mathbabe

Yesterday the ratings agency S&P settled a lawsuit with the Department of Justice for awarding ridiculously high ratings for mortgage-backed securities way back when. For their massive contribution to the world-wide financial crisis, they got fined $1.5 billion, nobody went to jail, and they didn’t even have to admit what they’d done is wrong.

But here’s something they did admit: their use of the word “objective” when describing their models was mere “marketing puffery,” not to be taken seriously. This is called the “puffery defense” by Bloomberg.

To be fair, this wasn’t just about the usage of the word objective. From Bloomberg’s piece:

S&P said in its request to dismiss the case that the government can’t base its fraud claims on S&P’s assertions that its ratings were independent, objective and free of conflicts of interest because U.S. courts have found that such vague and generalized statements are the kind of “puffery” that a reasonable investor wouldn’t rely on.

Now, as some of you know, I’m writing a book about destructive mathematical models. And pretty much all of the models make claims of being objective. It’s part of the marketing for those models, a requirement to lure people into using complex, mathematical black boxes instead of their own brains, and crucially, in place of their own sense of fairness and accountability.

Example: Value-added models for teachers are showered with claims of objectivity (see page 4 of this marketing brochure for example), even though those claims are questionable at best.

So, it makes me wonder, is the Puffery Defense going to be widespread? Is it a technical and legalistic approach? Are we going to have a redefinition of that word so that companies are officially allowed to claim objectivity while actually meaning nothing like objectivity?

I was disappointed with Obama’s retraction of the tax benefit for college savings, referred to as the “529 plan.”

And, although some would claim that the 529 tax shelter was used by more than rich or very well-off people, it’s still a very lopsided regressive tax, because the majority of Americans can barely scrape by on their income, never mind saving for their kids’ college funds. But that’s not exactly the point I’m trying to make, although it’s a very important point.

The larger point is this: whenever we make college more affordable by helping people pay for college, it just makes college more expensive. Tuition rises to meet our new-found ability to pay. And although I can’t prove causality for every tuition hike, the data kind of speaks for itself:

from: http://www.mybudget360.com/college-worth-costs-debt-college-tuition-and-student-debt-levels-value-of-education/

versus here’s the federal aid growth:

from http://www.zerohedge.com/news/2012-09-28/student-loan-bubble-19-simple-charts

The result of our federal loan programs, which were started with good intentions, is that whereas before college was out of reach for lots of people, now it’s still out of reach, they go anyway, and then emerge loaded with debt. It’s not actually a huge improvement for the vast majority of the middle class, but it’s become a requirement to get a reasonable job so people are forced to go through it, kind of like a hazing ritual.

There’s another related reason why college tuition goes up, namely because we have stopped funding state schools, so their tuition is higher, and the other colleges also rise to meet them. But part of the reasoning behind that is because we have all these federal loans available, so why would we need to fund the state schools.

We need to put into place ways for tuition to go down. First, we make paying for college harder, and that includes for upper middle class folks. The reasoning is this: if you’re the only person having trouble paying for something, that’s bad. But if everyone has trouble paying for something, the price goes down.

Second, we make state schools much cheaper, or even free, by funding them.

Lots of traveling in the last couple of days, and not enough sleep, has prevented me from posting as often as I’d like. Aunt Pythia sends her regrets.

But if you’re interested, please take a few minutes to listen to this week’s Slate Money podcast, which I particularly enjoyed being part of, and meeting this week’s esteemed guest Mina Kimes of ESPN the Magazine. We talked about money issues around sports, namely whether college athletes should be paid, the economics of stadiums in cities, and the NFL Commissioner Roger Goodell.

If you’re interested, go here or look up “Slate Money podcast” on iTunes.

It’s interesting to me how science has come into conflict with the news in the past week. First we had the deflategate, where footballs mysteriously deflated during a playoff game, and then we had an over hyped blizzard.

The NFL recently hired physicists at Columbia to help make the case for science with the football fiasco, but I think that’s unnecessary: a few good experiments with temperature and friction and lots of measurements by lots of different pressure gauges will empirically demonstrate how much of a range we might expect from such things. In other words, understanding errorbars.

As for the blizzard, this article nicely articulates the science of weather forecasting and what went wrong. But what is interesting is that, in general, models have gotten much better, and in particular are good at predicting how powerful a storm is going to get. In this case the model got that right, but then the error came in figuring out exactly where the storm would travel and when.

Again, it’s a case of errorbars, and the public seems not to understand it. Or maybe they just don’t want to.

In fact, I heard quite a few people call in to ESPN radio over the past week trying to explain to the sports radio hosts what might be going on scientifically, only to be hung up on. The truth is, it’s not as interesting a story to think about it just happening outside our control. It messes with our sense of omnipotence and control.

This is bad news for society, as more and more things become “datafied” and as we assume that will translate into perfect information.

This Thursday I’m heading up to the Dartmouth Math department to give a colloquium on the subject of data science. They made the following poster for my talk:

Also, next month I’m excited to be the moderator of a panel at the All Souls Unitarian Church on the east side of Manhattan, with some amazing panelists. There’s also a poster for that event:

I hope I see you there!

The exciting news today (besides tonight’s blizzard!) is the Greek elections. Yesterday an anti-austeristy party called Syriza won the plurality of the votes, and is on the very verge of winning a majority as well.

This is huge because the leader of the party, Alexis Tsipras, has basically promised the Greek people that, if elected, he would refuse to pay off any more of Greece’s debt.

How did this happen? Well, From the perspective of the Greek people, the negotiations around their economic problems have been taken on by their last two governments since 2008 with a bunch of European technocrats behind closed doors and in an intensely undemocratic process. Well, this is when democracy fought back.

Possible ramifications: If Greece indeed defaults, and it might leave or get booted out of the Eurozone (this is called “Grexit”), which may or may not be a good thing for Greece long term, but in any case is very interesting. Short term, the black market in Greece is said to be highly developed, so the average person isn’t entirely dependent on functioning banks anyway.

Also, I’m sure Greece has been looking at Argentina recently to see how their (accidental) default has been going, namely not as bad as everyone predicted. The world will be watching Greece to see what happens and to see how smaller countries can and will deal with stifling debt in the future.

Time passes quickly, my friends. It seems like only yesterday that Aunt Pythia was answering really long questions, and today her questions seem to be extra short. Last week it was cold outside – freezing! – but this week it is warm and snowy (but not for long!). Last week she was knitting a cowl, this week a colorful scarf. Crazy changes, in other words.

Indeed the only thing that hasn’t changed is an absolute willingness, on the part of Aunt Pythia, to offer up irrelevant and terrible advice to you earnest people. Many apologies, you definitely deserve better, but this is just something Aunt Pythia was born with, there’s nothing for it.

My suggestion for you is to just turn away and stop reading. I mean, how many obscene images must one be subjected to??

This is a liqueur filled sperm-shaped bottle. I know it really exists because I bought one at a liquor store in San Antonio a couple of weeks ago, no shit. No, I haven’t opened it yet.

Wait, you’re still here? Really? Well, in that case, come on in, enjoy the warmth, get under a hand-knitted blanket, and don’t forget to:

ask Aunt Pythia a question at the bottom of the page!

By the way, if you don’t know what the hell Aunt Pythia is talking about, go here for past advice columns and here for an explanation of the name Pythia.

——

Dear AP,

is it worth saving, or should we just burn it all down and start again?

Sick of Bull Systems

Dear Sick,

I’m going to assume you’re talking about the financial system. I’m tempted to say “burn it” but there would actually be severe short-term problems caused by there being no financial system. Moreover, it isn’t clear that a new one would be built better than the existing one. I know that sounds disappointingly unrevolutionary, but there it is.

If you are feeling desperate, may I suggest ignoring it and starting a new one. If I had time I would be more active in the public bank movement in this country, which seems like a better alternative to ours and can exist in parallel.

Aunt Pythia

——

Dear Aunt Pythia,

Have you seen the Celtic Oracle designs? I made a deck but would like additional divination material.

Oracular Designs

Dear Oracular,

Nice! And flattering to oracles such as myself! Can I make a wee request? More naked people, especially men? Thanks.

Aunt Pythia

——

Dear Aunt Pythia,

I would like to start watching Dr. Who but I’m intimidated by 50+ years of shows. How do you get started?

Dr. Who Ignoramus

Dear DWI,

Common problem, I sympathize. The truth is, it doesn’t matter much. Let me give you a cheat sheet which should be more than adequate:

1. Dr. Who is always a man who talks fast and is incredible smug, although usually in a lovable way.
2. He sometimes has a dog named K-9 with him. If he does, you’re watching an earlier show.
3. He almost always has a “companion” with him, who is almost always female, mostly young, and sometimes a love interest, although not in earlier shows.
4. Sometimes his companion has other companions, who are often there as comedic relief.

That’s about it! Oh, and they travel through time solving problems on earth and on alien planets. So there, now you have no excuse not to watch.

Aunt Pythia

——

Hello Aunt Pythia,

Not a question, but a thank you for your answer to my previous question. It was helpful, and you are right! College towns are still towns, and as such the occupants must take the usual precautions when going out. I knew this, from personal experience walking home many a late night during grad school. But somehow the father in me did not want to admit it.

After I first wrote, a talk with my daughter segued into a talk about college, academics, academic pressure, and campus safety, and I was once again surprised by how grown up my daughter is. She and her friends are well aware of the risks, and do watch out for each other. And now that she has been accepted at Cornell (we found out last night), we’ll no doubt have these talks again, at which time I will mention Aunt Pythia’s advice.

Thank you once again,

Worried In Academia

Dear Worried,

Wow, wonderful! I almost never know if my advice actually helps, so this is amazing feedback, thank you for giving it to me!

Aunt Pythia

——

Dear Aunt Pythia,

I’m in my first year in a Phd program in math. I’ve always been academically successful, especially in math, and this semester is no exception. Although I know it will be difficult and take a lot of hard work, I’m moderately confident that I have the ability to get through my qualifying exams. After all, my aptitude for high stakes testing is what’s gotten me this far.

It’s what comes next that concerns me. Specifically, I’m not at all confident that I have what it takes to actually do research in math. I generally have a good memory (especially in the short term); I’m good at reproducing proofs I’ve seen before and at applying techniques in ways I’m familiar with, but I worry that I’m not an especially creative thinker and also that I coast by via collaborating with others. (I realize that the second concern can be irrational, at least from a coursework perspective, since I do comparably well on exams as on homework, but it’s still in the back of my head.)

(It’s probably also be pertinent to mention that I’m male, and haven’t ever felt invalidated either institutionally or on an individual level with respect to my ability–these concerns are entirely my own.)

I’ve had only two research experiences up until this point. The first I don’t put much weight on, since it was in another field that I quickly realized I was not that interested in (which contributed to my decision a few years ago to focus more on math). The second was a project I worked on with a faculty advisor throughout my last year and a half of undergrad. It was in an area that I was interested in and my advisor was great. However, I often would become consumed with anxiety and overwhelmed to the point that I was unable to get anything done.

Part of it was adjusting to working independently and in an unstructured environment, but even when I was given a list of moderately specific things to do it didn’t necessarily help. Despite having plenty of time available to devote to the project, I would put off working persistently, often getting to the point where I would stay up later and later the hour before my next meeting, becoming more and more panicked but for some reason still incapable of working. By the time I’d snap out of it, it would be so late that I would be too exhausted to really do my best work, and it definitely showed. It was stressful for me, frustrating for my advisor, and (clearly) not really productive mathematically. And yet I couldn’t bring myself to change, week after week!

I felt especially bad for being a disappointment for someone who has done a lot for me and who inspired me to seriously pursue math in the first place. Especially, since to anyone not in my head this all came across as purely poor work habits/laziness–my advisor told me shortly before graduation that I have everything it takes to succeed in grad school, as long as I work hard enough, which was simultaneously affirming and distressing. Part of me also thinks that this was all just garden variety laziness and that if I just had worked harder and focused better it wouldn’t have been an issue.

So I guess my question is, where do I go from here? What can I do to keep this from happening in the future? Do I really have a problem, or is it just a combination of laziness and lack of self-confidence?

Apprehensively,
Uneasy New Scholar, Upbraiding or Reassurance Essential

Dear UNSURE,

First off, amazing sign off. Much appreciated.

Next, thank you so much for asking this question. And, given that you are a highly successful and encouraged male, the issue is nicely isolated: how does doing well on highly structured undergrad work and standardized tests relate to being a good researcher?

The answer is unclear, actually, in general. I mean, I don’t want to panic you, because I actually think math research is a skill you can pick up if you are smart and work hard, but on the other hand, it might not be that easy, especially for you.

Let me put it this way. Theoretically, we want to attract to math research a bunch of people who:

1. love math,
2. work hard and don’t mind being wrong and can live with not knowing whether they are, and
3. are “good at math”, where I’m going to ignore what exactly that means, partly because I don’t want to get drawn into the genius myth discussion and partly because I actually think the first two qualifications are dominant.

But here’s the thing. Instead, we attract to math research, via our post-college applications selection method, people who:

1. may love math but may just have been told they’re “good at math” and may not know the difference,
2. know how to master a well-defined skill where they get continuous feedback from tests and other people that they are making progress, and
3. are probably plenty “good at math.”

So you see, there is likely a mismatch between the first two points.

I’m going to hope, for your sake, that you love math. Because you’ll need it, believe me.

Assuming you do, then you’ll need to spend time on #2, which means you (ironically) need to stop caring about outside measures of progress so you can lose yourself in your work and make progress. Get it? It’s confusing when you first encounter it, and unintuitive, and it might be extra hard for someone who is addicted to external evaluations and encouragement, which honestly it sounds like you are to some extent. Just as an example, you don’t owe your advisor anything except your gratitude. You are doing math for yourself from here on in.

The good news is, it often sucks at first, so don’t think you’ve already failed. You just need to develop new skills. It’s kind of like a muscle you didn’t know you had that you need to make super strong.

I suggest trying it out in short bursts. Find yourself a few hours of time, where you are not urgently needed by some classwork or something, and lose yourself in thought around some mathematical object, with no specific need of a milestone. Play with the math, see what you find, and don’t feel like you’ve wasted your time at the end, even if you feel like you have. It was your time to waste, after all.

Anyhoo, that’s the muscle you will need to develop. Once you get good at it, you can lose yourself for days or weeks at a time and then every now and then stumble on actual progress. You can do it! Start small!

At least that’s how it has always worked for me. Other mathematicians, feel free to chime in if you disagree.

Good luck!

Aunt Pythia

——

Well, you’ve wasted yet another Saturday morning with Aunt Pythia! I hope you’re satisfied! If you could, please ask me a question. And don’t forget to make an amazing sign-off, they make me very very happy.

Click here for a form or just do it now:

I’m paying lots of attention to the Supreme Court’s coming decision on The Fair Housing Act. A New York Times editorial of this morning does a good job explaining the issues, including the concern that Chief Justice Roberts seems to think we’ve moved past racial discrimination in this country.

The burning question is whether housing developments and the like are responsible only for intentionally discriminating against individuals, or whether they are responsible in a more general, statistical sense, of having disparate impact on groups of people. The New York Times, like me, hopes for a broader reading, consistent with the 11 courts of appeals decisions over the last 40 years. From the Times:

The ability to show discriminatory effect has only become more important as intentional discrimination has become harder to prove. To take one prominent example, the Justice Department relied on it to negotiate the largest-ever fair-lending settlement — $335 million — with Bank of America in 2011. The bank’s mortgage unit, Countrywide Financial, had charged higher average fees and interest rates to black and Latino borrowers than to whites with the same credit risk, a practice that former assistant attorney general Thomas Perez called “discrimination with a smile.”

This case is focused on housing, but of course it could generalize to all sorts of other systems, including job applications and credit applications among others.

If we stick to the “intentional discrimination” only, we are opening up a door to (even more) widespread use of algorithmic decision-making that produces unfair and discriminatory results. And as it turns out, it’s easy to produce a model that effectively discriminates.

And if you are not in charge of your own system, then who is?

I’m neck deep in writing nowadays, but I wanted to share two extremely interesting and provocative pieces around women which come at feminism with from very different angles.

First, this essay, entitled If we liberate men’s sexuality, the war against women can end (hat tip Susan Webber), was written by a professional dominatrix, which is always an eye-opening perspective. She suggests that if we promote a new kind of feminism which she calls intersectional feminism, rather than depending on the old-school moralistic feminism, then we have a better chance to reach men, especially the men who might otherwise join the extremist misogynistic “men’s right’s” movement or become part of the vile pickup artist movement.

I think she has a bunch of interesting points. It is clearly true that men are boxed in in terms of their sexuality just as women are, and for men that don’t fit the standard mold it amounts to a kind of torture; the answer then is to promote a kind of sexual license for all people, not just women. Also, I think she’s absolutely correct to focus on sexual frustration as a major cause of all sorts of bad things. It’s not just about competing for jobs with women, it’s also about not getting laid.

Second, this Science Friday piece (hat tip Thessy) on the perceived requirement of innate genius as an obstacle for women in various fields. I wrote about this issue recently.

In particular a caller named Emily tells the guests how she was a straight A student at NYU, who graduated summa cum laude, and was passionate about philosophy, but was told by her advisor that she “just didn’t have what it takes” to go on to graduate school.

I cannot tell you how many people I know who have gone through something similar. And, I might add, such stories, which are generally completely unreported, flies in the face of ridiculous claims such as those made in this recent New York Times opinion piece that sexist mistreatment in science is minor and anecdotal.

Last thing: it’s cool and interesting how many conversations are being conducted around these important issues. I see it as progress just to be able to assume that other people I run into are sufficiently aware of the issues to talk about them, including my teenage sons.

This was crossposted from Not Even Wrong and written by Peter Woit.

Last summer I wrote here about an article in the AMS Notices which appeared to make misleading claims about the NSA’s involvement in putting a backdoor in an NIST cryptography standard known as DUAL_EC_DRBG. The article by Richard George, a mathematician who worked at the NSA, addressed the issue of the NSA doing this kind of thing by discussing an example of past history when they were accused of doing this, but were really actually strengthening the standard. He then went on to claim that:

I have never heard of any proven weakness in a cryptographic algorithm that’s linked to NSA; just innuendo.

This appears to be a denial of an NSA backdoor in the standard, while not saying so explicitly. If there is a backdoor, as most experts believe and the Snowden documents indicate, this was a fairly outrageous use of the AMS to mislead the math community and the public. At the time I argued with some at the AMS that they should insist that George address explicitly the question of the existence of the backdoor, but didn’t get anywhere with that. One of their arguments was that George was speaking for himself, not the NSA.

The question of fact here is a very simple and straightforward mathematical one: how was the choice used in the standard of points P and Q on an elliptic curve made? There is a known way to do this that provides a backdoor. Did the NSA use this method, or some other one for which no backdoor is known? The NSA refused to cooperate with the NIST investigation into this question. The only record of what happened when the NIST asked about how P and Q were chosen early on in the development of the standard is this, which indicates that people were told by the NSA that they were not allowed to publicly discuss the question.

Remarkably, the latest AMS Notices has a new article with an extensive discussion of the DUAL_EC_DRBG issue, written by mathematician Michael Wertheimer, the NSA Director of Research. At first glance, Wertheimer appears to claim that the NSA was unaware of the possibility of a backdoor:

With hindsight, NSA should have ceased supporting the dual EC_DRBG algorithm immediately after security researchers discovered the potential for a trapdoor. In truth, I can think of no better way to describe our failure to drop support for the Dual_EC_DRBG algorithm as anything other than regrettable.

On close reading though, one realizes that Wertheimer does not address at all the basic question: how were P and Q chosen? His language does not contain any actual denial that P and Q have a backdoor.

For a careful examination of the Wertheimer piece by an expert, see this from Matthew Green. Green concludes that

… it troubles me to see such confusing statements in a publication of the AMS. As a record of history, Dr. Wertheimer’s letter leaves much to be desired, and could easily lead people to the wrong understanding.

In a recent podcast on the subject Green states

I think it’s still going on… I think that the NSA has really adopted a policy of tampering with cryptographic products and they’re not going to give that up. I don’t think that this is a time that they want to go out admitting what they did in this particular case as a result of that.

Given that this is now the only official NSA statement about the DUAL_EC_DRBG issue, the Notices article has drawn a lot of attention, see for instance here. The Register summarizes the story with the headline NSA: So sorry we backed that borked crypto even after you spotted the backdoor.

The publication of the George and Wertheimer pieces by the AMS has created a situation where there are just two possibilities:

• Despite what experts believe and Snowden documents indicate, the NSA chose P and Q by a method that did not introduce a backdoor. For some reason though they are unwilling to state publicly that this is the case.
• P and Q were chosen with a backdoor, and the AMS has been now repeatedly been used to try and mislead the mathematics community about this issue.

I’ve contacted someone at the AMS to try and find out whether the question of a backdoor in P and Q was addressed in the refereeing process of the article, but been told that they won’t discuss this. I think this is an issue that now needs to be addressed by the AMS leadership, specifically by demanding assurances from Wertheimer that the NSA did not choose a backdoored P and Q. If this is the case I can see no reason why such assurances cannot be provided. If the NSA and Wertheimer won’t provide this, I think the AMS needs to immediately cut off its cooperative programs with the agency. There may be different opinions about the advisability of such programs, but I don’t think there can be any argument about the significance of the AMS being used by the NSA to mislead the mathematics community.

In a recent issue of Science, there was an article entitled Belief that some fields require ‘brilliance’ may keep women out (hat tip Gary Cornell) that absolutely resonates with my experiences, both as a mathematician and as a teacher.

Namely, it talks about the extent to which women are discouraged to go into a field because that field is somehow reserved for “geniuses,” and women are rarely if ever bestowed with that label. Mathematics is definitely one of those fields; if you are exceptionally successful in mathematics, people call you a genius, and it’s pretty hard to be successful if people don’t think you’re a genius.

But other STEM fields have less of a reputation for geniuses, and they have correspondingly more women. Biology, for example. Moreover, there are some fields outside of STEM that have way fewer women, which seems unexplained unless you have the “genius” theory. Philosophy is the obvious example here, a very macho field.

In the Science article, they were reporting on a study done by Sarah-Jane Leslie, Andrei Cimpian, Meredith Meyer, and Edward Freeland, in which they surveyed researchers from all sorts of fields in all sorts of research universities and asked them to rate, on a scale of 1-7, statements about their own discipline along the lines of, “Being a top scholar of [discipline] requires a special aptitude that just can’t be taught”. Here’s the critical graph:

STEM subjects above, non-STEM below. The negative correlation is the key to this study. I am particularly struck by the difference between statistics and math.

It’s just one study, and the response rate was small, so the word is not final. Even so, I think this proves that we should look into this more, gather more evidence, and see where it leads.

Personally, I have already spent quite a bit of time trying to deal with this very problem in mathematics. For example, I’ve explained before how I deliberately teach kids an introduction to proof that emphasizes practice over the silly and distracting concept of having an innate gift. It works, and it’s more fun too, for both men and women.

If I were designing a curriculum for STEM subjects I would rely heavily on this idea to inform my approaches to all sorts of things, partly because I think it’s true, but partly because the other things we think might matter are harder to change.

If you think about it, it’s actually a pretty reasonable roadmap for how to attract a more diverse group of people to mathematics or other subjects. You just need to create an environment of learning that emphasizes practice over genius. Actively dispel the genius myth. Achieving that cultural shift gets harder the higher up the research ladder you go, though, partly because it’s hard for older people to give up the “genius” label they worked so hard for. But it’s worth a try.

There’s a new book out, called The Black Box Society and written by Frank Pasquale, a lawyer focused on technology and a friend of mine. It’s published by Harvard University Press and it looks like this:

To be honest, when I first received it I was a bit worried that it would make my book, which I am utterly engaged in writing, entirely moot. After all, Frank and I had discussed his book and I’d seen earlier versions. I knew it contained information about racist secret algorithms in finance and tech, and there were also other issues in common with our two books.

Now that I’ve had a chance to read it, though, I’m not as worried. First of all, Frank’s book is aimed at a different audience, which is to say a somewhat more academic and technical audience. In particular his policy recommendations near the end of the book seem to be written for lawyers who know the current laws and need arguments to improve them.

Also, his focus is on secrecy itself as a means of power, whereas I focus on models as the object of interest.

I like a lot of what Frank says, and I think his metaphors work really well. For example, he talks about the early promise of the internet to expose information of all sorts, on powerful corporations as well as individuals. Then he talks about how reality has been a disappointment, and we’ve ended up with an internet that acts as a “one way mirror,” whereby powerful corporations can see into individual’s lives but those individuals can’t look back.

He also makes the important point that, when it comes to the NSA and other government agencies snooping around, while they might be legally prevented from gathering certain kinds of data about people, nothing prevents them from buying information and profiles from data warehouses like Acxiom, which can do the kind of collecting that they can’t. In other words, the data warehousing industry acts as a giant loophole in the set of rules protecting our civil liberties.

For another really interesting review of Frank’s book, written by a software engineer, take a look at David Auerbach’s Slate review (hat tip Jordan Ellenberg). In particular he has interesting things to say about the extent to which algorithms are intentionally evil (they’re probably not) and the extent to which engineers can fix problems (they probably can).

In any case, I recommend The Black Box Society, it’s a fascinating and important book.

I seem to have caught a break at the San Antonio airport, with free wifi. So I will take this opportunity to offer a link to my prezi talk.

See the prezi here: http://prezi.com/makkue0d84nc/?utm_campaign=share&utm_medium=copy&rc=ex0share

One embarrassing omission from my talk is the existence of many public facing math podcasts. Embarrassing not because I knew about them – I didn’t – but because I should have, since after all I participate in a weekly podcast myself, so of course I know it’s a new and exciting medium. Luckily, the audience member who pointed out my mistake has agreed to write a guest post surveying the math podcast landscape, so stay tuned for that.

Did you hear about TechCrunch’s leaked documents detailing the client list of Palantir, the super secretive data mining contractor (hat tip Chris Wiggins)? Palantir, founded by uberlibertarian Peter Thiel, had clients as of 2013 including the LAPD, the CIA, DHS, NSA, the FBI, and CDC. Besides data mining for government agencies, they also work in the finance sector and the legal sector.

Here’s the scariest thing about the TechCrunch article:

Samuel Reading, a former Marine who works in Afghanistan for NEK Advanced Securities Group, a U.S. military contractor, was quoted in the document as saying It’s the combination of every analytical tool you could ever dream of. You will know every single bad guy in your area.”

That quote, if true, belies a lack of understanding of what data mining can actually do in terms of accuracy. No data mining tool can be both comprehensive and accurate – find all the bad guys with no accidental good guys getting caught in the net. It’s just not possible, unless you have DNA samples with markers for “bad guyness,” and even then DNA tests sometimes get mixed up.

It behooves an expensive and fancy consulting company to act like their tools are prophetic, however, even if that means false positives or false negatives happen all the time, which of course they do, with any algorithm.

It’s bad enough when stupid start-up companies claim big data solves everything, when what they’re doing is trying to solve a problem nobody cares about. It’s another thing altogether when it’s our military and military contractors and police and secret services, and when we don’t have any view into what it actually does. Scary stuff.

So I’m here at JMM, hanging out with my buddy Aaron Abrams and finagling free wifi at the Hyatt (pro tip from Jonathan Bloom: sign up to be on their gold membership plan, which is free, and as a member you get free wifi).

Aaron and I started talking about the case of MIT professor Walter Lewin, and whether his OpenCourseWorks physics lectures should or should not have been removed after he was discovered to have been a sexual harasser.

UPDATE: Here’s an article giving some idea of what Lewin did, which was basically to harass women who were taking his online class.

I’ve already asserted that it makes sense to me that they are removed, but I wasn’t happy with my explanation. I think I’ve understood it better now, and I wanted to throw it out there.

To explain it, let’s move to a more cut and dry example, or at least an older one, namely Harvard mathematician George Birkhoff. That guy was a hugely famous and powerful mathematician in his day, which was in the 1930’s. He was also a huge anti-semite, and prevented Harvard from hiring jewish mathematicians fleeing the Nazis.

When it comes to doing math, I might write a paper that uses a result he proved. Will I cite him? Personally, I would feel weird about it. Citing someone, speaking their name, is not just a mathematical shortcut, a way of avoiding proving everything from basic principles, although it is that, of course. If you have no prior knowledge about someone, you might not see that, but I’ve set it up explicitly so you see more than that.

Here’s what I see. By citing him, I am doing more than giving him credit for proving something, I’m including him in the community of mathematics, which is actually an honor. And honestly I’d rather not honor the wisdom of someone I detest.

Update: to be clear I would cite him if I needed to. I just would actively feel weird about it. I might even add a note.

Going back to Walter Lewin. Supposedly he can explain certain kinds of physics really really well. People say this, and I believe them. But of course the physics is already known, he’s not inventing something, and other people can also explain it, just not quite as well, at least right now.

Why would a given person choose to watch Lewin’s lectures instead of someone else’s lectures on the same material? Well, what is the delta between those two experiences? On the one hand, it’s a better explanation, which adds, but on the other hand, it’s the knowledge that we are honoring a man with no integrity, which subtracts. If written citation is received wisdom, then actually sitting and listening to a person is even more intimate.

For me, personally, these two opposite considerations don’t add up to a net positive. I’d rather watch someone else explain the physics.

As for MIT’s OpenCourseWorks (OCW) platform, they also had a “delta” computation to make, and they had to take into account the community they are trying to build through OCW. They want women in particular to feel welcomed to that community, and they decided that the videos’ presence made that more difficult (and it’s already difficult enough in physics). I think they made the right call.

This is a guest post by Tom Adams, who spent over 20 years in the securitization business and now works as an attorney and consultant and expert witness on MBS, CDO and securitization related issues.

Good news for would-be home buyers  – the Obama Administration heard your concerns and has a new tool to help make homes more affordable!

Are they going to increase wages? Or reduce the price of homes? No, they’re going to attack mortgage rates for Federal Housing Administration (FHA) borrowers. Of course, mortgage rates are already at close to all time lows, having declined significantly over the past year to about 3.7% on conventional 30 year fixed rate loans. The Administration’s main tool for doing this is to cut the insurance fee charged by the Federal Housing Authority on new mortgages by 0.50%, from 1.35% to 0.85% (on top of the interest rate charged to borrowers).

This fee is paid by borrowers into a fund that the FHA uses to protect itself against losses in case borrowers that it has insured later default. In theory, this move was somewhat controversial because the FHA’s fund had incurred higher than expected losses during the crisis and the FHA had to ask Congress for money to shore up the fund not that long ago. Around the same time, the FHA raised this insurance premium to additionally replenish the fund.

If it’s already really cheap to borrow money, is another 0.5% reduction going to make that big a difference? Probably not, because historically low interest rates haven’t been the obstacle to buying a house. I expect the number of net, new home buyers produced as a result of this change will be considerably lower than the Administration is projection (“millions of homeowners,” according to Obama’s statement today).

Rather, would-be homeowners don’t have the income to support buying the houses listed for sale in their markets – which is another way of saying that, for average Americans homes are too expensive for them to afford (or wages are too uncertain for them to want to buy).

Also note that the new lower fee is primarily aimed at new home purchasers. In order for existing FHA borrowers to get the new lower premium they would have to refinance into a new loan, which means they’d have to incur new closing costs. The new closing costs would probably eat up most of the savings for a year or more. Presumably, this would discourage many existing borrowers from refinancing for the lower premium, which helps the FHA by allowing it to retain the old, higher premium on the borrowers who don’t refinance.

This highlights one of those fundamental conundrums in the housing market. Existing homeowners and home sellers want home prices to go up. Representatives of this group are great at lobbying and have convinced many people (including, by all appearances, this Administration) that rising home prices are a good thing for America. On the other hand, potential home buyers would rather not have home prices going up – because that makes buying much harder. For whatever reason, this group has about zero lobbying juice.

Making credit cheaper is a small tool the Administration has via this reduced premium, so they used it, I guess. But it’s an action that has consequences, including potentially running the risk of not having enough in the fund down the road if losses increase (not a risk I’m especially worried about – the Urban Institute did a fine analysis of why the lower fee is probably sufficient – but it’s a reasonable concern).  In addition, it is somewhat disheartening that the Administration still seems to believe that the solution to consumer issues is to have the consumers take on more debt.

The most significant impact of this change is that it will make FHA loans more competitive with Fannie Mae and Freddie Mac loans.  You may recall that Mel Watt, the man in charge of the Federal Housing Finance Agency (FHFA), which manages Fannie and Freddie, made a big announcement recently that the GSE’s would offer 97% loan-to-value (LTV) ratio loans to qualified borrowers.  Previously, that type of LTV had been mostly the territory of the FHA.

So, effectively, this is just a form of catch-up for the FHA. The various government housing agencies are competing for market share among the same limited universe of qualifying borrowers by trying to get them to take on bigger mortgages than they would qualify for previously. For the average would-be buyer of the average house, the new, lower FHA fee would be worth about $900 a year, equivalent to about a $75 reduction in monthly payment.

It’s hard to believe that anyone in the Administration believes that this will do much for making homes more affordable for Americans. Perhaps it is a measure, however, of how seriously the Administration is taking the issue of housing affordability. There are big issues in housing and the economy that need to be taken seriously – like resolution of Fannie and Freddie, home prices that still remain beyond the reach of many Americans, stagnant wages, on-going foreclosure and mortgage servicing problems – but the Administration seems content to tinker around the edges and try to sell it as important reform.

Hey, so this is cool. The Alternative Banking group just came out with a second Huffington Post essay, this time on how the bailout isn’t over, how it didn’t work, and how we’re already preparing for the next one. I think it came out really well. You can read it here.

Also, I’ll be giving a talk at the Joint Math Meetings again this year, this time as an invited MAA speaker. My title is Making the Case for Data Journalism, and you can see the abstract here. I guess I’m speaking on Monday afternoon at 4pm in a place called the Lila Cockrell Theatre.

So, a few things. If you’re a math nerd planning to be in San Antonio this weekend, please don’t leave Sunday, because there are still talks on Monday! Also, if you want to hang out, leave a comment or send me email and I’ll try to figure out a way to meet up with you. I honestly feel like I don’t know too many mathematicians anymore, so it would be nice to see or meet a friendly face. I’m getting to San Antonio Friday.

Today I’m going to do my best to explain Andrew Gelman’s recent intriguing post on his blog for the sake of non-statisticians including myself (hat tip Catalina Bertani). If you are a statistician, and especially if you are Andrew Gelman, please do correct me if I get anything wrong.

Here’s his post, which more or less consists of one picture:

I decided to explain this to my friend Catalina, because she asked me to, in terms she could understand as a student of midwifery. So I invented a totally fake set-up involving breast-fed versus bottle-fed babies.

Full disclosure: I have three kids who were both breast fed and bottle fed for various lengths of time and, although I was once pretty opinionated about the whole thing, I could care less at this point and I don’t think the data is in either (check this out as an example). So I’m not actually trying to make any political point.

Anyhoo, just to make things super concrete, I want to imagine there’s a small difference in weight, say at 5 years of age, between bottle fed and breast fed children. The actual effect is like 1.7 pounds at 5 years. Let’s assume that, which is why we see a blue line in the graph above at 1.7 with the word “assumed” next to it. You can decide who weighs more and if that’s a good thing or not depending on your politics.

OK, so that’s the underlying “truth” in the situation, but the point is, we don’t actually know it. We can only devise tests to estimate it, and this is where the graph comes in. The graph is showing us the distribution of our estimates of this effect if we have a crappy test.

So, imagine we have a crappy test – something like, we ask all our neighbors who have had kids recently how they fed their kids and how much those kids weighed at 5 years, and then we averaged the two groups. That test would be crappy because we probably don’t have very many kids overall, and the 5-year check-ups aren’t always exactly at 5 years, and the scales might have been wrong, or clothes might have been confusing the scale, and people might not have reported it correctly, or whatever. A crappy test.

Even so, we’d get some answer, and the graph above tells us that, if our tests are at a certain level of crappiness, which we will go into in a second, then very likely our estimate of the difference will come in between something like -22 pounds and +24 pounds. And the “most likely” answer would be the correct one, sure, but that doesn’t mean it’s all that likely to even come close – say within 2 pounds – of the “true effect”. In fact, if you make a band of width 4 centered around the “true effect” level, you’d definitely capture a smallish percentage of the total area under the curve. In fact, it looks like a good 45% of the area under the curve is in negative territory, so the chances are really very good that the test estimate, at this level of crappiness, would give you the wrong sign. That’s a terrible test!

Let’s be a bit more precise now about what we mean by “crappy.” The crappiness of our test is measured by its power, which is defined as “the probability that it correctly rejects the null hypothesis – i.e. the hypothesis that the “true effect” is zero – when it is false.” In other words, power quantifies how well the test can distinguish between the blue line above and the line at zero. So if the bell curve were really really concentrated at the blue line, then more of the total area under the curve would be on the positive side of zero, and we’d have a much better test. Alternatively, if the true effect were much stronger, say at 25 instead of 1.7, then even with a test this imprecise, the power would be much much higher because the bulk of the bell curve would be to the right of zero.

On the one hand, power estimates are done by researchers, and they are attempting to achieve a power of at least 0.80, or 80%, so the above power of 0.06 is indeed extremely low and our test is indeed very crappy by researching standards. But on the other hand, since researchers are expected to estimate their power to be at least 0.80, there’s probably fudging going on and we might be trusting tests to be less crappy than they actually are. Also, I am no expert on how to accurately estimate the power of a test, but there’s an example here, and in general it depends on your sample size (how many kids) and the actual effect size, as we have already discussed. In general it requires way more data to produce evidence of a small effect.

OK so now we have some general sense of what “crappiness” means. But what about the red parts?

Those are the “statistically significant” parts of the distribution. If we did our neighborhood kids test and we found an effect of 20 or -20, we’d be totally convinced, even though our test was crap. There are two take-aways from this. First, that “statistically significant” in the presence of a small actual effect and a crappy test means that we are wildly overestimating the effect. Second, that the red part on the left is about a third of the size of the red part on the right, which is to say that when we get a result that seems “statistically significant,” in the presence of a crappy test, it still has a one in four chance of being totally wrong.

In other words, when we have crappy tests, we just shouldn’t be talking about statistical significance at all. But of course, nobody can publish their results without statistical significance, so there’s that.

Greetings, friends! I’ve missed you all!

Since returning from her travels, Aunt Pythia has been continuously marveling in the wonders of flannel and wool, and has decided to knit up something along these Celtic lines:

Is that not gorgeous? I love the tangled-upedness of the center. And, of course, the doubly rainbow-ic aspects.

Here’s the thing, though: the pattern comes from the excellent book Celtic Charted Designs that Aunt Pythia is absolutely sure she has somewhere in her house, but can’t find. in fact she’s spent the good part of the morning searching her house. So if the column is a wee bit short and/or frustrated today, you’ll know why.

On to business! Aunt Pythia has lots of questions to answer, given that she was away last week, and she’s eager to get through some. But before she forgets,

please think of something Celtic

to ask Aunt Pythia at the bottom of the page!

By the way, if you don’t know what the hell Aunt Pythia is talking about, go here for past advice columns and here for an explanation of the name Pythia.

——

Dear Aunt Pythia,

What are your thoughts on this 401k article? 

Another Potential Pass At Legalizing Longterm Investments Not Going (well)

Dear APPALLING,

Wow, your sign off is longer than the body of you letter. Well done. OK so let me quote the heart of the problem fingered in the article:

Millions of people are clearly not using 401(k) plans as retirement accounts at all, and it’s a threat to their financial health.

I’d phrase it differently, namely:

In this day and age, working people need all their money, and 401(k) plans have proven to be saving strategies which are only realistic for well-off people, which entirely misses the point of how to deal with the older middle class in our country. Instead of relying on such wishful thinking, we should scrap the whole system, which by the way only serves to expand Wall Street’s power and give tax breaks to the rich, and we should instead expand Social Security.

Love,

Aunt Pythia

——

My Dearest Aunt Pythia,

In mid-90’s I completed class work on an MA in Applied Economics/Econometrics at a state school in California. Stupidly did not complete thesis (things got busy on political campaigns and such, and never got back to it). Like many I fell in love with economics, public policy, and their interrelations.

Now, many years later, my econometric/data/statistical modeling skills have aged with me and have become lost from my mind.

My first question is: What would you recommend as a refresher of data skills? I’m certain I don’t need to redo all I’ve done before- the skills are there but need to be refreshed and awakened (I assume/hope).

My second question is: Assuming the skills can be reawakened, what is my fastest and least costly method to enter data work? For programming many people create apps or small programs to be able to show code samples to prospective employers. Is there something analogous in data work? Should I build a model of aggregate demand changes from quantitative easing/M2 changes and shrinking consumer credit (from institutional rule changes) and post it online to show skills? I’ve also been looking at the data science certificate from Coursera/Johns Hopkins, but don’t know that it would matter on a resume. My old university requires restart for the old M.S. (which I understand), so should I pursue a new M.S. in current state school (UMUC has a Masters Data Analytics, which I think I would enjoy anyway, but is pretty pricy).

Anyway, hoping you, my dear Aunt, will have some advice for a data enthusiast with dusty data skills to freshen skills and move into analysis. Oh, I should mention that I am taking a lot of computer programming classes lately to get skills in that area as well.

Dusty Skills

Dear Dusty Skills,

Please don’t take this the wrong way, but the very first thing you need to do when applying for any data job is to use a spell-checker. I must have corrected 5 words in your letter.

Next, although I agree that a Coursera certificate might not be considered all that important, the skills you hopefully acquire with such a certificate would be. And yes, I do suggest you build something with your skills, although tackling QE seems both onerous and unlikely. I’d do something less abstract if I were you, and set up a website portfolio so everyone can see your mad skillz. Oh, and you might want to take a look at my book, Doing Data Science, although you might be past that stuff already.

Good luck!

Aunt Pythia

——

Aunt Pythia,

I’m a father whose daughter is applying to colleges. I also work at a college, as does my wife. And like many employees in academia, I’ve been following with horror the reports of college rape: the under-reporting, the Judicial review boards, the administrators eager to downplay the problem, etc.

As a father the horror easily spills over into terror. I want my daughter to grow into the challenges of living away from home; I want her to learn in a enlightening, and encouraging environment; and I want her to have fun. I also want her to be safe.

I am outraged at the clueless administration officials and public safety officers who say “girls should not go to parties, or drink,” all the while wanting to scream at my daughter “don’t go to parties or drink.”

How can I have a meaningful conversation about going off to college, learning, having fun, but be safe, without sounding like *those* administrators?

Worried In Academia

——

Dear Worried,

I went to college in the early 1990’s at UC Berkeley. My first year there was the scene of multiple Gulf War protests, and about 3 or 4 street riots, streaming by my dorm near Telegraph Ave, during which me and my two roommates didn’t dare leave our room. In my sophomore year we heard the Rodney King verdict and it was chaos in the streets for a few days.

I guess what I’m saying is that, due to the obviously volatile and threatening mood of the campus and neighboring towns back then, I was always on alert, and defensive. All of my friends took self-defense classes, and I carried around pepper spray, in my right hand, and my keys in my left, whenever I walked home at night. I biked places so I could get away more quickly. It was my assumption that I would need to protect myself and that there were people who would hurt me if I didn’t. I’m not saying there weren’t people who drank too much and got themselves vulnerable – in fact while I was there, there were multiple burning deaths in fraternities that did crazy things with couches – but that I personally would never have been involved with such stuff. For that matter there was a lot of campus rapes, which we knew about, and the police knew about, and kept us going to our self-defense classes.

Nowadays, we have a very different notion, and also a different reality, which is mostly a good thing, but has weird consequences. One of them is a sense that colleges are safe places, which they most certainly are not. College administrations have come a long way on marketing their campuses as attractive and safe, but it’s just a marketing thing, and it sends confusing and deeply mixed messages to parents and kids, which pisses me off. At the end of the day, when you go to college, you are an adult, and you need to be responsible for your safety, which means not getting out of hand, and keeping trustworthy friends close to you to make sure you don’t, and to make sure they don’t.

So, and I know this is a tough issue, but my advice is to tell your daughter to learn to size up the energy of a party, and see if dangerous things are happening, and to have a group of friends at all times that are looking out for you, and who you are looking out for, and to take self-defense classes, and to carry mace or at least a siren for when you travel alone at night.

Also, and this is actually the most important piece of advice: get your daughter to drink with you a few times, before she goes to college, so she’ll know what it feels like to have too much. The most educational night of my life was a night in the summer before college, when my dad got me and my friend Becky puking drunk. I never let that happen again, because I knew when I’d had too much. I think far too many kids get to college never having been allowed to go overboard with drinking, so they do it for the first time with strangers. Bad idea!

One last thing. I think that in the next couple of decades, the police will learn how to adequately and sensitively deal with rapes, and when that happens we won’t need to worry as much about campus police forces, which are totally inadequate and rife with conflicts of interest. But obviously you don’t have two decades to wait for that to happen, since your daughters are going to college now. Plus, I may be just being unrealistic about the progress we could make.

I hope that helps!

Aunt Pythia

——

Dear Aunt Pythia,

I keep hearing about how rampant sexism is in STEM fields, particularly in tech workplaces, where I can see myself heading toward after college. It’s really discouraging, especially since I think I experience some sort of sexism in my classes here in college (in computer science way more than in math), and even worse, I can’t seem to speak up because this kind of sexism is really subtle (i.e. a guy got angry with me for his incompetence with a certain technology. I would’ve spoke up but the assignment was worth so little.)

These hurtful incidences just build up over time, and whenever I vent to my friends, some “brush it off” as it not being serious. My parents told me that what I experience here in college won’t be any different in the workplace.

So as I search for summer internships, I carry this cloud of insecurity and doubt. Should I go forward? What’s the point? Breaking gender barriers sounds great, but my God, there are so many women out there who choose to leave because the barrier is so high and strong. I can easily see myself leaving the tech industry because its stubborn lack of support toward women and its more harmful PR farces showing that they do “support women.” Is it ever worth it? How do I reconcile with this?

Unsure of the future

Dear Unsure,

My motto is, celebrate the victories and ignore the defeats. Where by “victories” we mean “getting a computer program to work” and by “defeats” we mean “some insecure guy took out his frustration on me because I’ve got boobs.”

In other words, don’t think about yourself or your actions as A Woman In STEM. Instead, think about what your personal goals are, and what interests you, and what you’d like to learn about or accomplish. Make it an internal conversation about your wants and needs and passions, rather than an external conversation about how you look to other people. And if your internal voice is telling you to leave STEM, then by all means do it, but if not, don’t let those fuckers get you down. Do it because it’s cool and you love it, not because some assholes do or do not have an agenda for you or an ego riding on what and how you do things. Separate the two issues and it will help, because math and computer science are really cool.

And because it’s not always possible to totally ignore the defeats, I’d also encourage you to find better friends who will let you vent and will vent along with you. What’s up with them?!

Good luck, for reals! Keep me posted!

Aunt Pythia

——

Please submit your well-specified, fun-loving, cleverly-abbreviated question to Aunt Pythia!

Click here for a form.

I came across an interesting poster that’s been put up on a few lampposts on my street.  It rather pathetically offers a $2,000 reward for information leading to the arrest of George Welch for operating a bucket shop in New York.

This got me thinking about the notion of vigilante justice and the failure of the Department of Justice, or pretty much anyone else, to prosecute people on Wall Street for the financial crisis.  What if more people, frustrated by the lack of prosecutorial interest in Wall Street, decided to take matters into their own hands?  What if there was an outbreak of bounties being put on the heads of wrong-doing bankers so that some street justice could be applied, as the person posting this poster appeared to be seeking?

Wikipedia has a surprisingly elegant definition of vigilante justice as:

the idea that adequate legal mechanisms for criminal punishment are either nonexistent or insufficient. Vigilantes typically see the government as ineffective in enforcing the law; such individuals often claim to justify their actions as a fulfillment of the wishes of the community.

The mood of the community I follow on Twitter and around the web certainly resonates with this definition.  A lot of ink has been spilled on how the government has failed to enforce the law with respect to the Financial Crisis and that a collection of the wrong-doers, big and small, have gotten away with it, at the expense of the rest of us. Occupy, obviously, was an expression of frustration about the lack of law enforcement, though it did not have a vigilante component.  Growing dissatisfaction with our government is manifesting itself in many places –  including the most recent anti-incumbent mid-term elections.  And despite whistleblowers, such as Alayne Fleischmann or Edward Snowden naming names and institutions, nothing seems to change.

There’s a long, (not so) proud tradition of vigilante justice in our country (and, of course dating back to societies much older than our country).  Vigilante justice stories in the American frontier were tales of how people bound together to fight back against lawlessness.  In my youth, movies like Billy Jack, Death Wish or Rambo portrayed the desperate, yet justified (?), actions of people who had had enough with lawlessness and weren’t going to take it anymore.  The real life story of Bernhard Goetz was often portrayed in a similar fashion in the tabloids.  Today, vigilante themed movies and shows, like Batman or Dexter, are everywhere.  In the hands of the right storyteller, vigilante justice has a visceral appeal.

Vigilantism also has an awful, dark history in the US and elsewhere, including the legacy of lynchings in our not too distant past.  As angry as many of us have been about the aftermath of the financial crisis and the sense that the government has been bought by Wall Street money, the notion of vigilantism is still scary.  Who will really be making decisions about right and wrong if people take law into their own hands – the downtrodden and righteous, or the powerful and corrupt?

Upon doing a little internet research into the Wanted! poster on my street, I discovered that it wasn’t exactly a call for justice from a poor aggrieved investor in some bucket shop scheme.  Perhaps the name of the firm – Hooke, Lyon and Cinquer – should have given it away. Instead, this poster seems to be a reference to a piece of strange art by a early 20th Century artist named Marcel Duchamp.  Duchamp was a mysterious man and many people had a hard time understanding what he was getting at with his art.  He made this poster, with a picture of himself as the wanted man, but critics are unclear about what he was saying with it.

Frankly, I have no idea why someone is posting them on my street now, almost 50 years after the original artist’s death.  It seems noteworthy, somehow, that Duchamp’s poster originated in the lawless, Boardwalk Empire days of the 1920s, but I’m not sure why exactly.

I realized that I had been pranked by the poster, because I was sympathetic to a story about a small investor being burned by a Wall Street con artist, and a bounty on the scammer’s head seemed like an innovative, though unlikely, solution to the failure of law enforcement.   So what was the point of this prank by Duchamp and by his new imitator on my street?

I’m not an art expert in any way (particularly not an expert on Dadaism that Duchamp helped originate), but my interpretation of today’s poster is that vigilantism is, itself, a prank.  Despite fantasies of lawless bankers being tarred and feathered, what I (and I assume others) really want is a justice system that works, not one where people have to take the law into their own hands.  In an excellent article written in response to the Ferguson troubles, Kareem Abdul Jabbar argues that we should use our rage at injustice to work to fix the system, and he has a point.  Vigilantism is an illusion of justice… but the sense that the system isn’t working is still real.  Maybe there’s an alternative interpretation of Duchamp’s prank:  Unless more people within the system actually start to enforce the law against the powerful (as folks like Judge Rakoff or Ben Lawsky have shown is possible), then justice and government will lose their authority and become an illusion.

It’s art, so I don’t know that there is a definitive interpretation, but Duchamp’s piece tricked me and challenged me and pushed me, so I like whichever of these interpretations I apply.