have all the money and power. They even invented a new word for this, namely “plutonomy.” This excerpt from one of the three memos kind of sums it up:
We project that the plutonomies (the U.S., UK, and Canada) will likely see even more income inequality, disproportionately feeding off a further rise in the profit share in their economies, capitalist-friendly governments, more technology-driven productivity, and globalization… Since we think the plutonomy is here, is going to get stronger… It is a good time to switch out of stocks that sell to the masses and back to the plutonomy basket.
The lawyers for Citigroup keep trying to make people take down the memos, but they’re easy to find once you know to look for them. Just google it.
Nothing that surprising, economically speaking, except for maybe the fact that their reaction, far from being outrage, is something bordering on gleeful. But they aren’t totally complacent:
Low-end developed market labor might not have much economic power, but it does have equal voting power with the rich.
This equal voting power seems to be a pretty serious concern for their plans. They go on to say:
A third threat comes from the potential social backlash. To use Rawls-ian analysis, the invisible hand stops working. Perhaps one reason that societies allow plutonomy, is because enough of the electorate believe they have a chance of becoming a Pluto-participant. Why kill it off, if you can join it? In a sense this is the embodiment of the “American dream”. But if voters feel they cannot participate, they are more likely to divide up the wealth pie, rather than aspire to being truly rich.
Could the plutonomies die because the dream is dead, because enough of society does not believe they can participate? The answer is of course yes. But we suspect this is a threat more clearly felt during recessions, and periods of falling wealth, than when average citizens feel that they are better off. There are signs around the world that society is unhappy with plutonomy – judging by how tight electoral races are.
But as yet, there seems little political fight being born out on this battleground.
This explains to me why Occupy was treated the way it was by Bloomberg’s cops and the entrenched media like the New York Times (and nationally) – the idea that people are opting out and no longer believe they have a chance of being a Pluto-participant is essentially the most threatening thing they can think of. Interestingly, they also say this:
A related threat comes from the backlash to “Robber-barron” economies. The
population at large might still endorse the concept of plutonomy but feel they have lost out to unfair rules. In a sense, this backlash has been epitomized by the media coverage and actual prosecution of high-profile ex-CEOs who presided over financial misappropriation. This “backlash” seems to be something that comes with bull markets and their subsequent collapse. To this end, the cleaning up of business practice, by high-profile champions of fair play, might actually prolong plutonomy.
This is what Dodd-Frank has done, to some extent: a law that makes things seem like they’re getting better, or at least confuses people long enough so they lose their fighting spirit.
Finally, from the third memo:
➤ What could go wrong?
Beyond war, inflation, the end of the technology/productivity wave, and financial collapse, we think the most potent and short-term threat would be societies demanding a more ‘equitable’ share of wealth.
Note the perspective: what could go wrong. Lest we wonder who inititated class warfare.
NSA mathematicians
When I was a promising young mathematician in college, I met someone from the NSA who tried to recruit me to work for the spooks in the summer. Actually, “met someone” is misleading- he located me after I had won a prize.
I didn’t know what to think, so I accepted his invitation to visit the institute, which was in La Jolla, in Southern California (I went to UC Berkeley so it wasn’t a big trip).
When I got to the building, since I didn’t have clearance, everybody had to stop working the whole time I was there. It wasn’t enough to clean their whiteboards, one of them explained, they had to wash them down with that whiteboard spray stuff, because if you look at a just-erased whiteboard in a certain way you can decipher what had been written on it.
I met a bunch of people, maybe 6 or 7. They all told me how nice it was to work there, how the weather was beautiful, how the math problems were interesting. It was strangely consistent, but who knows, perhaps also true.
One thing I’d already learned before coming is that there are many layers of work that happen before the math people in La Jolla are given problems to do. First, the actual problem is chosen, then the “math” of the problem is extracted from the problem, and third it’s cleansed so that nobody can tell what the original application is.
Knowing this (and I was never contradicted when I explained that process), I asked each of them the same question: how do you feel about the fact that you don’t know what problem you’re actually solving?
Out of the 6 or 7 people I met, everyone but one person responded along the lines, “I believe everything the United States Government does is good.” The last guy said, “yeah, that bothers me. I am honestly seriously considering leaving.”
Needless to say, I didn’t take the job. I wasn’t yet a major league skeptic, but I was skeptical enough to realize I could not survive in such an environment, with colleagues that oblivious. They also mentioned that I’d have to stop dating my Czech boyfriend and that I’d need to submit information about all my roommates for the past 10 years, which was uber creepy.
Nowadays I hear estimates that 600 mathematicians work at the NSA, and of course many more stream through during the summer when school’s not in session, both at La Jolla and Princeton. Somehow they don’t mind not knowing how their work actually gets used. I’m not sure how that’s possible but it clearly is.
This mindset came back to me, and not in a good way, when I read this opinion piece and watched this video in the New York Times a couple of days ago.
William Binney, a mathematician, was working on Soviet Union spying software that got converted to domestic spying after 9/11. In other words, they used his foreign spying algorithm on a new data source, namely American citizen’s raw data. He objected to that, so strongly that he’s come out against it publicly.
The big surprise is how come they let him know what they were actually up to. My guess is he was high enough up the chain that they thought he’d be okay with it – he’d been there 32 years, and I guess he was considered an insider.
In any case, watch the video: this is a courageous man. The FBI came into his house with guns drawn to intimidate him against his whistleblowing activities and yet he hasn’t been cowed. Indeed, after getting dressed (he was coming out of the shower when they exploded into his house), he explained to them the crimes of George Bush and Dick Cheney on his back porch.
As he explains, “the purpose is to monitor what people are doing”. He explains how people’s social media data and other kinds of data are linked over domains and over time to build profiles of Americans over time: “you have 10 years of their life that you can lay out in a timeline, that involves anybody in the country”.
Describing the dangers of this program, Binney was extremely articulate:
- “The danger here is that we could fall into a totalitarian state like East Germany”
- “We can’t have secret interpretations of laws and run them in secret and not tell anybody. We can’t make up kill lists and not tell anybody what the criterion is for being on the kill list”
- “Just because we call ourselves a democracy doesn’t mean we will stay that way.”
There you have it. The good news is that that guy is no longer helping the NSA do their thing.
But the bad news is, plenty of mathematicians still are. And if you want to find a community more trusting and loyal than mathematicians, I think you’d have to go to a kindergarten somewhere. Not to mention the fact that, as I described above, the problems are intentionally cleaned to look innocuous.
Another example, possibly the most important one of all, of mathematics being manipulated to potentially evil ends. We will have trouble proving actual evil consequences, of course, since there’s no transparency. The only update we will get is via the next whistleblower who can handle guns pointed at him as he leaves his shower.
Explain your revenue model to me so I’ll know how I’m paying for this free service
When you find a website that claims to be free for users, we should know to be automatically suspicious. What is sustaining this service? How could you possibly have 35 people working at the underlying company without a revenue source?
We’ve been trained to not think about this, as web surfers, because everything seems, on its face, to be free, until it isn’t, which seems outright objectionable (as I wrote about here). Or is it? Maybe it’s just more honest.
When I go to the newest free online learning site, I’d like to know how they plan to eventually make money. If I’m registering on the site, do I need to worry that they will turn around and sell my data? Is it just advertising? Are they going to keep the good stuff away from me unless I pay?
And it’s not enough to tell me it’s making no revenue yet, that it’s being funded somehow for now without revenue. Because wherever there is funding, there are strings attached.
If the NSF has given a grant for this project, then you can bet the project never involves attacking the NSF for incompetence and politics. If it’s a VC firm, then you’d better believe they are actively figuring out how to make a major return on their investment. So even if they’re not selling your registration and click data now, they have plans for it.
So in other words, I want to know how you’re being funded, who’s giving you the money, and what your revenue model is. Unless you are independently wealthy and want to give back to the community by slaving away on a project, or you’re doing it in your spare time, then I know I’m somehow paying for this.
Just in the spirit of disclosure and transparency, I have no income and I pay a bit for my WordPress site.
When to quit your nerd job
I get lots of emails nowadays from quantitative people who are unhappy in academics, or in finance, or in tech, and want to know what they should do next, and specifically if they should quit their job. Most of them have Ph.D.’s or are even professors or well-established in their profession. They’re interested in switching fields, or at least jobs, and they want advice.
Maybe I get so many emails like this because they’ve read my advice post and realize I’m all about these three rules:
- Go for it! (this usually is all most people need, especially when talking about the crush type of advice)
- Do what you’d do if you weren’t at all insecure (great for people trying to quit a bad job or deciding between job offers)
- Do what a man would do (I usually reserve this advice for women)
I’m going to concentrate mostly on rule #2 today in giving job advice.
Most of the time, the people who ask me are in pretty darn shitty situations and really want to quit, and really want to be able to say to themselves that they deserve better, but are kept from doing so from some kind of fear that there are no better jobs out there or that they deserve to be treated badly. It’s really surprising and annoying that they are so afraid to ask for and demand more. Why are nerds always underselling themselves?
Here are things I hear people complain about that make me want to punch them (or really, give them encouraging hugs and then kicks in the pants):
- My brain is rotting. Why on earth would you stay in a job where your brain rots? Don’t you realize that, as Ph.D.’s in math or stats or physics or whatever, our brains are our main tools? That’s why we get paid, that’s why we will always be able to get a job, but only if we don’t let them rot. It’s kind of like an athlete saying, yeah I’m on this professional team but I spend all day lying around watching TV so my muscles have completely atrophies. Guess what, athlete, that’s no good!
- I’m isolated and nobody ever talks to me. If teamwork is important to you, this is a dealbreaker. Get your ass up and look around. Are there other people in your field/ department/ group who have similar skills as you but who are working with other people? How did they get that set up? Can you get that set up? If you have a boss, can you tell your boss you need to work with other people?
- I’m being used by my company – they pay me well but they don’t give me real work to do. I’m mainly here for them to show clients they have a Ph.D. working in the back. This is pretty common and really terrible, because it leads to brainrot as well as isolation, and moreover your name is attached to what is probably a shitty business model and product. I say demand to get in on the business for real or leave. Simple as that, you don’t want to collude in fraudulent business offerings.
- The pace and politics of academics drive me nuts. I totally get this, personally, because these are also things that led me to leave academics. On the one hand, I’m super glad I left because those things really did drive me nuts. On the other hand, let me just say, you never get rid of your problems, you just get new ones. You have to be prepared for fast-paced but still political problems outside of academics.
My theory is that people are way too slow to quit a job, or at least to agitate for a better position within their workplace. And keep in mind I’m saying this to you as an unemployed person, so you know I know how to quit a job – I’m a pro! The truth is, though, that I quit each job knowing there are lots of juicy jobs out there for people with quantitative skills.
My advice:
First find out what you’re worth on the open market. Look at job listings, talk to people, mention that you’re open to talking to people, find out what else is out there. You may realize you have it pretty good after all, or that it’s worth talking to people inside your company or department about changing your position slightly that would help out your mental state a lot.
Second, you could do the above and then end up saying to yourself, “What the fuck! They’re either promoting me/ moving me or else I’m quitting!”. This is a perfect moment to make demands you wouldn’t normally have the balls to make, and they often work. In fact you should keep in mind that it’s most companies’ policy to generally underpay and underappreciate their employees until they demand better, and then to give in to those demands. True fact.
Next, if you do decide to leave, do a budget on your finances and figure out how many months you can afford to be unemployed. It turns out that people are always very conservative about this (understandably) and it takes them quite a bit of emotional turmoil to even make that calculation with hard numbers. But it’s a good idea, because you’ll often find that you actually have enough money to quit your job and spend a few months learning skills and networking to get a job that you actually think might be a better fit for you.
You can also try to get another job while you’re working, but it’s really hard to be sure you’re not just embarking on a rebound relationship. I prefer being unemployed for a while myself, but it’s all about personality.
Good luck, and remember rule #2!
Someone didn’t get the memo about regulatory capture
So there’s this guy named Benjamin Lawsky, and he’s the New York State Superintendent of Financial Services. Last week he blew open a case against a British bank named Standard Chartered for money laundering and doing business with Iran.
The other regulators don’t like his style one bit, even though he managed to force Standard Chartered to pay $340 million for their misdeeds, as well as look like bad guys. I’ll get back to why the other regulators are pissed but first a bit more on the settlement.
What’s not cool about a fine is that nobody goes to jail and they continue business as usual, hopefully without the money laundering (their stock has mostly recovered as well).
What is cool about the $340 million fine is that it took almost no time compared to other settlements with banks (a nine month investigation before the blowup last week) and that it’s actually pretty big – bigger, for example, then the proposed settlement SEC is making with Citigroup which judge Rackoff blocked for shorting their clients in 2008 and not admitting wrongdoing.
In this case of Standard Chartered, they may not be admitting wrongdoing but we’ve all already read the evidence, as well as the smoking gun email:
The business chugged along even after the banking unit’s chief executive in the Americas warned in a 2006 memo that the company and its management might be vulnerable to “catastrophic reputational damage” and “serious criminal liability.”
According to the regulatory order, a bank official in London replied: “You f- Americans. Who are you to tell us, the rest of the world, that we’re not going to deal with Iranians.”
[Aside: do you think, being a polite Brit, that this guy actually wrote “f-” in his email?]
Back to the other regulators. They are so used to working for the banks, it is inconceivable to them to publicize damning evidence before giving the heads up to the bank in question looking for a quiet settlement. That’s the way they do things. And then they never get much money, and nobody ever goes to jail. Oh, and it takes forever.
They argue that this is because they don’t have enough resources to go the distance with lawyers, but it’s also because their approach is so weak.
So naturally they’ve been pretty upset that Lawsky has balls when they don’t, especially since he doesn’t have nearly the resources that the SEC has.
My favorite ridiculous argument against Lawsky and his approach came from this article I read yesterday on Reuters. It stipulates that Lawsky is creating an environment where there’s a possibility of regulatory arbitrage. From the article:
But a central lesson of the financial crisis was the need for regulators to better cooperate and share information. Working at cross purposes creates opportunities for what’s known as “regulatory arbitrage,” whereby banks circumvent regulations by exploiting rivalries among their various overseers.
Um, what? That whole mindset is clearly off.
The goal would be the regulators get to decide who’s the bad guy, not the banks. And don’t tell me loopholes in the regulatory structure are introduced by having a regulator willing to do his job without sucking everybody’s dick first. Please.
And if I’m a regulator, and if it would work better to share my information with Lawsky to do my job as a regulator, you better believe I’m willing to share it with him if I can get credit alongside him for exposing illegal activities. That is, if I really want to expose illegal activities.
The U.S. Treasury is a bad baby daddy
It occurs to me, when reading Treasury’s latest excuse for the unbelievably shitty performance of HAMP, that Treasury has been a really crappy baby daddy. From a recent New York Times article (also see this):
Mr. Summers declined to comment on the record, but other current and former officials echoed Mr. Geithner’s view that the administration had done well under the circumstances. Some said they underestimated the complexity of helping millions of people. Some said they tried too hard at first to protect taxpayers from unnecessary losses. But they agreed that the most important problem was beyond their control: the mortgage industry was set up either to collect payments or to foreclose, and it was not ready to help people.
“They were bad at their jobs to start with, and they had just gone through this process where they fired lots of people,” said Michael S. Barr, a former assistant Treasury secretary who served as Mr. Geithner’s chief housing aide in 2009 and 2010. “The only surprise was that they were even more screwed up than the high level of screwiness that we expected.”
I mean, let’s say I have a whining teenager who I’ve just realized has stolen my money, signed my name to various notes to the principal, and has been playing hooky for months or even years. I might not ask that same kid to help his friends with their college applications unsupervised.
I might think he needs to be watched, and that I’d keep in mind the selfishness and immaturity that he’s already exposed as I watch him, to make sure he doesn’t end up plagiarizing his best friends’ college essay, or steal the application fees, or something else I hadn’t even thought of.
What I wouldn’t worry about is the possibility that he’s not smart enough to help his friends – he’s already shown me how manipulative and clever he can be when it benefits him.
Moreover, if I didn’t supervise that kid, then after none of his friends get into college I’d blame myself, and not the kid, for my failing. Because he’s only a kid, and I’m supposed to be the grownup. I’d be a bad baby daddy.
That’s what Treasury is doing. Those guys knew better than to trust the banks with something like HAMP, which was essentially unsupervised and had too many conflicting incentives for the banks to ever be expected to actually help people in trouble with their mortgage. They set it up terribly, looked the other way when the banks did nothing (and as Barofsky explained to us, this was intentional – they were foaming the runway for the banks to recover), and now they’re trying to say it’s because the banks were screwed up.
Not good enough, Treasury.
mathbabe 