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Data Without Borders: datadive weekend!
I’m really excited to be a part of the datadive this weekend organized by Data Without Borders. From their website:
Selected NGOs will work with data enthusiasts over the weekend to better understand their data, create analyses and insights, and receive free consultations.
I’ve been asked to be a “data wrangler” at the event, which means I’m going to help project manage one of the projects of the weekend, which is super exciting. It means I get to hear about cool ideas and techniques as they happen. We’re expecting quite a few data scientists, so the amount of nerdiness should be truly impressive, as well as the range of languages and computing power. I’m borrowing a linux laptop since my laptop isn’t powerful enough for the large data and the crunching. I’ve got both python and R ready to go.
I can’t say (yet) who the N.G.O. is or what exactly the data is or what the related questions are, but let me say, very very cool. One huge reason I started this blog was to use data science techniques to answer questions that could actually really matter to people. This is my first real experience with that kind of non-commercial question and data set, and it’s really fantastic. The results of the weekend will be saved and open.
I’ll be posting over the weekend about the project as well as showing interim results, so stay tuned!
Wall Street and the protests
Today I want to update you on my involvement with the Occupy Wall Street protest and also make an observation about the defensive behavior we see by the Wall Streeters themselves.
Update
Yesterday after work I went back to the protests and looked around to offer a teach-in. Unfortunately it hadn’t been sufficiently organized: the contact who had originally invited me wasn’t around, and hadn’t confirmed with me on email, and nobody else knew anything. It was also very windy, threatening rain, and the noise of the drumming was overbearing. There were drumming circles on two of the four corners of the square, and in the other two corners there were already meetings going on. It would be great if the protests could restrict the drumming area so that people could actually talk.
However, I kind of suspected this would happen, so I wasn’t disappointed. I handed out some flyers with a few friends that met me down there, and I met a few new really interesting and engaging people. I got re-invited to give a teach-in by a very nice man named Rock, who took my information. Rock suggested a daytime talk sometime around noon, and this sounds about right. Hopefully this will pan out, but even if it doesn’t now I have a flyer to distribute and it’s a conversation starter if nothing else. One of my friends also suggested having a t-shirt made with the phrase, “ask me about the financial system” printed on it. I think this is a great idea. I will go back down and be involved when I can make the time.
Also, I wanted to share Matt Taibbi’s column about the protest. His five top demands have a lot in common with the ones we came up with here.
Act Crazy
You know how some people win fights even though they’re not big or strong? They act totally crazy and angry, and it works because it confuses their opponents. This is what I think the tactic of the big bosses on Wall Street is right now. They’ve got Tim Geithner talking about it:
“They react to what is pretty modest, common sense observations about the system as if they are deep affronts to the dignity of their profession. And I don’t understand why they are so sensitive,” Geithner said at a forum hosted by The Atlantic and the Aspen Institute.
We’ve also seen Paul Krugman address this:
Last year, you may recall, a number of financial-industry barons went wild over very mild criticism from President Obama. They denounced Mr. Obama as being almost a socialist for endorsing the so-called Volcker rule, which would simply prohibit banks backed by federal guarantees from engaging in risky speculation. And as for their reaction to proposals to close a loophole that lets some of them pay remarkably low taxes — well, Stephen Schwarzman, chairman of the Blackstone Group, compared it to Hitler’s invasion of Poland.
The overall idea is to act like they are the victims somehow. Actually there’s another article in Bloomberg about the Wall St suffering, which I find fascinating as a phrase, and which contains passages like this one:
Bankers aren’t optimistic about those gains. Options Group’s Karp said he met last month over tea at the Gramercy Park Hotel in New York with a trader who made $500,000 last year at one of the six largest U.S. banks.
The trader, a 27-year-old Ivy League graduate, complained that he has worked harder this year and will be paid less. The headhunter told him to stay put and collect his bonus.
Here’s the thing. They are suffering, in exactly the same way that a child who is spoiled suffers when they are told they can’t get a toy in a store that they want even though they have one at home just like it. But that’s not real need, that’s a temper tantrum. It’s the parents’ responsibility to ignore that kind of posturing and establish reasonable expectations. But the analogy becomes kind of painful here, because who are the parents?
I guess you’d want them to be the government, or the regulators, but the problem is that those groups have shown the same lack of imagination (or fear) of a new world as the people on Wall Street.
So even though the protests are disorganized and sometimes annoying, the very fact that they are putting pressure on the system to fundamentally change is why I will continue to support them.
Bayesian regressions (part 2)
In my first post about Bayesian regressions, I mentioned that you can enforce a prior about the size of the coefficients by fiddling with the diagonal elements of the prior covariance matrix. I want to go back to that since it’s a key point.
Recall the covariance matrix represents the covariance of the coefficients, so those diagonal elements correspond to the variance of the coefficients themselves, which is a natural proxy for their size.
For example, you may just want to make sure the coefficients don’t get too big, or in other words there’s a penalty for large coefficients. Actually there’s a name for just having this prior, and it’s called L2 regularization. You just set the prior to be 
You’re going to end up adding this prior to the actual sample covariance matrix as measured by the data, so don’t worry about the prior matrix being invertible (but definitely do make sure it’s symmetrical).
Moreover, you can have many different priors, corresponding to different parts of the covariance matrix, and you can add them all up together to get a final prior.
From my first post, I had two priors, both on the coefficients of lagged values of some time series. First, I expect the signal to die out logarithmically or something as we go back in time, so I expect the size of the coefficients to die down as a power of some parameter. In other words, I’ll actually have two parameters: one for the decrease on each lag and one overall tuning parameter. My prior matrix will be diagonal and the 
My second prior was that the entries should vary smoothly, which I claimed was enforceable by fiddling with the super and sub diagonals of the covariance matrix. This is because those entries describe the covariance between adjacent coefficients (and all of my coefficients in this simple example correspond to lagged values of some time series).
In other words, ignoring the variances of each variable (since we already have a handle on the variance from our first prior), we are setting a prior on the correlation between adjacent terms. We expect the correlation to be pretty high (and we can estimate it with historical data). I’ll work out exactly what that second prior is in a later post, but in the end we have two priors, both with tuning parameters, which we may be able to combine into one tuning parameter, which again determines the strength of the overall prior after adding the two up.
Because we are tamping down the size of the coefficients, as well as linking them through a high correlation assumption, the net effect is that we are decreasing the number of effective coefficients, and the regression has less work to do. Of course this all depends on how strong the prior is too; we could make the prior so weak that it has no effect, or we could make it so strong that the data doesn’t effect the result at all!
In my next post I will talk about combining priors with exponential downweighting.
Koo: don’t be surprised by the crappy economy
First I wanted to thank you for the wonderful comments I’ve been enjoying and compiling from my last post about what’s corrupt about the financial system and what should be done about it. Even if I don’t end up doing the teach-in (hopefully I will! In any case I’ll go down there, even if it’s just to try to set up the teach-in for a later date) I think this is a really fantastic and important discussion. I’m putting together a final list of issues tonight and I think I’ll make a flyer to bring tomorrow, so if I don’t actually conduct the teach-in (yet) I’ll at least be able to give the info booth the flyers.
And it’s not too late! Please keep the comments coming.
Today I want to start a discussion on Richard Koo’s book, which is about Japan’s so-called “lost decade” (a reader suggested this book to me, and it’s fascinating, so thanks! And please feel free to make more suggestions for my reading list).
You can actually get a pretty good overview of his book by watching this excellent interview by Koo. For those of you, like me, whose sound doesn’t work on their computers, here’s his basic thesis:
- After the housing bubble in Japan burst, a bunch of firms, banks and otherwise, became technically insolvent. This meant that, although they had cash flow, they owed more than their assets.
- Because they were insolvent, they didn’t maximize profits like in normal times; instead they minimized debts.
- In other words, they didn’t borrow money to grow their businesses, like you’d expect in normal circumstances, which is proved by looking at data showing that corporate borrowing went down even as interest rates lowered to zero.
- The CEO’s didn’t talk about this because they don’t want anyone to know they’re insolvent!
- Investors are also somewhat blind to this, because they typically look at growth and cash flow issues.
- Japan’s government made massive investments in order to cover the lack of private investments.
- Rather than this being a mistake, this was absolutely essential to the Japanese economy and prevented a massive depression.
- Moreover, the idea that Japan had a lost decade is false: actually, there was a lot going on in that decade (actually, 15 years) but people didn’t see it. Namely, the balance sheets were slowly improved over the entire economy.
- This is a lesson for us all: any time there’s a massive credit bubble which breaks, we can expect a balance sheet recession where behavior like this is the rule. The U.S. economy right now is an example of this.
I have a few comments about this. I wanted to mention that I’m only about halfway through the book so it’s possible that Koo addresses some of these issues but on the other hand the book was published in 2009 but was clearly written before the U.S. credit crisis was really full-blown:
- A friend of mine who recently traveled to Japan noted that the people there live extremely well. In fact, if he hadn’t been told that their country has been in recession for nearly twenty years then he’d have never guessed it. This supports Koo’s claim that the Japanese government absolutely did the right thing by bankrolling the economy when it did. It also brings up a very basic question: how do we measure success? And why do we listen to economists when they tell us how to define success?
- Not every country can do what Japan did in terms of investing in its economy, although the U.S. probably can. In other words, it depends on how other countries see your credit risk whether you can go ahead and bail out an entire economy.
- Some of the businesses in the U.S. are clearly not technically insolvent; we’ve already seen ample evidence of cash hoarding. On the other hand, I guess if sufficiently many are, then the overall environment can be affected like Koo describes.
- In general it makes me wonder, how many of the firms out there today are technically insolvent? How insolvent? How long will it take for those that are to either fail outright or pay back their loans? If we go by this article, then the answer is pretty alarming, at least for the banks.
In general I like Koo’s book in that it introduces a new paradigm which explains something as totally self-evident that had been mysterious. It’s pretty bad news for us, though, for two reasons. First, it means we could be in this (by which I mean stagnant growth) for a long, long time, and second, considering the hyperbolic political situation, it’s not clear that the government will end up responding appropriately, which means we may be in it for even longer.
What’s wrong with Wall Street and what should be done about it?
I am trying to figure out the top five (or so) most important corrupt and actionable issues related to the financial system. I’m going to compile this list in order to conduct a “teach-in” at the Occupy Wall Street protest next week. The tentative date is Wednesday, October 12, at 5:30pm.
I’d love to hear your thoughts: please tell me if I’m missing something or got something wrong or left something out.
The list I have so far:
- Investment bankers trading their books and taking outrageous risks which lead to government-backed bailouts because they are “too big to fail”. The related action in the U.S. might be the “Volcker rule” (i.e. reinstating something like Glass-Steagall); unfortunately it’s being watered down as you read this.
- Ratings agencies in collusion with their clients. The actions here would be changing the pay structure of the ratings agencies and opening up the methods, as well as having better regulatory oversight. We also need to change the structure of ratings agencies, and either make it easier to form an agency or make the agencies that already exist and have government protection actually accountable for their “opinions”.
- SEC and other regulators in collusion with the industry. The action here would be to nurture and maintain an adversarial relationship between regulators and bankers. We’ve seen too many people skip from the SEC to the banks they were regulating and then back. There should be rules against this (how about a minimum time requirement of 5 years between jobs on the opposite sides?). There should also be much better funding for the SEC and the other regulators, so they can actually meet their expanded mandate.
- Conflict of interest issues from economists and business school professors. If you’ve seen “Inside Job” then you’ll know all about how professors at various universities use their credentials to back up questionable practices. Moreover, they are often not even required to expose their industry connections when they do expert witnessing or write “academic” papers. The action here would be, at the very least, to force full disclosure for all such appearances and all publications. I’ve heard some good news in this direction but there obviously should be a standard.
- Rampant buying of politicians and influence of lobbyists from the financial industry. This is maybe more of a political problem than a financial one so I’m willing to chuck this off the list. Please tell me if you have something else in mind. Someone has suggested the opaque and elevated pension fund management system. Although I consider that pretty corrupt, I’m not sure it’s as important as other issues to the average person. I’m on the fence.
Saturday afternoon quickie
Two things.
- If I see another fucking article about how the world is going to miss Steve Jobs I’m going to puke. He made and sold overpriced gadgets for fucks sake! It’s hero worship plain and simple, maybe even a sick cult.
- I am happy that I’ve been invited to give a “teach-in” at Occupy Wall Street next Wednesday at 5:30 (tentative date and time). I’ve promised an overview of the 5 top corrupt things in the financial system. I’d really appreciate your thoughts: what is your top 5 list? I want them to be both important and relatively actionable. So far I’ve got:
- Volcker rule (i.e. reinstating something like Glass-Steagall); it’s being watered down as you read this.
- Ratings agencies in collusion with their clients
- SEC and other regulators in collusion with the industry
- Rampant buying of politicians and influence of lobbyists from the financial industry
- Incredibly poor incentives for the individuals in the industry, both in terms of salary and whistleblowing
Habits
This is a guest post by my friend Tara Mathur:
I don’t need to read Tiger Mother to know that I don’t have one. I don’t remember either of my parents putting a lot of pressure on me to do things – even to study, although I developed that habit on my own.
As kids we develop some habits on our own, but we pick up a lot of habits from our parents.
We learn habits from our parents in a few ways. One is by mirroring them. For example, my parents have always read in bed before going to sleep and so have I; it’s so natural to me that until I got married I thought this was something everyone did.
Another is by having our parents make us do something repeatedly. For example, when we first brushed our teeth it probably seemed like a pain to do, but our parents kept making us do it, and it became automatic.
How can we cultivate new habits as adults?
(And am I the only one who associates the word “will-power” with pain and failure? People use that word when they’re talking about doing something really hard, against their natural tendencies. I hear that word and think, how is this gonna last?)
In the last few years I’ve become a big fan of a blog called Zen Habits written by Leo Babauta. He’s made big positive changes in his life – getting out of debt, quitting smoking, running marathons, starting a successful writing career – by focusing on habits rather than goals. Even though big goals are sexy and easy to get excited about, it’s the daily habits, built up baby step by baby step, which last and which comprise most of our life. By definition, when something is a habit we don’t have to rely on willl-power to stick with it. It’s effortless, automatic behavior. Leo emphasizes starting small and focusing on one habit at a time.
This could apply to any positive change we’d like to make in our life. BJ Fogg, a human behavior expert who runs the Persuasive Technology Lab at Stanford, sums up the three steps to cultivate a new habit as follows:
- Make it tiny. To create a new habit, you must first simplify the behavior. Make it tiny, even ridiculous. (examples: floss one tooth, walk for three minutes, do two push-ups)
- Find a spot. Find a spot in your existing routine where this tiny new behavior could fit. Put it after some act that is a solid habit for you, like brushing teeth or eating lunch. One key to a new habit is this simple: you need to find what it comes after.
- Train the cycle. Now focus on doing the tiny behavior as part of your routine – every day, on cycle. At first you’ll need reminders. But soon the tiny behavior will get more automatic. Keep the behavior simple until it becomes a solid habit. That’s the secret to success.
That’s it! He says. Just keep your tiny habit going. Believe in baby steps. Eventually it will naturally expand to the bigger behavior, without much effort.
(There are other tricks too. I’ve also read that you’ll pick up a habit more quickly if you surround yourself with people who already have the habit you want — though I’m not sure if it will last when you’re no longer around those people. Try it and see what works.)
Financial Terms Dictionary
I’ve got a bunch of things to mention today. First, I’ll be at M.I.T. in less than two weeks to give a talk to women in math about working in business. Feel free to come if you are around and interested!
Next, last night I signed up for this free online machine learning course being offered out of Stanford. I love this idea and I really think it’s going to catch on. There are groups here in New York that are getting together to talk about the class and do homework. Very cool!
Next, I’m going back to the protests after work. The media coverage has gotten better and Matt Stoller really wrote a great piece and called on people to stop criticizing and start helping, which is always my motto. For my part, I’m planning to set up some kind of Finance Q&A booth at the demonstration with some other friends of mine in finance. It’s going to be hard since I don’t have lots of time but we’ll try it and see. One of my artistic friends came up with this:
Finally, one last idea. I wanted to find a funny way to help people understand financial and economic stuff, so I thought of starting a “Financial Terms Dictionary”, which would start with an obscure phrase that economists and bankers use and translate it into plain English. For example, under “injection of liquidity” you might see “the act of printing money and giving it to the banks”.
I’d love comments and suggestions for the Financial Terms Dictionary! I’ll start a separate page for it if it catches on.
Bayesian regressions (part 1)
I’ve decided to talk about how to set up a linear regression with Bayesian priors because it’s super effective and not as hard as it sounds. Since I’m not a trained statistician, and certainly not a trained Bayesian, I’ll be coming at it from a completely unorthodox point of view. For a more typical “correct” way to look at it see for example this book (which has its own webpage).
The goal of today’s post is to abstractly discuss “bayesian priors” and illustrate their use with an example. In later posts, though, I promise to actually write and share python code illustrating bayesian regression.
The way I plan to be unorthodox is that I’m completely ignoring distributional discussions. My perspective is, I have some time series (the 
A “bayesian prior” can be thought of as equivalent to data you’ve already seen before starting on your dataset. Since we think of the signals (the 
The information you need to know about the
Let me illustrate with an example. Suppose you are working on the simplest possible model: you are taking a single time series and seeing how earlier values of 
What is your prior for this? Turns out you already have one (two actually) if you work in finance. Namely, you expect the signal of the most recent data to be stronger than whatever signal is coming from older data (after you decide how many past signals to use by first looking at a lagged correlation plot). This is just a way of saying that the sizes of the coefficients should go down as you go further back in time. You can make a prior for that by working on the diagonal of the covariance matrix.
Moreover, you expect the signals to vary continuously- you (probably) don’t expect the third-from recent variable 
In my next post I’ll talk about how to combine exponential down-weighting of old data, which is sacrosanct in finance, with bayesian priors. Turns out it’s pretty interesting and you do it differently depending on circumstances. By the way, I haven’t found any references for this particular topic so please comment if you know of any.
My friend the coffee douche
About a year ago or so, I went with my friend to a new coffee store in lower Manhattan that he was super excited about. He knew the name of their espresso machine (the Slayer) and kept going on about how amazing the espresso made from this machine must be, if done right. I was happy to go, first because I needed coffee and second because I just like my friend and like it when people get really into things. On the way there I told him that the way he was waxing poetic about the Slayer really defined him as an all-out “coffee douche”. He took it well- in fact I think he actually loved the title. Coffee douches rarely get rewarded with titles, I realized.
I used to be a coffee douche myself. Or at least a potential coffee douche. I worked at Coffee Connection in my youth, which was eventually bought out by Starbucks but in its time gave lots of people in the Boston area pretty good coffee. I hung with the owner, especially once I decided to go to Berkeley, because that’s where he went for undergrad and where he learned to love good coffee (he told me he fell in love at Istanbul Express, I wonder if that place still exists). At some point I knew how many seconds of roasting produced each style (I never liked Italian Roast myself- too burnt) and the characteristics of the different coffees from all over the world (mmm… Sumatra).
Over time, though, I lost it. Something about having kids. I’m now at the level of carrying around Nodoz in my purse just in case I’m traveling and there’s no coffee machine in the hotel room (or in case those tiny little packages of grounds are insufficient). I still enjoy a good cup of Sumatra but I’m almost equally happy going to 7 Eleven. So you can see that coffee douchery is at best a fond memory for me.
When we got to the store, we were immediately asked at the door if we were “press”. Umm, no, what’s going on? It turned out that Sylvia was the guest barista! She was 3 time Brazilian pull champion!! I inferred that this meant there are actually competitions for making espresso. My friend was getting more and more excited and agitated. We got our pictures taken before and after the coffee drinks arrived. Or rather, our cups and saucers were- I think we may have only accidentally entered a frame or two. Sylvia was very gracious and hard-working at the same time. I think I managed to shake her hand, just for the celebrity moment of it all.
As an aside, I noticed something about the whole coffee movement thing when I was checking out Sylvia and her methods. Everything there has a fetishized whiff to it. The coffee machine was the Slayer, the various implements were wooden of some kind of hardwood that they were happy to explain in detail, and although I can’t remember all the names of the implements, I got the distinct impression that there may be a sex shop in the back room with leather and wooden tools very similar to the coffee tools. Maybe just me.
Here’s a close-up sexy shot of the Slayer (if you look carefully at the reflections you will note at least 3 people there admiring its shiny round parts), taken from the website of RBC coffee:
I don’t think I’ve ever been under such pressure to enjoy my espresso, but it was pretty good (I think). Near the end of drinking it, we seemed to be peppered with technical questions from the people there, including the owner of the store, the owner of the coffee plantation that supplied the store, and the guy who roasted the coffee beans. It was a triumvirate of coffee! I was glad I had my coffee douche with me!! He impressed them with his idiosyncratic knowledge (I remember his sympathy combined with pride when he mentioned that he was aware that there were laws against roasting in Manhattan but not in Brooklyn, so did they roast in Brooklyn? They did).
When I left, I was invigorated. Here are these people, completely obsessed and fascinated with coffee and everything pertaining to coffee. In some sense it struck me as a waste of time, but in a larger sense it was very very cool. That’s what’s interesting and fun about humans, after all, that they get totally nerdy and into things that other people can’t relate to, and they really improve our knowledge as a community about the best way to do that thing. There are probably people somewhere who are as into park benches as these guys are into coffee, and thanks to them the park benches are getting more and more comfy and beautifully designed and long-lasting, at least if you know where to go for really excellent park benches.
Data science: tools vs. craft
I’ve enjoyed how many people are reading the post I wrote about hiring a data scientist for a business. It’s been interesting to see how people react to it. One consistent reaction is that I’m just saying that a data scientist needs to know undergraduate level statistics.
On some level this is true: undergrad statistics majors can learn everything they need to know to become data scientists, especially if they also take some computer science classes. But I would add that it’s really not about familiarity with a specific set of tools that defines a data scientist. Rather, it’s about being a craftsperson (and a salesman) with those tools.
To set up an analogy: I’m not a chef because I know about casserole dishes.
By the way, I’m not trying to make it sound super hard and impenetrable. First of all I hate it when people do that and second of all it’s not at all impenetrable as a field. In fact I’d say it the other way: I’d prefer smart nerdy people to think they could become data scientists even without a degree in statistics, because after all basic statistics is pretty easy to pick up. In fact I’ve never studied statistics in school.
To get to the heart of the matter, it’s more about what a data scientist does with their sometimes basic tools than what the tools are. In my experience the real challenges are things like
- Defining the question in the first place: are we asking the question right? Is an answer to this question going to help our business? Or should we be asking another question?
- Once we have defined the question, we are dealing with issues like dirty data, too little data, too much data, data that’s not at all normally distributed, or that is only a proxy to our actual problem.
- Once we manhandle the data into a workable form, we encounter questions like, is that signal or noise? Are the errorbars bigger than the signal? How many more weeks or months of data collection will we need to go through before we trust this signal enough to bet the business on it?
- Then of course we go back to: should we have asked a different question that would have not been as perfect an answer but would have definitely given us an answer?
In other words, once we boil something down to a question in statistics it’s kind of a breeze. Even so, nothing is ever as standard as you would actually find in a stats class – the chances of being asked a question similar to a stats class is zero. You always need to dig deeply enough into your data and the relevant statistics to understand what the basic goal of that t-test or statistic was and modify the standard methodology so that it’s appropriate to your problem.
My advice to the business people is to get someone who is really freaking smart and who has also demonstrated the ability to work independently and creatively, and who is very good at communicating. And now that I’ve written the above issues down, I realize that another crucial aspect to the job of the data scientist is the ability to create methodology on the spot and argue persuasively that it is kosher.
A useful thing for this last part is to have broad knowledge of the standard methods and to be able to hack together a bit of the relevant part of each; this requires lots of reading of textbooks and research papers. Next, the data scientist has to actually understand it sufficiently to implement it in code. In fact the data scientist should try a bunch of things, to see what is more convincing and what is easier to explain. Finally, the data scientist has to sell it to everyone else.
Come to think of it the same can be said about being a quant at a hedge fund. Since there’s money on the line, you can be sure that management wants you to be able to defend your methodology down to the tiniest detail (yes, I do think that being a quant at a hedge fund is a form of a data science job, and this guy woman agrees with me).
I would argue that an undergrad education probably doesn’t give enough perspective to do all of this, even though the basic mathematical tools are there. You need to be comfortable building things from scratch and dealing with people in intense situations. I’m not sure how to train someone for the latter, but for the former a Ph.D. can be a good sign, or any person that’s taken on a creative project and really made something is good too. They should also be super quantitative, but not necessarily a statistician.
“Our organization does not reward failure” – Koch
You have to check out this Bloomberg article about Koch Industries. Although it rambles a bit at times, it’s absolutely mesmerizing and horrible. Here’s the main premise, which bizarrely comes near the end of the article:
For six decades around the world, Koch Industries has blazed a path to riches — in part, by making illicit payments to win contracts, trading with a terrorist state, fixing prices, neglecting safety and ignoring environmental regulations. At the same time, Charles and David Koch have promoted a form of government that interferes less with company actions.
The phrase “our organization does not reward failure” comes from a book in 2007 written by one of the Koch brothers where he somehow fails to discuss a pipeline explosion that had recently killed two teenagers in Oklahoma:
The 570-mile-long pipeline carrying liquid butane from Medford, Oklahoma, to Mont Belvieu, Texas had corroded so badly that one expert, Edward Ziegler, likened it to Swiss cheese. The company didn’t give 40 of the 45 families near the explosion site — including the Smalley and Stone families — any information about what to do in case of an emergency, the NTSB wrote.
The article is complete, in that it even has a spiteful twin brother of one of the Koch brothers appearing to give away his brothers for stealing.
The Senate held hearings in May 1989 after Bill Koch, David Koch’s twin brother, told a U.S. Senate special committee on investigations that Koch Industries was stealing oil on American Indian reservations, cheating the federal government of royalties.
The investigators caught Koch Oil’s employees falsifying records so that the company would get more crude than it paid for, shortchanging Indian families, Elroy said. Koch’s records showed that the company took 1.95 million barrels of oil it didn’t pay for from 1986 to 1988, according to data compiled by the Senate.
One thing that fascinating to me is that there are two whistle-blowers in the story, both women who were essentially fired for having ethics (one reported on bribes and the other on toxic gas dumping, both sued the company after leaving). Doesn’t it seem like women are more often whistle-blowers? Especially if you consider the fact that high ranking people in these kinds of companies with access to the kind of information that whistle-blowers need to uncover fraud are typically men.
These Koch brothers are seriously despicable, and really all they seem to care about is the ability to make money without having to worry about rules, even basic rules of morality. They currently largely bankroll the Tea Party. It’s a scary thought that I could someday live in a country whose president owes a favor to these guys.
First day of calculus class
Last night I had dinner with a friend who is a post-doc in math, and she was mentioning that her students, especially in the lower-level calculus classes, generally don’t refer to her as “professor.” This would be fine since she’s not yet a professor, but she also mentioned they do refer to graduate student men in the same department as professor. She’s a young looking woman, and my guess is they simply don’t know better. Here’s what my advice to her was (and as usual, I’d give this advice to both men and women).
On the first day of class, introduce yourself and put your name on the board, explain when and where you got a Ph.D., what your field of research is, what your current job is, as well as office hours and homework policies. In addition, wear a button-down shirt that first day of class. It’s kind of ridiculous but it works, in the sense that the students will be more impressed with you, which translates into them behaving more respectfully.
Moreover, it’s totally appropriate and not manipulative to explain your credentials. It’s probably most important for calculus, because generally those students don’t really want to be there, at least not all of them. Upper level classes contain students who are more psyched about math and eager to like their professors. I say this partly from experience, partly from talking to other people about their experiences, and partly via information I glean from the student evaluations I’ve read.
Speaking of evaluations, at some point I want to write about the noise that come from calculus evaluations, because that may as well be an entire subfield of statistics in itself. For example, I think there may be more variation depending on semester than depending on professor, due to the way kids take calculus in high school. In general it’s really hard to infer how good a job you did teaching based on calculus evaluations.
However, there is some signal. I remember reading about a study that said when some guy who was teaching two sections was introduced the first day in one of the sections by a distinguished-looking professor who went on about the instructor’s credentials, that class had much better end-of-semester evaluations, even though the content of the two sections was identical. Even more evidence that you should formally introduce yourself, if not bring in a friend for the job.
Is the Onion actually America’s finest news source?
Have you noticed that some of the best reporting nowadays is satire? I feel like I learn most of the news I know from reading newspapers online, but I’m unusual: most people, especially young people, seem to get their news from the Daily Show and Colbert, as well as the Onion.
And it’s not just the writing, which is generally excellent and intelligent, as well as hilariously entertaining. It’s the topics themselves that are incisive and that get to the heart of what’s ridiculous or dysfunctional about our financial, cultural, and political systems.
What if we started a newspaper that took its cues directly from the Onion, and rewrote every article in a straight, anti-satire way? Would that newspaper be better or worse than the New York Times? I claim it would be more bizarre but also more relevant to our lives. It may miss entire swaths of typical news coverage but then again it would cover certain things in a more holistic light.
For example, what would a anti-satirist do with this article? Or this one? Just having someone seriously articulate why these things are so funny would be a good start, and an article I’d love to read.
Mortar Hawk: hadoop made easy
Yesterday a couple of guys from Mortar came to explain their hadoop platform. You can see a short demo here. I wanted to explain it at a really high level because it’s cool and a big deal for someone like me. I’m not a computer scientist by training, and Mortar allows me to work with huge amounts of data relatively easily. In other words, I’m not sure what ultimately will be the interface for analytics people like me to get access to massive data, but it will be something like this, if not this.
To back up one second, for people who are nodding off, here’s the thing. If you have terabytes of data to crunch, you can’t put it on your computer to take a look at it, and then crunch, because your computer is too small. So you need to pre-crunch. That’s pretty much the problem we need to solve, and people have solved it either one of two ways.
The first is to put your data onto a big relational database, on the cloud or something, and use SQL or some such language to do the crunching (and aggregating and what have you) until it’s small enough to deal with, and then download it and finish it off on your computer. The second solution, called MapReduce (the idea started at Google), or hadoop (the open-source implementation started at Yahoo) allows you to work on the raw data directly where it lies (e.g. on the Amazon cloud (where it’s actually Elastic MapReduce, which I believe is a fork of hadoop)), in iterative steps called mappings and reduction steps.
Actually there’s an argument to be made, apparently, because I heard it at the Strata conference, that data scientists should never use hadoop at all, that we should always just use relational databases. However, that doesn’t seem economical, the way it’s set up at my work anyway. Please comment if you have an opinion about this because it’s interesting to me how split the data science community seems to be about this issue.
On the other hand, if you can make using hadoop as easy as using SQL, then who cares? That’s kind of what’s happened with Mortar. Let me explain.
Mortar has a web-based interface with two windows. On top we have the pig window and on the bottom a python editor. The pig window is in charge and you can call python functions in the pig script if you have defined them below. Pig is something like SQL but is procedural, so you tell it when to join and when to aggregate and what functions to use in what order. Then pig figures out how to turn your code into map-reduce steps, including how many iterations. They say pig is good at this but my guess is that if you really don’t know anything about how map-reduce works then it’s possible to write pig code that’s super inefficient.
One cool feature, which I think comes from pig itself but in any case is nicely viewable through the Mortar interface, is that you can ask it to “illustrate” the resulting map-reduce code and it takes a small sample of your data and shows example data (of “every type” in a certain sense) at every step of the process. This is super useful as a bug-watching feature to see that it’s looking good with small data sets.
The interface is well designed and easy to use. Overall it reduces a pretty scary and giant data job to something that would probably take me about a week to feel comfortable. And new hires who know python can get up to speed really quickly.
There are some issues right now, but the Mortar guys seem eager to improve the product quickly. To name a few:
- it’s not yet connected to git (although you can save pig and python code you’ve already run),
- you can’t import most python modules except super basic ones like math (including ones you’ve written; right now you have to copy and paste into their editor),
- they won’t be able to ever let you import numpy because they are actually using jython and numpy is c-based,
- it doesn’t automatically shut down the cluster after your job is finished, and
- it doesn’t yet allow people to share a cluster
These last two mean that you have to be pretty on top of your stuff, which is too bad if you want to leave for the night and start a job and then bike home and feed your kids and put them to bed. Which is kind of my style.
Please tell me if any of you know other approaches that allow python-savvy (but not java savvy) analytics nerds access to hadoop in an easy way!
Occupy Wall Street: Day 13
So I went to see the Occupy Wall Street protests this morning before work and this evening after work again. Here are some of my comments and observations.
First, if you are interested in checking it out, know that there are small marches at opening and closing bell for the market.
However, the police have made it basically impossible to walk on Wall Street, due to some incredibly annoying barricades.
So for our march this morning we seemed to just circle the city block where the protest is based, although I didn’t stay til the end so it’s possible they decided to very very slowly march on Wall Street proper.
Second, they have “assemblies” twice a day, with guest speakers sometimes (Michael Moore, Susan Sarandon and Cornel West have visited), and this is where general announcements are made. The crowd was quite large tonight and it was difficult to hear what the speaker and the repeaters were saying, which is frustrating. But maybe it’s easier at the 1pm assembly. Also, it seems to be easier to actually discuss issues in the morning- at night it gets loud and kind of crazy and hard to focus in my opinion.
Next, I’d like to address the issue of the message of the protesters being dismissed as incoherent. For the record, I went to a conference at the end of 2009 at Columbia Business School on the financial crisis and what we should do about it, where the speakers were fancy economists from central banks and CEOs of international banks, and they were about as incoherent as these protesters. There was absolutely no getting them to say anything that was an actual plan or even an attempt at a plan for changing the system so this mess wouldn’t happen again. I should know, because there was a question and answer period and I asked.
Having said that, there have been some pretty unconvincing statements reported from some of the protesters in terms of what they would like to see. For example, some of them seem to think that short selling should be banned. As some of you know, I disagree. In fact there are lots of seriously corrupt and ridiculous things going on in the financial system which they should know about and they should protest, and I’d like to invite them to educate themselves.
In particular, if you are someone interested in knowing stuff about how the financial system works, then please ask! A major part of why I blog is to try to inform people about these things who are interested. Please comment below and ask whatever you want, and if I don’t know the answer I will find someone who does, or I will blog about the question.
Having said that, I’d like to add that it’s on the one hand perfectly reasonable that people don’t understand the financial system, because it has essentially been set up to be too complicated to understand, and on the other hand it’s also reasonable to think of the entire financial system as a black box which can be judged by its outputs.
Finally, if we are going to judge the system by looking at its outputs, then these protesters, who are in general young, with educations, huge students debts, and hopeless outlooks, have a pretty dismal view. In other words they have every right to complain that the system is fucking them, even though they don’t know how the system works. I for one am super proud that they’re out there doing something, even if it’s not obviously organized and polished, rather than passively sitting by.
Go Rays!
As a long-time (yes since they sucked) Red Sox fan, let me just say, the Tampa Bay Rays totally deserve to be in the play-offs. They made me a fan last night with an absolutely amazing game.
Never apologize
Last night I was talking to a friend of mine about my teaching experiences, and what’s it’s like to be a woman in math and to be taken seriously. We were going over the standard stuff, that women are too self-effacing compared to men and tend not to strut their stuff enough. But then I remembered this story from my early teaching experiences that kind of put a different spin on that.
I was in grad school, and over the summer I went to Berkeley to teach at a women in math program, which was still called the “Mill’s program” even though it was being held at Berkeley. It was a really fun experience, something like 30 days of lecture and problem session, and I led the problem sessions.
It was some time in the second week when, one day because of something or other, I hadn’t prepared completely and I apologized to the class for being slightly unprepared. I said something like, “sorry I’m not completely prepared today”. I remember thinking that, in spite of that, the class went very well and there was no “damage” from my being unprepared. Every other day I was completely, perhaps overly prepared, and that was the only day I ever mentioned something about my preparedness.
At the end of the summer we got back teaching evaluations, and I remember that a full half of the evaluations described me as unprepared.
I made a promise to myself never ever to apologize for anything again. And I never have, and I’ve never been accused like that since. Which isn’t to say I pretend to be a perfect teacher, but there are subtle ways of dealing with imperfections (my favorite: turn a self-criticism into a flattery. Instead of saying, oh how stupid I am for not thinking of that, say oh how smart you are for thinking of that. Generosity is not a negative in my experience!).
Going back to last night, though, it’ a two-way street. Women may be too self-effacing, but other people (including women!) are absolutely too dismissive. It’s a very important thing to keep in mind when you are teaching or presenting.
One other thing, in a one-on-one, professional setting, I believe you can apologize and not be executed for it (sometimes and depending on the person), but in a teacher-students setting, or when you’re presenting to clients in business, or even when you’re presenting to colleagues, you’re giving a performance and need to be flawlessly confident.
In an ideal world, we would use this information to learn to become better audiences, to not be dismissive and overly harsh of self-effacing people, and I do try to keep this in mind when I’m in the audience. But it’s going to take lots of effort for this to happen on a large scale, especially among strangers. It’s a cultural axiom in a certain sense.
My advice to young people, especially women: never apologize.
Occupy Wall Street—Report
This is a guest post by FogOfWar.
I was originally going to lead with a tongue-in-cheek comment (later in the post now), but then the NYPD did something colossally stupid. If you haven’t seen it, here’s the video from this last weekend. It pretty much speaks for itself.
There’s a lot to be said about freedom of expression and police overreaction. I’ve been to see the protests a number of times, and they’ve never been violent and in fact seem pretty well trained in the confines of freedom of assembly in the US legal system. Using mace against an imminent threat of violence is OK for the police, but the video seems to show no threatening moves made at all (and it runs for a good period before the police attack so it wasn’t edited out).
I’d suggest the NYPD be shown the following video (taken from the protests in Greece) to demonstrate when things reach a level where force might be an appropriate response. Note that the crowd is attacking with sticks, Molotov cocktails and a fucking bowling ball. In contrast, the NYPD appears to be pepper spraying people for just holding signs and walking down the street. What the fuck?
There are maybe a few hundred people consistently protesting at “Occupy Wall Street” for about 10 days now. It’s got a definite crunchy vibe to the center. Drumming and Mohawks are mandatory:
But also a (growing?) contingent of more mainstream participants like this one:
Here’s a crowd shot for scale:
And some people painting signs:
And then of course, there’s the dreaded “consensus circle”:
It’s hard to tell what they really want to happen—this was up at one of the information booths (but then down the next time I went):
Misspelled “derivatives”, and there are some things on that list that are spot on and then others that are just weird and irrelevant (DTC? Really?). I don’t think you can hold that against them though. I work in the industry, and I’ve been spending the last three years thinking about this stuff and I still find it confusing and hard to come up with a cohesive plan of what I think should be done. At least these people are doing something, even if it’s a bit incoherent at times.
I have to end with my all time favorite sign from the protest. Someone was looking for good cardboard and inadvertently came up with the following:
“Delicious pizza to pay off the taxpayers”. Now that’s a slogan I think we can all rally behind!
-FoW
mathbabe 
