Money in politics: the BFF project
This is a guest post by Peter Darche, an engineer at DataKind and recent graduate of NYU’s ITP program. At ITP he focused primarily on using personal data to improve personal social and environmental impact. Prior to graduate school he taught in NYC public schools with Teach for America and Uncommon Schools.
We all ‘know’ that money influences the way congressmen and women legislate; at least we certainly believe it does. According to poll conducted by law professor Larry Lessig for his book Republic Lost, 75% of respondents (Republican and Democrat) said that ‘money buys results in Congress.’
And we have good reason to believe so. With astronomical sums of campaign money flowing into the system and costly, public-welfare reducing legislation coming out, it’s the obvious explanation.
But what does that explanation really tell us? Yes, a congresswoman’s receiving millions dollars from an industry then voting with that industry’s interests reeks of corruption. But, when that industry is responsible for 80% of her constituents’ jobs the causation becomes much less clear and the explanation much less informative.
The real devil is in the details. It is in the ways that money has shaped her legislative worldview over time and in the small, particular actions that tilt her policy one way rather than another.
In the past finding these many and subtle ways would have taken a herculean effort: untold hours collecting campaign contributions, voting records, speeches, and so on. Today however, due to the efforts of organizations like the Sunlight Foundation and Center for Responsive Politics, this information is online and programmatically accessible; you can write a few lines of code and have a computer gather it all for you.
The last few months Cathy O’Neil, Lee Drutman (a Senior Fellow at the Sunlight Foundation), myself and others have been working on a project that leverages these data sources to attempt to unearth some of these particular facts. By connecting all the avenues by which influence is exerted on the legislative process to the actions taken by legislators, we’re hoping to find some of the detailed ways money changes behavior over time.
The ideas is this: first, find and aggregate what data exists related to the ways influence can be exerted on the legislative process (data on campaign contributions, lobbying contributions, etc), then find data that might track influence manifesting itself in the legislative process (bill sponsorships, co-sponsorships, speeches, votes, committee memberships, etc). Finally, connect the interest group or industry behind the influence to the policies and see how they change over time.
One immediate and attainable goal for this project, for example, is to create an affinity score between legislators and industries, or in other words a metric that would indicate the extent to which a given legislator is influenced by and acts in the interest of a given industry.
So far most of our efforts have focused on finding, collecting, and connecting the records of influence and legislative behavior. We’ve pulled in lobbying and campaign contribution data, as well as sponsored legislation, co-sponsored legislation, speeches and votes. We’ve connected the instances of influence to legislative actions for a given legislator and visualized it on a timeline showing the entirety of a legislator’s career.
Here’s an example of how one might use the timeline. The example below is of Nancy Pelosi’s career. Each green circle represents a campaign contribution she received, and is grouped within a larger circle by the month it was recorded by the FEC. Above are colored rectangles representing legislative actions she took during the time-period in focus (indigo are votes, orange speeches, red co-sponsored bills, blue sponsored bills). Some of the green circles are highlighted because the events have been filtered for connection to health professionals.
Changing the filter to Health Services/HMOs, we see different contributions coming from that industry as well as a co-sponsored bill related to that industry.
Mousing over the bill indicates its a proposal to amend the Social Security act to provide Medicaid coverage to low-income individuals with HIV. Further, looking around at speeches, one can see a relevant speech about the children’s health insurance. Clicking on the speech reveals the text.
By combining data about various events, and allowing users to filter and dive into them, we’re hoping to leverage our natural pattern-seeking capabilities to find specific hypotheses to test. Once an interesting pattern has been found, the tool would allow one to download the data and conduct analyses.
Again, It’s just start, and the timeline and other project related code are internal prototypes created to start seeing some of the connections. We wanted to open it up to you all though to see what you all think and get some feedback. So, with it’s pre-alphaness in mind, what do you think about the project generally and the timeline specifically? What works well – helps you gain insights or generate hypotheses about the connection between money and politics – and what other functionality would you like to see?
The demo version be found here with data for the following legislators:
- Nancy Pelosi
- John Boehner
- Cathy McMorris Rodgers
- John Boehner
- Eric Cantor
- James Lankford
- John Cornyn
- Nancy Pelosi
- James Clyburn
- Kevin McCarthy
- Steny Hoyer
Note: when the timeline is revealed, click and drag over content at the bottom of the timeline to reveal the focus events.
THIS REQUIRES YOUR MOCKERY
My title today is the subject line of a message I received from my buddy Jordan Ellenberg. Thanks for making things so easy for me to blog this morning, Jordan!
So here’s the subject: a Silicon Valley entrepreneur’s self-help book, including advice on how to quantify and measure your sex life, among other things – every other thing, in fact.
Just in case you’ve missed it, there’s a movement afoot among certain people to collect data about themselves on the level of heart rate, daily exercise and eating patterns, and the like, with the goal of self-improvement.
It’s got a name – the Quantified Self movement – and if I haven’t mentioned it before, it’s because honestly, it’s too easy, and I generally speaking like a challenge.
I saw a bunch of these guys at the health analytics conference I went to a couple of months ago, and let me tell you, they’re weird, and they know it, and they don’t care.
They honestly feel sorry for people who don’t have a Ironman Triathlon (or four) to train for via wireless excel spreadsheets. I mean, how do those people know whether they’ve actually improved? How do they know if they’ve eaten enough carbs? How do they know if they’ve slept??
As far as these Quantified Selfers (QSers) are concerned, it’s only a matter of time before everyone is, like them, making themselves perfect, and they’re the vanguard with nothing to be defensive about.
So anyhoo, those QS guys are convinced that they’re accomplishing something with all of their number collecting and crunching, like maybe they’ll live forever or something (after curing cancer), and they’re just so douchey I feel sorry for them. Blogging about them and trashing them would be like a mean older kid in the playground telling a bunch of little kids that there’s no Santa Claus.
Why do that? Why pop their bubble?
Here’s why: it’s just plain fun, especially now that they’ve ventured into sexy territory with their spreadsheets.
Here are a couple of questions for the Quantified Sexual Selfers (QSSers) in the audience, please get back to me.
- Yes or no: nothing says “hot ‘n’ steamy” like a fitbit readout of historical orgasms.
- Where does the sensor band get attached, and does it come with a vibrating option?
- Are your orgasms more satisfying before or after syncing your daily data with Stephen Wolfram’s?
- What’s your metric of success, and how do you know your girlfriend ain’t gaming the system?
Aunt Pythia’s advice
Aunt Pythia is ever so pleased to be here today, on her 41st birthday no less, spewing forth questionable advice that nobody will be willing to go on the record as having read, but which she knows in her heart each reader secretly treasures.
Now, when Aunt Pythia was on her death bed two weeks ago, the call was raised for more questions, and quickly. And readers, you responded, which brings tears to Aunt Pythia’s eyes, it really does. It brought her back from the brink and she’s eternally grateful.
The problem is, though, this: some of these questions are of dubious substance. To be honest, they’re very short, not extremely well-thought out or juicy, and don’t pose an existential conundrum.
Of course, one doesn’t want to look a gift horse in the mouth, so I’ve arranged to answer these questions in speed-round fashion today. I hope you enjoy it, and please don’t forget:
Submit your existential conundrums to Aunt Pythia at the bottom of this page!
By the way, if you don’t know what the hell I’m talking about, go here for past advice columns and here for an explanation of the name Pythia.
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Dear Aunt Pythia,
What should I do when, after posting a video from Vi Hart, a reader responds “I’ve got to marry that girl.”?
Math Guy
Dear Math Guy,
Offer to administer the wedding! Turns out you can get certified as a minister with an app called “OrdainThyself”.
Aunt Pythia
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Dear Aunt Pythia,
If you were a flavor of ice cream, what flavor of ice cream would you be?
Sleepless in Seattle
Dear SiS,
Not sure about me, but my kids would all be Ben & Jerry’s Coffee Heath Bar Crunch, which I ate pretty much continuously and exclusively during my three pregnancies.
Not me, but I had that same stoned expression.
I hope that helps!
Aunt Pythia
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Dear Aunt Pythia,
I am a 24 year-old grad student, and I’ve noticed the following trend in my life: When I was younger (read, 14 and older), I always was attracted to people around 19 years of age which was too old for me. But now, I’m still attracted to people around 19 years of age, which is quickly getting too young for me. What should I do???
Feeling a little bit like a Cougar…
Dear Wanna-be Cougar,
Just as I can’t claim to be part of the generation of 20-somethings that refuse to make appointments more than 17 minutes in advance, and then only by text, you cannot claim to be a cougar, sorry. That’s reserved for women who are at least 40, possibly 41, and there’s no extra room at this table.
Not me, but I do share the sentiment
In terms of your “problem,” it’s one of those things you can’t control, as far as I know, so just take the posture of bewildered amusement at your own desires, and make sure you don’t do anything illegal or weird.
Smooches,
Aunt Pythia
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Dear Aunt Pythia,
Since I know how fond you are of bridge, I have a question about slam bidding: Given the fact that you and your partner have a guaranteed slam, what is the probability that you will bid into that slam? What are the ways to maximize that probability, in terms of convention? What are the easiest ways to invite slam to your partner? What is your opinion of cue bidding, and what are the least confusing ways to cue bid?
Seeker Abling Young Cardsharks
Dear SAYC,
I appreciate how your sign-off is code for how I should answer this question.
But even so, I’m going to go with my gut here: when I’m in a perceived slam with my partner, I always make sure to stare knowingly into his or her eyes, with raised eyebrows, and mouth the word “slam”, Colbert-style.
Me.
If that isn’t getting through I squeeze his or her knee under the table. Works every time. For me, bridge is all about being fun and ridiculous, and I never follow the rules unless it’s more fun to do so.
I hope that helps!
Auntie P
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Please submit your well-specified, fun-loving, cleverly-abbreviated question to Aunt Pythia!
The creepy mindset of online credit scoring
Usually I like to think through abstract ideas – thought experiments, if you will – and not get too personal. I take exceptions for certain macroeconomists who are already public figures but most of the time that’s it.
Here’s a new category of people I’ll call out by name: CEO’s who defend creepy models using the phrase “People will trade their private information for economic value.”
That’s a quote of Douglas Merrill, CEO of Zest Finance, taken from this video taken at a recent data conference in Berkeley (hat tip Rachel Schutt). It was a panel discussion, the putative topic of which was something like “Attacking the structure of everything”, whatever that’s supposed to mean (I’m guessing it has something to do with being proud of “disrupting shit”).
Do you know the feeling you get when you’re with someone who’s smart, articulate, who probably buys organic eggs from a nice farmer’s market, but who doesn’t expose an ounce of sympathy for people who aren’t successful entrepreneurs? When you’re with someone who has benefitted so entirely and so consistently from the system that they have an almost religious belief that the system is perfect and they’ve succeeded through merit alone?
It’s something in between the feeling that, maybe you’re just naive because you’ve led such a blessed life, or maybe you’re actually incapable of human empathy, I don’t know which because it’s never been tested.
That’s the creepy feeling I get when I hear Douglas Merrill speak, but it actually started earlier, when I got the following email almost exactly one year ago via LinkedIn:
Hi Catherine,
Your profile looked interesting to me.
I’m seeking stellar, creative thinkers like you, for our team in Hollywood, CA. If you would consider relocating for the right opportunity, please read on.
You will use your math wizardry to develop radically new methods for data access, manipulation, and modeling. The outcome of your work will result in game-changing software and tools that will disrupt the credit industry and better serve millions of Americans.
You would be working alongside people like Douglas Merrill – the former CIO of Google – along with a handful of other ex-Googlers and Capital One folks. More info can be found on our LinkedIn company profile or at www.ZestFinance.com.
At ZestFinance we’re bringing social responsibility to the consumer loan industry.
Do you have a few moments to talk about this? If you are not interested, but know someone else who might be a fit, please send them my way!
I hope to hear from you soon. Thank you for your time.
Regards,
Adam
Wow, let’s “better serve millions of Americans” through manipulation of their private data, and then let’s call it being socially responsible! And let’s work with Capital One which is known to be practically a charity.
What?
Message to ZestFinance: “getting rich with predatory lending” doesn’t mean “being socially responsible” unless you have a really weird definition of that term.
Going back to the video, I have a few more tasty quotes from Merrill:
- First when he’s describing how he uses personal individual information scraped from the web: “All data is credit data.”
- Second, when he’s comparing ZestFinance to FICO credit scoring: “Context is developed by knowing thousands of things about you. I know you as a person, not just you via five or six variables.”
I’d like to remind people that, in spite of the creepiness here, and the fact that his business plan is a death spiral of modeling, everything this guy is talking about is totally legal. And as I said in this post, I’d like to see some pushback to guys like Merrill as well as to the NSA.
On being a data science skeptic: due out soon
A few months ago, at the end of January, I wrote a post about Bill Gates naive views on the objectivity of data. One of the commenters, “CitizensArrest,” asked me to take a look at a related essay written by Susan Webber entitled “Management’s Great Addiction: It’s time we recognized that we just can’t measure everything.”
Webber’s essay is really excellent, not to mention impressively prescient considering it was published in 2006, before the credit crisis. The format of the essay is simple: it brings up and explains various dangers in the context of measurement and modeling of business data, and calls for finding a space in business for skepticism. What an idea! Imagine if that had actually happened in finance when it should have back in 2006.
Please go read her essay, it’s short.
Recently, when O’Reilly asked me to write an essay, I thought back to this short piece and decided to use it as a template for explaining why I think there’s a just-as-desperate need for skepticism in 2013 here in the big data world as there was back then in finance.
Whereas most of Webber’s essay talks about people blindly accepting numbers as true, objective, precise, and important, and the related tragic consequences, I’ve added a small wrinkle to this discussion. Namely, I also devote concern over the people who underestimate the power of data.
Most of this disregard for unintended consequences is blithe and unintentional (and some of it isn’t), but even so it can be hugely damaging, especially to the individuals being modeled: think foreclosed homes due to crappy housing-related models in the past, and think creepy models and the death spiral of modeling for the present and future.
Anyhoo, I’m actively writing it now, and it’ll be coming out soon. Stay tuned!
PyData and a few other things
So here’s the thing about being a parent of benign neglect: it’s no walk in the park. I talk a big game, but the truth is I’ve have trouble getting to sleep from the anxiety. To distract myself I’ve been watching Law & Order episodes on Netflix until the wee hours of the night.
Two things about this plan suck. First, my husband is in Amsterdam, which means he’s 6 time zones away from our oldest son whereas I’m only 3, but somehow that means I’m shouldering 99.5% of the responsibility to worry (there’s some universal geographic law of parenting at work there but I don’t know how to formulate it). Second, half of the L&O episodes involve either children getting maimed or killed or child killers. Not restful but I freaking can’t stop!
In any case, not much extra energy to spring out of bed and write the blog, so apologies for a sparse period for mathbabe. For whatever reason I woke up this morning in time to blog, however, so as to not miss an opportunity it’s gonna be in list form:
- I’ve been invited to keynote at PyData in Cambridge, MA at the end of the month – me and Travis Oliphant! I’m still coming up with the title and abstract for my talk, but it’s going to be something about storytelling with data using the iPython Notebook. Please make suggestions!
- I was in a Wall Street Journal article about Larry Summers, talking about whether he’s got a good personality to take over from Ben Bernanke, i.e. should we trust our lives and our future with him. I say nope. What’s funny is that my uncle, economist Bob Hall, is also referred to in the same article. The journalist didn’t know we’re related until after the article came out and Uncle Bob informed him.
- Hey, can we give it up for Eliot Spitzer? The powers that be are down about that guy presumably for having sex with prostitutes but really because he’s a threat. I say legalize prostitution, unionize the prostitutes a la the dutch, and put Spitzer in charge of something involving money and corruption, he’s smart and fearless. Who’s with me?
- It looks like good news: the Consumer Financial Protection Bureau might be cracking down on illegal debt collector tactics. Update: wait, the fines are fractions of 1% of the revenue these guys made on their unfair practices. Can we please have a rule that when you get caught breaking the law, the fine will be large enough so it’s no longer profitable?
Measuring Up by Daniel Koretz
This is a guest post by Eugene Stern.
Now that I have kids in school, I’ve become a lot more familiar with high-stakes testing, which is the practice of administering standardized tests with major consequences for students who take them (you have to pass to graduate), their teachers (who are often evaluated based on standarized test results), and their school districts (state funding depends on test results). To my great chagrin, New Jersey, where I live, is in the process of putting such a teacher evaluation system in place (for a lot more detail and criticism, see here).
The excellent John Ewing pointed me to a pretty comprehensive survey of standardized testing called “Measuring Up,” by Harvard Ed School prof Daniel Koretz, who teaches a course there about this stuff. If you have any interest in the subject, the book is very much worth your time. But in case you don’t get to it, or just to whet your appetite, here are my top 10 takeaways:
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Believe it or not, most of the people who write standardized tests aren’t idiots. Building effective tests is a difficult measurement problem! Koretz makes an analogy to political polling, which is a good reminder that a test result is really a sample from a distribution (if you take multiple versions of a test designed to measure the same thing, you won’t do exactly the same each time), and not an absolute measure of what someone knows. It’s also a good reminder that the way questions are phrased can matter a great deal.
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The reliability of a test is inversely related to the standard deviation of this distribution: a test is reliable if your score on it wouldn’t vary very much from one instance to the next. That’s a function of both the test itself and the circumstances under which people take it. More reliability is better, but the big trade-off is that increasing the sophistication of the test tends to decrease reliability. For example, tests with free form answers can test for a broader range of skills than multiple choice, but they introduce variability across graders, and even the same person may grade the same test differently before and after lunch. More sophisticated tasks also take longer to do (imagine a lab experiment as part of a test), which means fewer questions on the test and a smaller cross-section of topics being sampled, again meaning more noise and less reliability.
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A complementary issue is bias, which is roughly about people doing better or worse on a test for systematic reasons outside the domain being tested. Again, there are trade-offs: the more sophisticated the test, the more extraneous skills beyond those being tested it may be bringing in. One common way to weed out such questions is to look at how people who score the same on the overall test do on each particular question: if you get variability you didn’t expect, that may be a sign of bias. It’s harder to do this for more sophisticated tests, where each question is a bigger chunk of the overall test. It’s also harder if the bias is systematic across the test.
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Beyond the (theoretical) distribution from which a single student’s score is a sample, there’s also the (likely more familiar) distribution of scores across students. This depends both on the test and on the population taking it. For example, for many years, students on the eastern side of the US were more likely to take the SAT than those in the west, where only students applying to very selective eastern colleges took the test. Consequently, the score distributions were very different in the east and the west (and average scores tended to be higher in the west), but this didn’t mean that there was bias or that schools in the west were better.
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The shape of the score distribution across students carries important information about the test. If a test is relatively easy for the students taking it, scores will be clustered to the right of the distribution, while if it’s hard, scores will be clustered to the left. This matters when you’re interpreting results: the first test is worse at discriminating among stronger students and better at discriminating among weaker ones, while the second is the reverse.
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The score distribution across students is an important tool in communicating results (you may not know right away what a score of 600 on a particular test means, but if you hear it’s one standard deviation above a mean of 500, that’s a decent start). It’s also important for calibrating tests so that the results are comparable from year to year. In general, you want a test to have similar means and variances from one year to the next, but this raises the question of how to handle year-to-year improvement. This is particularly significant when educational goals are expressed in terms of raising standardized test scores.
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If you think in terms of the statistics of test score distributions, you realize that many of those goals of raising scores quickly are deluded. Koretz has a good phrase for this: the myth of the vanishing variance. The key observation is that test score distributions are very wide, on all tests, everywhere, including countries that we think have much better education systems than we do. The goals we set for student score improvement (typically, a high fraction of all students taking a test several years from now are supposed to score above some threshold) imply a great deal of compression at the lower end of this distribution – compression that has never been seen in any country, anywhere. It sounds good to say that every kid who takes a certain test in four years will score as proficient, but that corresponds to a score distribution with much less variance than you’ll ever see. Maybe we should stop lying to ourselves?
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Koretz is highly critical of the recent trend to report test results in terms of standards (e.g., how many students score as “proficient”) instead of comparisons (e.g., your score is in the top 20% of all students who took the test). Standards and standard-based reporting are popular because it’s believed that American students’ performance as a group is inadequate. The idea is that being near the top doesn’t mean much if the comparison group is weak, so instead we should focus on making sure every student meets an absolute standard needed for success in life. There are three (at least) problems with this. First, how do you set a standard – i.e., what does proficient mean, anyway? Koretz gives enough detail here to make it clear how arbitrary the standards are. Second, you lose information: in the US, standards are typically expressed in terms of just four bins (advanced, proficient, partially proficient, basic), and variation inside the bins is ignored. Third, even standards-based reporting tends to slide back into comparisons: since we don’t know exactly what proficient means, we’re happiest when our school, or district, or state places ahead of others in the fraction of students classified as proficient.
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Koretz’s other big theme is score inflation for high-stakes tests: if everyone is evaluated based on test scores, everyone has an incentive to get those scores up, whether or not that actually has much correlation with learning. If you remember anything from the book or from this post, remember this phrase: sawtooth pattern. The idea is that when a new high-stakes standardized test appears, average scores start at some base level, go up quickly as people figure out how to game the test, then plateau. If the test is replaced with another, the same thing happens: base, rapid growth, plateau. Repeat ad infinitum. Koretz and his collaborators did a nice experiment in which they went back to a school district in which one high-stakes test had been replaced with another and administered the first test several years later. Now that teachers weren’t teaching to the first test, scores on it reverted back to the original base level. Moral: score inflation is real, pervasive, and unavoidable, unless we bite the bullet and do away with high-stakes tests.
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While Koretz is sympathetic toward test designers, who live the complexity of standardized testing every day, he is harsh on those who (a) interpret and report on test results and (b) set testing and education policy, without taking that complexity into account. Which, as he makes clear, is pretty much everyone who reports on results and sets policy.
Final thoughts
If you think it’s a good idea to make high-stakes decisions about schools and teachers based on standardized test results, Koretz’s book offers several clear warnings.
First, we should expect any high-stakes test to be gamed. Worse yet, the more reliable tests, being more predictable, are probably easier to game (look at the SAT prep industry).
Second, the more (statistically) reliable tests, by their controlled nature, cover only a limited sample of the domain we want students to learn. Tests trying to cover more ground in more depth (“tests worth teaching to,” in the parlance of the last decade) will necessarily have noisier results. This noise is a huge deal when you realize that high-stakes decisions about teachers are made based on just two or three years of test scores.
Third, a test that aims to distinguish “proficiency” will do a worse job of distinguishing students elsewhere in the skills range, and may be largely irrelevant for teachers whose students are far away from the proficiency cut-off. (For a truly distressing example of this, see here.)
With so many obstacles to rating schools and teachers reliably based on standardized test scores, is it any surprise that we see results like this?
Parenting through benign neglect
In 1985, when I was 12 years old, I went to communist Budapest by myself, for a month. I’d met and befriended two Hungarian families when I was 11 and they were living next door to me for a year in Lexington, Massachusetts, and when they went back to Budapest they invited me to visit.
So it wasn’t like I didn’t have a place to sleep when I got there, but even so, my parents decided that yes, a trip across the world into a country that needed a visa to enter, that didn’t have a hard currency, and that didn’t have consistent phone lines at post offices (never mind at people’s homes, that was out of the question) was a great place for their 12-year-old daughter to visit by herself.
I also almost didn’t make the correct connection in Zurich, and I am seriously wondering what would have happened if I’d missed my flight. How would I have connected with my hosts? Where would I have slept? What would I have done for money?
I did make my flight, though, and I did meet my hosts, and the worst thing that happened to me was that when the cows got sick, I got sick – very sick. And to be fair, I turned 13 when I was there.
I came home appreciating milk pasteurization, and to a lesser extent milk homogenization. I was skinnier and less spoiled, I knew what really good peaches tasted like, and I was completely sick of paprika. Overall it was a good trip, and I’m glad I went.
And if I or my parents had been more cautious, I wouldn’t have gone. Goes to show you, sometimes it’s good not to think too hard about what could go wrong.
Unfortunately, I’m older now, and my 13-year-old just got on a plane to San Francisco by himself to attend a Model UN camp at Stanford. And all I can think about it what might go wrong.
Don’t get me wrong, it didn’t stop me from putting him on the plane. I’m trying to channel my parents’ benign neglect child-raising technique from which I benefitted so tremendously. He’s got a working cell phone, plenty of cash, and my BFF Becky will be within driving distance of him over there.
Hey, it’s not like he’s going to North Korea – which is, by the way, where he requested to be sent – and I’m pretty sure the milk there is pasteurized, as long as you avoid farmer’s markets.
Aunt Pythia: alive and well!
Aunt Pythia is just bursting with love and admiration for the courageous and articulate readers that sent in their thought-provoking and/or heart-rending questions in the last week which got her off life support and back into fighting shape.
On the one hand, Aunt Pythia did’t want to be a histrionic burden to you all, but on the other hand clearly histrionics work, so there it is. Thank you thank you thank you for allowing histrionics to work.
That’s not to say you should rest on your laurels, readers! First of all, Aunt Pythia always needs new questions (you don’t want her to get sick again, right?), and secondly, I’ve heard laurels can be quite prickly.
In other words,
Submit your question for Aunt Pythia at the bottom of this page!
By the way, if you don’t know what the hell I’m talking about, go here for past advice columns and here for an explanation of the name Pythia.
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Dear Aunt Pythia,
Isn’t the distribution thing kind of REALLY IMPORTANT for how we think of the sexual partner thing? If fifty women are getting it on with one man, while the other 49 men are, uh, monks, or vice versa, depending on the universe you live in, that certainly influences how you think about stereotypes.
Ms. Hold On A Second
Dear HOAS,
Yes it is, but the average should take care of that as long as the sample size is large enough to have that one lucky man represented, as well as the 49 unlucky men, in the correct proportions.
Let’s go with this a bit. How fat-tailed would sexual practice have to be to make this a problem? After all, there are distributions that defy basic intuition around this – look at the Cauchy Distribution, which has no defined mean or variance, for example. Maybe that’s what’s going on?
Hold on one cotton-picking second! We have a finite number of people in the world, so obviously this is not what’s going on – the average number of sexual partners exists, even if it’s a pain in the ass to compute!
But I’m willing to believe that there’s a sampling bias at work here. Maybe female prostitutes are excluded from surveys, for example. And if men always included their visits to prostitutes, that would introduce a bias.
I’ll go on record saying I doubt that explains the discrepancy, although to be mathematical about it I’d need to have an estimate of how much prostitute sex happens and with how many men. I don’t have that data but maybe someone does.
And of course it’s probably not just one thing. Some combination of the surveys being for college students, and fewer prostitutes being at college, and some actual lying. But my money’s on the lying every time.
Aunt Pythia
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Dear Aunt Pythia,
I’m not sure this is the correct forum for this question, but here it goes: I come from an economics/econometrics background, where the statistical modeling tool of choice is Stata. I now work at an organization in a capacity that is heavy on statistical modeling, in some cases (but not always) working with “Big Data”.
There is some freedom in terms of the tools we can use, but nobody uses Stata, to my knowledge. As somebody who is just starting out in this industry, I’m trying to get a pulse on which tool I should invest the time into learning, SAS or R. Do you have an opinion either way?
Lonely in Missouri
Dear Lonely,
Always go with the open source option. R, or even better, python. What with pandas and other recent packages, python is just fabulous.
Aunt Pythia
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Dear Aunt Pythia,
I’m a graduate student in math at a large state research school in the midwest, finishing in 2014. My question is about my advisor and my job plans.
First, here’s what I’m planning to do next year. My wife is a student in the same department as I am, and she’s also finishing next year. We both want to move to a big city. We’d settle for a Philadelphia or Seattle or really anywhere we can live without a car, but by “big city” I really mean New York. We’ve both lived there before and we like it better than anywhere else.
My wife wants a non-academic job. I’m going to apply for research postdocs. I should be a fairly strong candidate, but I’m no superstar and I definitely don’t think it’s assured that I’ll get one, especially with the limited set of places we’re willing to live. And that’s fine! I like the idea of being a professor, but there’s lots of other jobs that I think I’d like too. I know that I wouldn’t like living apart from my wife, or living somewhere that we hate.
My advisor has done a good job of making me into a researcher. The problem is that he’s just a difficult person. Less charitably, he’s an asshole (at least to me). He’s arrogant, rude, and demanding. The one time I ever told him he wasn’t treating me fairly (which I did politely, but in an email), he completely flipped out (in a series of emails) and told me that as his student, I had no right to talk to him that way.
I don’t want to make him sound like a complete monster: he’s from a culture that puts a lot of weight on respect and hierarchy, and I’ve seen him be empathetic and kind. But he absolutely cannot handle it if I disagree with him or don’t do what he says.
In all conversations we’ve had about my future, he seems to have no interest in what I actually want to do. I could have graduated last year, but my department had no problem letting me stay on so that I could finish at the same time as my wife. My advisor was really unhappy about this. His attitude was that a year wasn’t much time to spend away from a spouse (after all, he spent three!), and I should have at least applied for a few prestigious postdocs to maximize my chances of getting one.
Recently, my advisor emailed me just to tell me how disappointed he is in me: I have a bad attitude, I don’t always go to seminars even when he tells me I should, and that I make decisions about my future on my own, instead of in consultation with him. I responded politely (and distantly) to this.
So, here’s the question: should I do anything about all of this? I don’t work with my advisor mathematically anymore, and I’ve been much happier since we stopped. I have other projects to work on and other collaborators to work with, and I think other people in the department would be happy to give me problems or work with me on them. I don’t think my advisor is going to change in any way, and I’m the kind of person who can’t stand to be treated like an underling or told what to do. My advisor has said that he’s still happy to write me a recommendation. What things should I do? I’m hoping your answer is nothing, so that I can continue having as little contact as possible with my advisor.
Feeling Refreshed at the End of an Era
Dear FREE,
Here’s the thing. I have sympathy with some of your story but not with all of it. First I’ll tackle the negative stuff, then I’ll get to the sympathy.
If I understand correctly, you could have graduated last year but instead you’re graduating next year. So you’re staying an extra two years on the department’s dime. Doesn’t that seem a bit strange? How about if you finish and get a job in town as an actuary or something to see if non-academic work suits you? Are you preventing someone from entering the department by being there so long?
Also, you mention that you don’t go to seminars. I don’t think I always went to seminars as a young graduate student, but as I got more senior I appreciated how much language development there is in seminars – even when I didn’t understand the results I learned about how people think and talk about their work by going to seminars. I don’t think it was a waste of my time even though I ultimately left academics. I don’t think it would waste your time to go to seminars.
In other words, you sound like an entitled lazy graduate student, and I’m not so surprised your advisor is fed up with you. And I’m pretty sure your non-academic boss would be even less sympathetic to someone spending an extra two years doing not much.
Now here’s where I do my best to sound nice.
Sounds like your advisor doesn’t get you, possibly because he’s fed up with the above-mentioned issues. Like a lot of academics, he understands ambition in one narrow field, and doesn’t even relate to not wanting to be successful in this realm. That’s probably not going to change, and there’s no reason to take advice from him about how you want to live your life and the decisions you’re making for your family.
So yes, ignore him. But don’t ignore me, and I’m here to say: stop being an entitled lazy-ass.
Aunt Pythia
——
Ok I’ve never heard of Aunt Pythia, and I know this is too easy for her, but I can’t let her die.
Aunt Pythia,
If each woman I date is an independent trial, and the probability of marrying a woman I date is 0.1, how many women do I have to date before I can be at least 90% sure of getting married? (You can substitute “having sex” for “getting married” if you like.)
Anonymous
Dear Anonymous,
Aunt Pythia appreciates the sentiment, and the question.
Let’s sex up the question just a wee bit and change it from “getting married” to “having sex” as you suggested, and also raise your chances a bit to 17%, out of pure human compassion.
Let’s establish some notation: each time you date some woman we will record it either as a “G” for “got laid” or as a “D” for “dry.” So for example, after 4 women you might have a record like:
DDDGD,
which would mean you got laid with the fourth woman but with no other women.
Are we good on notation?
OK now let’s answer the question. How long do we wait for a G?
The trick is to turn it around and ask it another way: how likely is a reeeeeeally long string of D’s?
Chances of one D are good: (100-17)% = 83%.
Chances of two D’s in a row are less good: 0.83*0.83 = 0.67 = 67%.
Chances of three D’s in a row are even less good:
If you keep going you’ll notice that chances of 11 D’s in a row is 11% but chances of 12 D’s in a row are only 9%. that means that, by dating 12 women or more, your chances of getting laid are better than 90%. If you think it’s really a 10% chance every time, you’ll have to date 22 women for such odds. I’d suggest you invest in a membership on OK Cupid or some such.
Good luck!
Auntie P
——
Please submit your well-specified, fun-loving, cleverly-abbreviated question to Aunt Pythia!
You give me a capital requirement, I’ll give you a derivative to skirt it.
I’ve enjoyed reading Anat Admati and Martin Hellwig’s recent book, The Bankers’ New Clothes, which explains a ton of things extremely well, including:
- Differentiating between what’s “good for banks” (i.e. bankers) versus what’s good for the public, and how, through unnecessary complexity and shittons of lobbying money, the “good for bankers” case is made much more often and much more vehemently,
- that, when there’s a guaranteed backstop for a loan, the person taking out the loan has incentive to take on more risk, and
- that there are two different definitions of “big returns” depending on the context: one means big in absolute value (where -30% is bigger than -10%), the other mean big as in more positive (where -10% is bigger than -30%). Believe it or not, this ambiguity could be (at least metaphorically) taken as a cause of confusion when bankers talk to the public, in the following sense. Namely, when the expected return on an investment is, say, 3%, it makes sense for bankers to lever up their bets so they get “bigger returns” in the first sense, especially since there’s essentially no down side for them (a -30% return doesn’t affect them personally, a 30% return means a huge bonus). From the perspective of the public, they’d like to see the banks go for the “bigger return” in the second sense, so avoid the -30% scenario altogether, via restrained risk-taking.
Admati and Hellwig’s suggestion is to raise capital requirements to much higher levels than we currently have.
Here’s the thing though, and it’s really a question for you readers. How do derivatives show up on the balance sheet exactly, and what prevents me from building a derivative that avoids adding to my capital requirement but which adds risk to my portfolio?
I’ve been getting a lot of different information from people about whether this is possible, or will be possible once Basel III is implemented, but I haven’t reached anyone yet who is actually expert enough to make a definitive claim one way or the other.
It’s one thing if you’re talking about government interest rate swaps, but how do CDS’s, for example, get treated in terms of capital requirements? Is there an implicit probability of default used for accounting purposes? In that case, since such instruments are famously incredibly fat-tailed (i.e. the probability of default looks miniscule until it doesn’t), wouldn’t that encourage everyone to invest extremely heavily in instruments that don’t move their capital ratios much but take on outrageous risks? The devil’s in the detail here.
The regressive domestic complexity tax
I’ve been keeping tabs on hard it is to do my bills. I did my bills last night, and man, I’m telling you, I used all of my organizational abilities, all of my customer service experience, and quite a bit of my alpha femaleness just to get it done. Not to mention I needed more than 2 hours of time which I squeezed out by starting the bills while waiting for take-out.
By the way, I am not one of those sticklers for doing everything myself – I have an accountant, and I don’t read those forms, I just sign them and pray. But even so, removing tax issues from the conversation, the kind of expertise required to do my monthly bills is ridiculous and getting worse.
Take medical bills. I have three kids, so there’s always a few appointments pending, but it’s absolutely amazing to me how often I’m getting charged for appointments unfairly. I recently got charged for a physical for my 10-year-old son, even though I know that physicals are free thanks to ObamaCare.
So I call up my insurance company and complain, spend 15 minutes on the phone waiting, then it turns out he isn’t allowed to have more than one physical in a 12-month period which is why it was charged to me. But wait, he had one last April and one this April, what gives? Turns out last April it was on the 14th and this April it was on the 8th. So less than one year.
But surely, I object, you can’t ask for people to always be exactly 12 months apart or more! It turns out that, yes, they have a 30-day grace period for this exact reason, but for some reason it’s not automatic – it requires a person to call and complain to the insurance company to get their son’s physical covered.
Do you see what I mean? This is not actually a coincidence – insurance companies make big money from having non-automatic grace periods, because many people don’t have the time, the patience, and the pushiness to make them do it right, and that’s free money for insurance companies.
There are the (abstract) “rules” and then there’s what actually happens, and it’s a constant battle between what you know you’re paying for which you shouldn’t be and how much your time is worth. For example, if it’s less than $50 I just pay it even if it’s not reasonable. I’m sure other people have different limits.
I see this as a systemic problem. So this isn’t a diatribe against just insurance companies, because I have to jump through about 15 hoops a month like this just to get my paperwork sorted out, and they are mostly not medical issues. This is really a diatribe against complexity, and the regressive tax that complexity projects onto our society.
Rich people have people to work out their paperwork for them. People like me, we don’t have people to do this, but we have the time, skills, and patience to do it ourselves (and the money to buy takeout while we do it). There are plenty of people with no time, or who aren’t organized to have all the information they need at their fingertips when they make these calls, or are too intimidated by customer service phone lines to work it out.
And, as in the example above, there’s usually a perverse incentive for complexity to exist – people give up and pay extra because it’s not worth doing the paperwork. That means it’s always getting worse.
Bottomline: you shouldn’t need to have a college degree and customer service experience to do your bills. I’d love to see an estimate of how much more in unnecessary fees and accounting errors are paid by the poor in this country.
Payroll cards: “It costs too much to get my money” (#OWS)
If this article from yesterday’s New York Times doesn’t make you want to join Occupy, then nothing will.
It’s about how, if you work at a truly crappy job like Walmart or McDonalds, they’ll pay you with a pre-paid card that charges you for absolutely everything, including checking your balance or taking your money, and will even charge you for not using the card. Because we aren’t nickeling and diming these people enough.
The companies doing this stuff say they’re “making things convenient for the workers,” but of course they’re really paying off the employers, sometimes explicitly:
In the case of the New York City Housing Authority, it stands to receive a dollar for every employee it signs up to Citibank’s payroll cards, according to a contract reviewed by The New York Times.
Thanks for the convenience, payroll card banks!
One thing that makes me extra crazy about this article is how McDonalds uses its franchise system to keep its hands clean:
For Natalie Gunshannon, 27, another McDonald’s worker, the owners of the franchise that she worked for in Dallas, Pa., she says, refused to deposit her pay directly into her checking account at a local credit union, which lets its customers use its A.T.M.’s free. Instead, Ms. Gunshannon said, she was forced to use a payroll card issued by JPMorgan Chase. She has since quit her job at the drive-through window and is suing the franchise owners.
“I know I deserve to get fairly paid for my work,” she said.
The franchise owners, Albert and Carol Mueller, said in a statement that they comply with all employment, pay and work laws, and try to provide a positive experience for employees. McDonald’s itself, noting that it is not named in the suit, says it lets franchisees determine employment and pay policies.
I actually heard about this newish scheme against the poor when I attended the CFPB Town Hall more than a year ago and wrote about it here. Actually that’s where I heard people complain about Walmart doing this but also court-appointed child support as well.
Just to be clear, these fees are illegal in the context of credit cards, but financial regulation has not touched payroll cards yet. Yet another way that the poor are financialized, which is to say they’re physically and psychologically separated from their money. Get on this, CFPB!
Update: an excellent article about this issue was written by Sarah Jaffe a couple of weeks ago (hat tip Suresh Naidu). It ends with an awesome quote by Stephen Lerner: “No scam is too small or too big for the wizards of finance.”
When is smaller better?
It’s another whimsical Sunday morning, a perfect time to re-examine assumptions, and the one I’m working on this morning is when smaller business is actually better, where by “better” I might mean from the perspective of someone inside the business or from the perspective of the public.
I came to this question by way of two articles I’ve read recently.
Women CEO’s
First up we have this article from the Wall Street Journal, written by Sharon Hadary, which is entitled, “Why Are Women-Owned Firms Smaller Than Men-Owned Ones?” and basically wrings its hands about how self-defeating women are when it comes to owning businesses, how they never dream big enough.
Hey, that seems super irrational of women! They’re so self-limiting! Don’t they know that it’s not enough to own your own business, that you should really aspire to owning a business that is really huge?
But you know what? I’ve got a new way of looking at “irrational behavior.” Namely, assume it’s totally rational and figure out what assumptions you’ve got wrong. Let’s stop here and apply this approach. From the article:
Women start businesses to be personally challenged and to integrate work and family, and they want to stay at a size where they personally can oversee all aspects of the business.
Well that was kind of too easy. Turns out that right there, in the article, there’s a rational explanation for a so-called “irrational behavior.” Which is not to say that the writer respects that explanation, of course. Much of the rest of the article focuses how you can convince CEO women that they’re being idiots to think like that.
Of course, that mindset is not the entire story. And to the extent that women’s businesses are small against their will because of sexist behavior and being locked out of credit markets and/or big boy deals, that’s obviously bullshit.
[If I ever become a CEO, I can well imagine wanting to grow it way past the point of understanding or controlling it, because I’m all about being a big swinging dick (BSD), due to my highly robust natural testosterone levels. Because let’s face it, that’s what this is about.]
But if women don’t actually strive to be a BSD in a too-large-to-oversee Fortune 500 company because they’re happy running a smallish profitable business that allows them to see their kids, then why is that a sad story?
CEO pay
Now let’s move to a New York Times article, or really a series of articles, about CEO pay and how it’s big and only getting bigger. As my buddy Suresh explained to me, this is totally inevitable because, as the sizes of companies grow, the size of the CEO’s compensation grows.
Be nerdy with me for a second: if company A and company B merge, you now have a company that’s bigger than A or B, but you only have one CEO whereas you used to have two. So there’s that already, but it doesn’t completely explain it.
Think about the assets of this new company. To the extent that a CEO is supposed to be in charge of 1) not losing, and 2) actually growing these assets, they get some percentage of their “added value”, and that means they get twice as much credit for adding value in a company that’s twice as big.
Now I won’t go deeply into whether CEO’s actually add value – I think, at least in big-ass companies, and in the best-case scenario, CEO mostly they just ooze confidence and allow people to get work done. And I’m not saying this rule of thumb for a certain percentage of assets is reasonable, since it’s a cultural decision. But I do think just complaining about CEO pay being too big is missing the point.
Instead, I think we need to ask whether we think businesses are actually better off being bigger, and for whom. Economists go on and on about how you get economies of scale, but not if things are too big to understand, and not if the real economy of scale is devoted to politics and forming public policy – look at Monsanto for example.
Aunt Pythia: on her death bed
Dear Aunt Pythia Readers,
I’m afraid I have some bad news. Aunt Pythia has been suffering from a lack of (good) questions recently, and is running out of steam. She might well be dead within the week.
Although she’s been supplied with one new good question, as well as a few older questions she’s deemed somewhat lame (and quite a few that are downright obscene), it just doesn’t make sense for her to keep going without a week’s rest, and hopefully some shoring up of her “good questions” list.
Was it the fact that last week’s column was on Sunday? Was it because the questions have become less nerdy and more sex-related? Hard to say, but the truth is Aunt Pythia has been scraping by week to week since the get-go, and this was bound to happen at some point. She’s never yet made up a question, by the way, and considers it below her high-ish standards to do so (although she’s convinced she could make up some doozies if she tried).
Do you want her to die? Maybe you do. In that case: do nothing.
If you are, however, fond of Auntie P, then please take it upon yourself to ask a question below. Hopefully, with some TLC, she will be back on her feet next week. Otherwise, she will be permanently removed as a feature from mathbabe, which would be sad indeed.
Love,
Cathy
——
Here’s your chance to save Aunt Pythia!!
How to understand the career trajectory of Larry Summers
I heard from a Wall Street Journal recently that Summers is on the short list for the Fed Chair. I’m wondering, how often and in how many ways does this guy need to fail before people stop thinking this guy is the silver bullet?
Then I remember this article which talks about a study that connects overconfidence with social status. From the article:
“Our studies found that overconfidence helped people attain social status. People who believed they were better than others, even when they weren’t, were given a higher place in the social ladder. And the motive to attain higher social status thus spurred overconfidence,” says Anderson, the Lorraine Tyson Mitchell Chair in Leadership and Communication II at the Haas School.
Social status is the respect, prominence, and influence individuals enjoy in the eyes of others. Within work groups, for example, higher status individuals tend to be more admired, listened to, and have more sway over the group’s discussions and decisions. These “alphas” of the group have more clout and prestige than other members. Anderson says these research findings are important because they help shed light on a longstanding puzzle: why overconfidence is so common, in spite of its risks. His findings suggest that falsely believing one is better than others has profound social benefits for the individual.
Of course, Larry Summers isn’t the only example of this I can think of, but he’s a pretty perfect one.
How to be wrong
My friend Josh Vekhter sent me this blog post written by someone who calls herself celandine13 and tutors students with learning disabilities.
In the post, she reframes the concept of mistake or “being bad at something” as often stemming from some fundamental misunderstanding or poor procedure:
Once you move it to “you’re performing badly because you have the wrong fingerings,” or “you’re performing badly because you don’t understand what a limit is,” it’s no longer a vague personal failing but a causal necessity. Anyone who never understood limits will flunk calculus. It’s not you, it’s the bug.
This also applies to “lazy.” Lazy just means “you’re not meeting your obligations and I don’t know why.” If it turns out that you’ve been missing appointments because you don’t keep a calendar, then you’re not intrinsically “lazy,” you were just executing the wrong procedure. And suddenly you stop wanting to call the person “lazy” when it makes more sense to say they need organizational tools.
And she wants us to stop with the labeling and get on with the understanding of why the mistake was made and addressing that, like she does when she tutors students. She even singles out certain approaches she considers to be flawed from the start:
This is part of why I think tools like Knewton, while they can be more effective than typical classroom instruction, aren’t the whole story. The data they gather (at least so far) is statistical: how many questions did you get right, in which subjects, with what learning curve over time? That’s important. It allows them to do things that classroom teachers can’t always do, like estimate when it’s optimal to review old material to minimize forgetting. But it’s still designed on the error model. It’s not approaching the most important job of teachers, which is to figure out why you’re getting things wrong — what conceptual misunderstanding, or what bad study habit, is behind your problems. (Sometimes that can be a very hard and interesting problem. For example: one teacher over many years figured out that the grammar of Black English was causing her students to make conceptual errors in math.)
On the one hand I like the reframing: it’s always good to see knee-jerk reactions become more contemplative, and it’s always good to see people trying to help rather than trying to blame. In fact, one of my tenets of real life is that mistakes will be made, and it’s not the mistake that we should be anxious about but how we act to fix the mistake that exposes who we are as people.
I would, however, like to take issue with her anti-example in the case of Knewton, which is an online adaptive learning company. Full disclosure: I interviewed with Knewton before I took my current job, and I like the guys who work there. But, I’d add, I like them partly because of the healthy degree of skepticism they take with them to their jobs.
What the blogwriter celandine13 is pointing out, correctly, is that understanding causality is pretty awesome when you can do it. If you can figure out why someone is having trouble learning something, and if you can address that underlying issue, then fixing the consequences of that issue get a ton easier. Agreed, but I have three points to make:
- First, a non-causal data mining engine such as Knewton will also stumble upon a way to fix the underlying problem by dint of having a ton of data and noting that people who failed a calculus test, say, did much better after having limits explained to them in a certain way. This is much like the spellcheck engine of Google works by keeping track of previous spelling errors, and not by mind reading how people think about spelling wrong.
- Second, it’s not always easy to find the underlying cause of bad testing performance, even if you’re looking for it directly. I’m not saying it’s fruitless – tutors I know are incredibly good at that – but there’s room for both “causality detectives” and tons of smart data mining in this field.
- Third, it’s definitely not always easy to address the underlying cause of bad test performance. If you find out that the grammar of Black English affects students’ math test scores, what do you do about it?
Having said all that, I’d like to once more agree with the underlying message that a mistake is a first and foremost a signal rather than a reflection of someone’s internal thought processes. The more we think of mistakes as learning opportunities the faster we learn.
When is math like a microwave?
When I worked as a research mathematician, I was always flabbergasted by the speed at which other people would seem to absorb mathematical theory. I had then, and pretty much have now, this inability to believe anything that I can’t prove from first principles, or at least from stuff I already feel completely comfortable with. For me, it’s essentially mathematically unethical to use a result I can’t prove or at least understand locally.
I only recently realized that not everyone feels this way. Duh. People often just assemble accepted facts about a field quickly just to explore the landscape and get the feel for something – it makes complete sense to me now that one can do this and it doesn’t seem at all weird. And it explains what I saw happening in grad school really well too.
Most people just use stuff they “know to be true,” without having themselves gone through the proof. After all, things like Deligne’s work on Weil Conjectures or Gabber’s recent work on finiteness of etale cohomology for pseudo-excellent schemes are really fucking hard, and it’s much more efficient to take their results and use them than it is to go through all the details personally.
After all, I use a microwave every day without knowing how it works, right?
I’m not sure I know where I got the feeling that this was an ethical issue. Probably it happened without intentional thought, when I was learning what a proof is in math camp, and I’d perhaps state a result and someone would say, how do you know that? and I’d feel like an asshole unless I could prove it on the spot.
Anyway, enough about me and my confused definition of mathematical ethics – what I now realize is that, as mathematics is developed more and more, it will become increasingly difficult for a graduate student to learn enough and then prove an original result without taking things on faith more and more. The amount of mathematical development in the past 50 years is just frighteningly enormous, especially in certain fields, and it’s just crazy to imagine someone learning all this stuff in 2 or 3 years before working on a thesis problem.
What I’m saying, in other words, is that my ethical standards are almost provably unworkable in modern mathematical research. Which is not to say that, over time, a person in a given field shouldn’t eventually work out all the details to all the things they’re relying on, but it can’t be linear like I forced myself to work.
And there’s a risk, too: namely, that as people start getting used to assuming hard things work, fewer mistakes will be discovered. It’s a slippery slope.
Who stays off the data radar?
Last night’s Data Skeptics Meetup talk by Suresh Naidu was great, as I suspected it would be. I’m not going to be able to cover everything he talked about (a discussion is forming here as well) but I’ll touch on a few things related to my chosen topic for the day, namely who stays off the data radar.
In his talk Suresh discussed the history of governments tracking people with data, which more or less until recently was the history of the census. The issue of trust or lack thereof that people have in being classified and tracked has been central since the get-go, and with it the understanding by the data collectors that people respond differently to data collection when they anticipate it being used against them.
Among other examples he mentioned the efforts of the U.S. Census Bureau to stay independent (specifically, away from any kind of tax decisions) in order to be trusted but then turning around during war time and using census tracks to put Japanese into internment camps.
It made me wonder, who distrusts data collection so much that they manage to stay off the data radar?
Suresh gave quite a few examples of people who did this out of fear of persecution or what have you, and because, at least in the example of the Domesday Book, once land ownership was written down it was somehow “more official and objective” than anything else, which of course resulted in some people getting screwed out of their land.
It’s not just a historical problem, of course: it’s still true that certain populations, especially illegal immigrant populations, are afraid of how the census will be used and go undercounted. Who can say when the census might start being used to deport illegal immigrants?
As a kind of anti example, he mentioned that the census was essentially canceled in 1920 because the South knew that so many ex-slaves were moving north that their representation in government was growing weak. I say anti-example because in this case it wasn’t out of distrust, to avoid detection, but it was a savvy and political move, to remain looking large.
What about the modern version of government tracking? In this case, of course, it’s not just census data, but anything else the NSA happens to collect about us. I’m no expert (tell me if you know data on this) but I will hazard a guess on who avoids being tracked:
- Old people who don’t have computers and never have,
- Members of hacking group Anonymous who know how it works and how to bypass the system, and
- People who have worked or are now working at the NSA.
Of course there are a few other rare people that just happen to care enough about privacy to educate themselves on how to avoid being tracked. But it’s hard to do, obviously.
Let me soften the requirements a bit – instead of staying off the radar completely, who makes it really hard to find them?
If you’re talking about individuals, I’d start with this answer: politicians. In my work with Peter Darche and Lee Drutman from the Sunlight Foundation (blog post coming soon!) trying to follow money in politics, it’s amazed me time and time again how difficult it’s been to put together the political events for a given politician – events that are individually publicly recorded but are seemingly intentionally siloed so it will be extremely difficult to put together a narrative. Thanks to Peter’s recent efforts, and the Sunlight Foundations long-term efforts, we are getting to the point where we can do this, but it’s been a data munging problem from hell.
If you’re generalizing to entities and corporations, then the “making data collection hard” award should probably go to the corporations with hundreds of subsidiaries all over the world which now don’t even need to be reported on tax forms.
Funny how the very people who know the most about how data can be used are paranoid about being tracked.
Tonight: first Data Skeptics Meetup, Suresh Naidu
I’m psyched to see Suresh Naidu tonight in the first Data Skeptics Meetup. He’s talking about Political Uses and Abuses of Data and his abstract is this:
While a lot has been made of the use of technology for election campaigns, little discussion has focused on other political uses of data. From targeting dissidents and tax-evaders to organizing protests, the same datasets and analytics that let data scientists do prediction of consumer and voter behavior can also be used to forecast political opponents, mobilize likely leaders, solve collective problems and generally push people around. In this discussion, Suresh will put this in a 1000 year government data-collection perspective, and talk about how data science might be getting used in authoritarian countries, both by regimes and their opponents.
Given the recent articles highlighting this kind of stuff, I’m sure the topic will provoke a lively discussion – my favorite kind!
Unfortunately the Meetup is full but I’d love you guys to give suggestions for more speakers and/or more topics.
Ask Aunt Pythia and Cousin Lily: Sunday edition
Readers, Aunt Pythia’s confusion from traveling got her all mixed up and she forgot to distribute her pearls of wisdom yesterday on account of: she thought it was Friday. She is sincerely sorry, it won’t happen again.
Aunt Pythia is extremely grateful and pleased to announce that today she has help from a guest advice-giver, namely Cousin Lily, who specializes in sage advice for kinky people, or wanna-be kinky people.
We’ll start out with Cousin Lily’s advice, running the risk that nobody will bother to read anything else, since it’s much more interesting than anything Aunt Pythia knows about.
By the way, if you don’t know what you’re in for, go here for past advice columns and here for an explanation of the name Pythia. Most importantly,
Submit your question for Aunt Pythia at the bottom of this page!
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Dear Aunt Pythia/ Cousin Lily,
Since I promised: here’s the follow-up question(s). My partner and I have a great sex life, but perhaps you have some advice on how to make it better. Her kink is that she’s submissive. I’m pretty vanilla in this area- it’s mostly obliviousness on my part. It had never occurred to me that sex and power were anything but orthogonal, to be nerdy about it.
I have two things I’m puzzling over. First, I’m used to asking someone what they like in bed – nobody’s a mind reader, after all. However, if I ask, then I’m not really being dominant. Any way around this?
Second, I know the bedroom isn’t real life, but I have a real problem with anything that even has undertones of treating her badly (no play humiliation etc). I’ve figured out some activities that we both enjoy (e.g. telling her to make me a cake while naked, wrestling). I think she would like it if I pushed the boundaries a bit more. Any ideas on how to disassociate slightly more the bedroom from real life in my mind?
OK I’ll Bite
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Dear OK,
Please read “The Bottoming Book” by Hardy and Easton, stat. I highly recommend reading it together and letting it start some conversations between you. This book will help you understand the things that might be motivating your submissive partner, ways to explore Dominant/submissive (D/s) play safely (both physically and emotionally), and techniques for handling the times that things don’t go perfectly.
It is really common for “vanilla” partners to feel uncomfortable about their submissive partner’s desires regarding power and potentially things like pain and/or humiliation. But once you understand what motivates your submissive’s kink and what she is hoping to get from the experience, you will feel much more at ease about providing that. Not every sub is submissive in the same way or for the same reasons — understanding your sub’s kink (and finding out whether she understands it herself) will make the whole experience much more accessible. This better understanding will also allow you to view the exchange as your providing pleasure of a specific kind, rather than pain/abuse/etc.
NO MIND READING should be expected by either party. That is a recipe for disaster on both sides. Talk through in detail what each of you expects and wants, and do it long before you get into bed (although this discussion doesn’t have to be clinical — it can totally be sexy, just do it at the bar and not in the bedroom so that each person has time to reflect and react). This can be a tool to ease anxiety, but it can also be a tool to build anticipation: in other words, a total turn-on. Asking for input doesn’t mean you’re not being dominant; ask your sub to tell you the range of things that she would find sexy, and then YOU CHOOSE which of those things happen, or in what order, or when (depending on what you negotiate together). That is totally dom.
Take baby steps. It doesn’t have to be perfect the first time, or any time. Don’t go farther than you’re comfortable with, and trade feedback after each encounter. You should also develop techniques for getting feedback during your encounters. You can do this while remaining dominant: “How does that feel?” “I don’t like it, Sir.” “I didn’t ask if you liked it.” [but then you back off anyway, maybe after just one more prolonged second]. Just communicate, communicate, communicate. And read the book.
Finally, try to stop thinking about this as “[disassociating] the bedroom from real life”. We humans are complex creatures with multiple moods and identities. You don’t share the same side of yourself with your college friends that you do with your grandparents (hopefully), but that doesn’t make one experience more a part of “real life” than the other. Similarly, power dynamics in the bedroom are simply a way of exploring different parts of ourselves, and to fully explore your partner and all the levels of complexity she has to offer as a full human being is the most intimate, wonderful thing you could do — why would you ever want to disassociate that from the rest of your lives together? Embrace that part of her, and embrace whatever part of yourself engages with her inner submissive. I promise, it will add a new and rich dimension to your “real life” relationship if you let it.
For further reading and more specific guidance about exploring dominance, check out “The Topping Book” (also by Hardy and Easton) or “The Loving Dominant” (specific to male, heterosexual doms). Have fun!
Cousin Lily
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Dear Aunt Pythia,
I want to preface my question by saying that I’ve read your posts about Big Data as Big Brother and NSA Mathematicians and I definitely appreciate that there are a lot of very serious, very heavy issues involved in your discussions.
However, reading them has also made me worried about a potentially more nontrivial issue: should I be concerned that the porn I watch shows up in my browser history? The stuff I watch is all firmly mainstream, but you’ve said before that a person’s google search histories (given that they’re logged into gmail) are basically stored forever and could potentially be bought by agencies/companies looking to vet prospective employees.
And given that I’m a grad student who’s in a long distance relationship, my internet history would read to others like: math, math, math, porn, porn, math… Which is clearly not an impression I want to give of myself. Am I being needlessly paranoid?
Paranoia Generally Leads (2, Craziness)
Dear PGL(2,C),
That might be the nerdiest abbreviation I’ve ever seen. Hear hear! I would have answered your question simply based on that alone, but I actually want to address your very good question as well.
Here’s the thing: you have to understand that everyone watches porn. Or, if not everyone, than almost everyone. So yes, although your electronic footprint is going to have an enormous amount of smut attached to it, you’d only need to worry if your smut level is somehow much larger than the average guy’s smut level. And honestly, it doesn’t sound that way at all, given that 4 out of 6 example clicks above were mathy.
In other words, be nerdy with me for a moment and look at an extreme edge case where your browser history is absolutely transparent to anyone, including future employers, but so is everyone else’s. Then it’s a game of relativity: are you going to stand out as a huge perv? Not a chance. If, over time, people more and more start getting smart about hiding their smut, say by using a separate browser or going into incognito windows on Chrome, and it becomes the norm not to have a bunch of porn in your history, then not doing so will make you stand out. But honestly we’re not there yet.
And also, we’re not there yet for that edge case where your history is completely known. The truth is most employers that you’d work for as a mathematician don’t even pry into this kind of thing – it’s mostly a problem for shitty jobs at Walmart, where they’re trying to decide if you’re going to be a good robot or if you’re going to cause trouble, or if you work for the NSA or something.
So don’t fret! And good luck. What with a long distance relationship during grad school, you’re going to need it.
Aunt Pythia
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Dear Aunt Pythia,
I’d be a bit of a wet blanket to state Lybridos is ineffective, plus there’s a lot of money at stake. So I’m skeptical about the supporting research (e.g. a woman’s sex drive in a relationship plummets in a relationship, except maybe if they’re with a whaler). What are the warning signa as to whether such research is dodgy?
More Sex Research Please
Dear MSRP,
First of all, “MSRP” stands for “manufacturer’s suggested retail price” and it doesn’t do much for me. Please take copious notes from PGL(2, C) above.
Second, when I first read your question, I honestly wondered if it was written in English. I mean, there are references to all sorts of things I’ve never heard about – Lybridos? Great sex with whalers? – and then the actual question asks me to comment on research that’s unnamed. I’m sure you can do better! Just throw in a few url’s and we’d be good!
I managed to figure out what Lybridos is, since googling that isn’t so hard, but for the whaling comment I got nothing, and in the end I can’t figure out which research is or is not dodgy. So please do write back with references, especially for the sex-with-whalers comment, which especially intrigues me, thanks.
I did want to address the inherent topic here, though, namely of women’s sex drive and getting pills for it. Namely, I’m all for it. In fact I’d like to read profiles in the New York Times about a gaggle of 50-year-old women, preferably in the same knitting circle, who started taking pills to get their sex life kick-started, and it worked, and now their husbands are too exhausted to keep up so they (the husbands) hired extra men to come in and assist.
Why? Because the narrative on men and women’s sexuality is totally distorted and always paints the picture of women avoiding sex and men wanting and needing it. I honestly think this myth is perpetuated for the sake of men’s egos. Or, to be generous, it’s a survivorship bias problem, since married couples go to the doctor and complain when women lose interest but they don’t do the same thing when men do. Judging from my girlfriends, though, there’s no national crisis of women not being interested in sex. Of course there’s also a bias in the sample of women who are my girlfriends, but I must expect the truth to be somewhere in the middle.
I look forward to your more precise question next time!
Aunt Pythia
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Please submit your well-specified, cleverly-abbreviated question to Aunt Pythia!
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