This is a guest post by Leopold Dilg.
There’s little chance we can underestimate our American virtues, since our overlords so seldom miss an opportunity to point them out. A case in point – in fact, le plus grand du genre, though my fingers tremble as I type that French expression, for reasons I’ll explain soon enough – is the Cadillac commercial that interrupted the broadcast of the Olympics every few minutes.
A masterpiece of casting and directing and location scouting, the ad follows a middle-aged man, muscular enough but not too proud to show a little paunch – manifestly a Master of the Universe – strutting around his chillingly modernist $10 million vacation house (or is it his first or fifth home? no matter), every pore oozing the manly, smirky bearing that sent Republican country-club women swooning over W.
It starts with Our Hero, viewed from the back, staring down his infinity pool. He pivots and stares down the viewer. He shows himself to be one of the more philosophical species of the MotU genus. “Why do we work so hard?” he puzzles. “For this? For stuff?….” We’re thrown off balance: Will this son of Goldman Sachs go all Walden Pond on us? Fat chance.
Now, still barefooted in his shorts and polo shirt, he’s prowling his sleak living room (his two daughters and stay-at-home wife passively reading their magazines and ignoring the camera, props in his world no less than his unused pool and The Car yet to be seen) spitting bile at those foreign pansies who “stop by the café” after work and “take August off!….OFF!” Those French will stop at nothing.
“Why aren’t YOU like that,” he says, again staring us down and we yield to the intimidation. (Well gee, sir, of course I’m not. Who wants a month off? Not me, absolutely, no way.) “Why aren’t WE like that” he continues – an irresistible demand for totalizing merger. He’s got us now, we’re goose-stepping around the TV, chanting “USA! USA! No Augusts off! No Augusts off!”
No, he sneers, we’re “crazy, hardworking believers.” But those Frogs – the weaklings who called for a double-check about the WMDs before we Americans blasted Iraqi children to smithereens (woops, someone forgot to tell McDonalds, the official restaurant of the U.S. Olympic team, about the Freedom Fries thing; the offensive French Fries are THERE, right in our faces in the very next commercial, when the athletes bite gold medals and the awe-struck audience bites chicken nuggets, the Lunch of Champions) – might well think we’re “nuts.”
“Whatever,” he shrugs, end of discussion, who cares what they think. “Were the Wright Brothers insane? Bill Gates? Les Paul?… ALI?” He’s got us off-balance again – gee, after all, we DO kinda like Les Paul’s guitar, and we REALLY like Ali.
Of course! Never in a million years would the hip jazz guitarist insist on taking an August holiday. And the imprisoned-for-draft-dodging boxer couldn’t possibly side with the café-loafers on the WMD thing. Gee, or maybe…. But our MotU leaves us no time for stray dissenting thoughts. Throwing lunar dust in our eyes, he discloses that WE were the ones who landed on the moon. “And you know what we got?” Oh my god, that X-ray stare again, I can’t look away. “BORED. So we left.” YEAH, we’re chanting and goose-stepping again, “USA! USA! We got bored! We got bored!”
Gosh, I think maybe I DID see Buzz Aldrin drumming his fingers on the lunar module and looking at his watch. “But…” – he’s now heading into his bedroom, but first another stare, and pointing to the ceiling – “…we got a car up there, and left the keys in it. You know why? Because WE’re the only ones goin’ back up there, THAT’s why.” YES! YES! Of COURSE! HE’S going back to the moon, I’M going back to the moon, YOU’RE going back to the moon, WE’RE ALL going back to the moon. EVERYONE WITH A U.S. PASSPORT is going back to the moon!!
Damn, if only the NASA budget wasn’t cut after all that looting by the Wall Street boys to pay for their $10 million vacation homes, WE’D all be going to get the keys and turn the ignition on the rover that’s been sitting 45 years in the lunar garage waiting for us. But again – he must be reading our mind – he’s leaving us no time for dissent, he pops immediately out of his bedroom in his $12,000 suit, gives us the evil eye again, yanks us from the edge of complaint with a sharp, “But I digress!” and besides he’s got us distracted with the best tailoring we’ve ever seen.
Finally, he’s out in the driveway, making his way to the shiny car that’ll carry him to lower Manhattan. (But where’s the chauffer? And don’t those MotUs drive Mazerattis and Bentleys? Is this guy trying to pull one over on the suburban rubes who buy Cadillacs stupidly thinking they’ve made it to the big time?)
Now the climax: “You work hard, you create your own luck, and you gotta believe anything is possible,” he declaims.
Yes, we believe that! The 17 million unemployed and underemployed, the 47 million who need food stamps to keep from starving, the 8 million families thrown out of their homes – WE ALL BELIEVE. From all the windows in the neighborhood, from all the apartments across Harlem, from Sandy-shattered homes in Brooklyn and Staten Island, from the barren blast furnaces of Bethlehem and Youngstown, from the foreclosed neighborhoods in Detroit and Phoenix, from the 70-year olds doing Wal-mart inventory because their retirement went bust, from all the kitchens of all the families carrying $1 trillion in college debt, I hear the national chant, “YOU MAKE YOUR OWN LUCK! YOU MAKE YOUR OWN LUCK!”
And finally – the denouement – from the front seat of his car, our Master of the Universe answers the question we’d all but forgotten. “As for all the stuff? That’s the upside of taking only two weeks off in August.” Then the final cold-blooded stare and – too true to be true – a manly wink, the kind of wink that makes us all collaborators and comrades-in-arms, and he inserts the final dagger: “N’est-ce pas?”
This is a guest post by Manya Raman-Sundström.
If you talk to a mathematician about what she or he does, pretty soon it will surface that one reason for working those long hours on those difficult problems has to do with beauty.
Whatever we mean by that term, whether it is the way things hang together, or the sheer simplicity of a result found in a jungle of complexity, beauty – or aesthetics more generally—is often cited as one of the main rewards for the work, and in some cases the main motivating factor for doing this work. Indeed, the fact that a proof of known theorem can be published just because it is more elegant is one evidence of this fact.
Mathematics is beautiful. Any mathematician will tell you that. Then why is it that when we teach mathematics we tend not to bring out the beauty? We would consider it odd to teach music via scales and theory without ever giving children a chance to listen to a symphony. So why do we teach mathematics in bits and pieces without exposing students to the real thing, the full aesthetic experience?
Of course there are marvelous teachers out there who do manage to bring out the beauty and excitement and maybe even the depth of mathematics, but aesthetics is not something we tend to value at a curricular level. The new Common Core Standards that most US states have adopted as their curricular blueprint do not mention beauty as a goal. Neither do the curriculum guidelines of most countries, western or eastern (one exception is Korea).
Mathematics teaching is about achievement, not about aesthetic appreciation, a fact that test-makers are probably grateful for – can you imagine the makeover needed for the SAT if we started to try to measure aesthetic appreciation?
Why Does Beauty Matter?
First, it should be a bit troubling that our mathematics classrooms do not mirror practice. How can young people make wise decisions about whether they should continue to study mathematics if they have never really seen mathematics?
Second, to overlook the aesthetic components of mathematical thought might be to preventing our children from developing their intellectual capacities.
In the 1970s Seymour Papert , a well-known mathematician and educator, claimed that scientific thought consisted of three components: cognitive, affective, and aesthetic (for some discussion on aesthetics, see here).
At the time, research in education was almost entirely cognitive. In the last couple decades, the role of affect in thinking has become better understood, and now appears visibly in national curriculum documents. Enjoying mathematics, it turns out, is important for learning it. However, aesthetics is still largely overlooked.
Recently Nathalie Sinclair, of Simon Frasier University, has shown that children can develop aesthetic appreciation, even at a young age, somewhat analogously to mathematicians. But this kind of research is very far, currently, from making an impact on teaching on a broad scale.
Once one starts to take seriously the aesthetic nature of mathematics, one quickly meets some very tough (but quite interesting!) questions. What do we mean by beauty? How do we characterise it? Is beauty subjective, or objective (or neither? or both?) Is beauty something that can be taught, or does it just come to be experienced over time?
These questions, despite their allure, have not been fully explored. Several mathematicians (Hardy, Poincare, Rota) have speculated, but there is no definite answer even on the question of what characterizes beauty.
To see why these questions might be of interest to anyone but hard-core philosophers, let’s look at an example. Consider the famous question, answered supposedly by Gauss, of the sum of the first n integers. Think about your favorite proof of this. Probably the proof that did NOT come to your mind first was a proof by induction:
Prove that S(n) = 1 + 2 + 3 … + n = n (n+1) /2
S(k + 1) = S(k) + (k + 1)
= k(k + 1)/2 + 2(k + 1)/2
= k(k + 1)/2 + 2(k + 1)/2
= (k + 1)(k + 2)/2.
Now compare this proof to another well known one. I will give the picture and leave the details to you:
Does one of these strike you as nicer, or more explanatory, or perhaps even more beautiful than the other? My guess is that you will find the second one more appealing once you see that it is two sequences put together, giving an area of n (n+1), so S(n) = n (n+1)/2.
Note: another nice proof of this theorem, of course, is the one where S(n) is written both forwards and backwards and added. That proof also involves a visual component, as well as an algebraic one. See here for this and a few other proofs.
Beauty vs. Explanation
How often do we, as teachers, stop and think about the aesthetic merits of a proof? What is it, exactly, that makes the explanatory proof more attractive? In what way does the presentation of the proof make the key ideas accessible, and does this accessibility affect our sense of understanding, and what underpins the feeling that one has found exactly the right proof or exactly the right picture or exactly the right argument?
Beauty and explanation, while not obvious related (see here), might at least be bed-fellows. It may be the case that what lies at the bottom of explanation — a feeling of understanding, or a sense that one can ”see” what is going on — is also related to the aesthetic rewards we get when we find a particularly good solution.
Perhaps our minds are drawn to what is easiest to grasp, which brings us back to central questions of teaching and learning: how do we best present mathematics in a way that makes it understandable, clear, and perhaps even beautiful? These questions might all be related.
Workshop on Math Beauty
This March 10-12, 2014 in Umeå, Sweden, a group will gather to discuss this topic. Specifically, we will look at the question of whether mathematical beauty has anything to do with mathematical explanation. And if so, whether the two might have anything to do with visualization.
If this discussion peaks your interest at all, you are welcome to check out my blog on math beauty. There you will find a link to the workshop, with a fantastic lineup of philosophers, mathematicians, and mathematics educators who will come together to try to make some progress on these hard questions.
Thanks to Cathy, the always fabulous mathbabe, for letting me take up her space to share the news of this workshop (and perhaps get someone out there excited about this research area). Perhaps she, or you if you have read this far, would be willing to share your own favorite examples of beautiful mathematics. Some examples have already been collected here, please add yours.
This is a guest post by Tom Adams, who spent over 20 years in the securitization business and now works as an attorney and consultant and expert witness on MBS, CDO and securitization related issues. Jointly posted with Naked Capitalism.
Last week, the rules for the Volcker Rule – that provision of the Dodd-Frank Legislation that was intended to prevent (reduce?) proprietary trading by banks – were finalized. As a consequence, there has been a lot of chatter among financial types around the internet about what the rule does and doesn’t do and how it is good or bad, etc. Much of the conversation falls into the category of noise and distraction about unintended consequences, impacts on liquidity and broad views of regulatory effectiveness.
I call it noise, because in my view the real purpose of the Volcker Rule is to prevent another Citigroup bailout and therefore the measure of its effectiveness is whether the rule would accomplish this.
As you may recall, Citigroup required the largest bailout in government history in 2008, going back to the government well for more bailout funds several times. The source of Citigroup’s pain was almost entirely due to its massive investment in the ABS CDO machine. Of course, at the time of Citi’s bailout, there was a lot of noise about the potential financial system collapse and the risk posed by numerous other banks and institutions, so Citi as the main target of the TARP bailout, and ABS CDOs as the main cause of Citi’s pain, often gets lost in the folds of history.
The CDO market
In the years leading up to the financial crisis, Citi was an active underwriter for CDO’s backed by mortgage backed securities. Selling these securities was a lucrative business for Citi and other banks – far more lucrative than the selling of the underlying MBS. The hard part of selling was finding someone to take the highest risk piece (called the equity) of the CDO, but that problem got solved when Magnetar and other hedge funds came along with their ingenious shorting scheme.
The next hardest part was finding someone to take the risk of the very large senior class of the CDO, often known as the super-senior class (it was so named because it was enhanced at levels above that needed for a AAA rating).
For a time, Citi relied on a few overseas buyers and some insurance companies – like AIG and monoline bond insurers – to take on that risk. In addition, the MBS market became heavily reliant upon CDOs to buy up the lower rated bonds from MBS securitizations.
As the frenzy of MBS selling escalated, though, the number of parties willing to take on the super-seniors was unable to match the volume of CDOs being created (AIG, for instance, pulled back from insuring the bonds in 2006). Undeterred, Citi began to take down the super-senior bonds from the deals they were selling and holding them as “investments” which required very little capital because they were AAA.
This approach enabled Citi to continue the vey lucrative business of selling CDOs (to themselves!), which also enhanced their ability to create and sell MBS (to their CDOs), which enabled Citi to keep the music playing and the dance going, to paraphrase their then CEO Chuck Prince.
The CDO music stopped in July, 2007 with the rating agency downgrades of hundreds of the MBS bonds underlying the CDOs that had been created over the prior 24 months. MBS and CDO issuance effectively shut down the following month and remained shut throughout the crisis. The value of CDOs almost immediately began to plummet, leading to large mark-to-market losses for the parties that insured CDOs, such as Ambac and MBIA.
Citi managed to ignore the full extent of the declines in the value of the CDOs for nearly a year, until AIG ran into its troubles (itself a result of the mark-to-market declines in the values of its CDOs). When, in the fall of 2008, Citi finally fessed up to the problems it was facing, it turned out it was holding super-senior CDOs with a face value of about $150 billion which were now worth substantially less.
How much less? The market opinion at the time was probably around 10-20 cents on the dollar. Some of that value recovered in the last two years, but the bonds were considered fairly worthless for several years. Citi’s difficulty in determining exactly how little the CDOs were worth and how many they held was the primary reason for the repeated requests for additional bailout money.
Citi’s bailout is everyone’s bailout
The Citi bailout was a huge embarrassment for the company and the regulators that oversaw the company (including the Federal Reserve) for failing to prevent such a massive aid package. Some effort was made, at the time TARP money was distributed, to obscure Citi’s central role in the need for TARP and the panic the potential for a Citi failure was causing in the market and at the Treasury Department (see for example this story and the SIGTARP report). By any decent measure, Citi should have been broken up after this fiasco, but at least some effort should be made from a large bank ever needing such a bailout again, right?
Volcker’s Rule is Citi’s rule
So the test for whether the Volcker Rule is effective is fairly simple: will it prevent Citi, or some other large institution, from getting in this situation again? The rule is relatively complex and armies of lawyers are dissecting it for ways to arbitrage its words as we speak.
However, some evidence has emerged that the Volcker Rule would be effective in preventing another Citi fiasco. While the bulk of the rules don’t become effective until 2015, banks are required to move all “covered assets” from held to maturity to held for sale, which requires them to move the assets to a fair market valuation from… whatever they were using before.
Just this week, for example, Zions Bank announced that they were taking a substantial impairment because of that rule and moving a big chunk of CDOs (trust preferred securities, or TRUPS, were the underlying asset, although the determination would apparently apply to all CDOs) to fair market accounting from… whatever valuation they were using previously (not fair market?).
Here’s the key point. Had Citi been forced to do this as they acquired their CDOs, there is a decent chance they would have run into CDO capacity problems much sooner – they may not have been able to rely on the AAA ratings, they might have had to sell off some of the bonds before the market imploded, and they might have had to justify their valuations with actual data rather than self-serving models.
As a secondary consequence, they probably would have had to stop buying and originating mortgage loans and buying and selling MBS, because they wouldn’t have been able to help create CDOs to dump them into.
Given the size of Citi’s CDO portfolio, and the leverage that those CDOs had as it relates to underlying mortgage loans (one $1 billion CDO was backed by MBS from about $10 billion mortgages, $150 billion of CDOs would have been backed by MBS from about $1.5 trillion of mortgage loans, theoretically), if Citi had slowed their buying of CDOs, it might have had a substantial cooling effect on the mortgage market before the crisis hit.
This is a guest post by Nicholas Diakopoulos, a Tow Fellow at the Columbia University Graduate School of Journalism where he is researching the use of data and algorithms in the news. You can find out more about his research and other projects on his website or by following him on Twitter. Crossposted from engenhonetwork with permission from the author.
How can we know the biases of a piece of software? By reverse engineering it, of course.
When was the last time you read an online review about a local business or service on a platform like Yelp? Of course you want to make sure the local plumber you hire is honest, or that even if the date is dud, at least the restaurant isn’t lousy. A recent survey found that 76 percent of consumers check online reviews before buying, so a lot can hinge on a good or bad review. Such sites have become so important to local businesses that it’s not uncommon for scheming owners to hire shills to boost themselves or put down their rivals.
To protect users from getting duped by fake reviews Yelp employs an algorithmic review reviewer which constantly scans reviews and relegates suspicious ones to a “filtered reviews” page, effectively de-emphasizing them without deleting them entirely. But of course that algorithm is not perfect, and it sometimes de-emphasizes legitimate reviews and leaves actual fakes intact—oops. Some businesses have complained, alleging that the filter can incorrectly remove all of their most positive reviews, leaving them with a lowly one- or two-stars average.
This is just one example of how algorithms are becoming ever more important in society, for everything from search engine personalization, discrimination, defamation, and censorship online, to how teachers are evaluated, how markets work, how political campaigns are run, and even how something like immigration is policed. Algorithms, driven by vast troves of data, are the new power brokers in society, both in the corporate world as well as in government.
They have biases like the rest of us. And they make mistakes. But they’re opaque, hiding their secrets behind layers of complexity. How can we deal with the power that algorithms may exert on us? How can we better understand where they might be wronging us?
Transparency is the vogue response to this problem right now. The big “open data” transparency-in-government push that started in 2009 was largely the result of an executive memo from President Obama. And of course corporations are on board too; Google publishes a biannual transparency report showing how often they remove or disclose information to governments. Transparency is an effective tool for inculcating public trust and is even the way journalists are now trained to deal with the hole where mighty Objectivity once stood.
But transparency knows some bounds. For example, though the Freedom of Information Act facilitates the public’s right to relevant government data, it has no legal teeth for compelling the government to disclose how that data was algorithmically generated or used in publicly relevant decisions (extensions worth considering).
Moreover, corporations have self-imposed limits on how transparent they want to be, since exposing too many details of their proprietary systems may undermine a competitive advantage (trade secrets), or leave the system open to gaming and manipulation. Furthermore, whereas transparency of data can be achieved simply by publishing a spreadsheet or database, transparency of an algorithm can be much more complex, resulting in additional labor costs both in creation as well as consumption of that information—a cognitive overload that keeps all but the most determined at bay. Methods for usable transparency need to be developed so that the relevant aspects of an algorithm can be presented in an understandable way.
Given the challenges to employing transparency as a check on algorithmic power, a new and complementary alternative is emerging. I call it algorithmic accountability reporting. At its core it’s really about reverse engineering—articulating the specifications of a system through a rigorous examination drawing on domain knowledge, observation, and deduction to unearth a model of how that system works.
As interest grows in understanding the broader impacts of algorithms, this kind of accountability reporting is already happening in some newsrooms, as well as in academic circles. At the Wall Street Journal a team of reporters probed e-commerce platforms to identify instances of potential price discrimination in dynamic and personalized online pricing. By polling different websites they were able to spot several, such as Staples.com, that were adjusting prices dynamically based on the location of the person visiting the site. At the Daily Beast, reporter Michael Keller dove into the iPhone spelling correction feature to help surface patterns of censorship and see which words, like “abortion,” the phone wouldn’t correct if they were misspelled. In my own investigation for Slate, I traced the contours of the editorial criteria embedded in search engine autocomplete algorithms. By collecting hundreds of autocompletions for queries relating to sex and violence I was able to ascertain which terms Google and Bing were blocking or censoring, uncovering mistakes in how these algorithms apply their editorial criteria.
All of these stories share a more or less common method. Algorithms are essentially black boxes, exposing an input and output without betraying any of their inner organs. You can’t see what’s going on inside directly, but if you vary the inputs in enough different ways and pay close attention to the outputs, you can start piecing together some likeness for how the algorithm transforms each input into an output. The black box starts to divulge some secrets.
Algorithmic accountability is also gaining traction in academia. At Harvard, Latanya Sweeney has looked at how online advertisements can be biased by the racial association of names used as queries. When you search for “black names” as opposed to “white names” ads using the word “arrest” appeared more often for online background check service Instant Checkmate. She thinks the disparity in the use of “arrest” suggests a discriminatory connection between race and crime. Her method, as with all of the other examples above, does point to a weakness though: Is the discrimination caused by Google, by Instant Checkmate, or simply by pre-existing societal biases? We don’t know, and correlation does not equal intention. As much as algorithmic accountability can help us diagnose the existence of a problem, we have to go deeper and do more journalistic-style reporting to understand the motivations or intentions behind an algorithm. We still need to answer the question of why.
And this is why it’s absolutely essential to have computational journalists not just engaging in the reverse engineering of algorithms, but also reporting and digging deeper into the motives and design intentions behind algorithms. Sure, it can be hard to convince companies running such algorithms to open up in detail about how their algorithms work, but interviews can still uncover details about larger goals and objectives built into an algorithm, better contextualizing a reverse-engineering analysis. Transparency is still important here too, as it adds to the information that can be used to characterize the technical system.
Despite the fact that forward thinkers like Larry Lessig have been writing for some time about how code is a lever on behavior, we’re still in the early days of developing methods for holding that code and its influence accountable. “There’s no conventional or obvious approach to it. It’s a lot of testing or trial and error, and it’s hard to teach in any uniform way,” noted Jeremy Singer-Vine, a reporter and programmer who worked on the WSJ price discrimination story. It will always be a messy business with lots of room for creativity, but given the growing power that algorithms wield in society it’s vital to continue to develop, codify, and teach more formalized methods of algorithmic accountability. In the absence of new legal measures, it may just provide a novel way to shed light on such systems, particularly in cases where transparency doesn’t or can’t offer much clarity.
Crossposted from /var/null, a blog written by Aditya Mukerjee. Aditya graduated from Columbia with a degree in CS and statistics, was a hackNY Fellow, worked in data at OkCupid, and on the server team at foursquare. He currently serves as the Hacker-in-Residence at Quotidian Ventures.
A couple of weeks ago, I was scheduled to take a trip from New York (JFK) to Los Angeles on JetBlue. Every year, my family goes on a one-week pilgrimage, where we put our work on hold and spend time visiting temples, praying, and spending time with family and friends. To my Jewish friends, I often explain this trip as vaguely similar to the Sabbath, except we take one week of rest per year, rather than one day per week.
Our family is not Muslim, but by coincidence, this year, our trip happened to be during the last week of Ramadan.
By further coincidence, this was also the same week that I was moving out of my employer-provided temporary housing (at NYU) and moving into my new apartment. The night before my trip, I enlisted the help of two friends and we took most of my belongings, in a couple of suitcases, to my new apartment. The apartment was almost completely unfurnished – I planned on getting new furniture upon my return – so I dropped my few bags (one containing an air mattress) in the corner. Even though I hadn’t decorated the apartment yet, in accordance with Hindu custom, I taped a single photograph to the wall in my bedroom — a long-haired saint with his hands outstretched in pronam (a sign of reverence and respect).
The next morning, I packed the rest of my clothes into a suitcase and took a cab to the airport. I didn’t bother to eat breakfast, figuring I would grab some yogurt in the terminal while waiting to board.
I got in line for security at the airport and handed the agent my ID. Another agent came over and handed me a paper slip, which he said was being used to track the length of the security lines. He said, “just hand this to someone when your stuff goes through the x-ray machines, and we’ll know how long you were in line.’ I looked at the timestamp on the paper: 10:40.
When going through the security line, I opted out (as I always used to) of the millimeter wave detectors. I fly often enough, and have opted out often enough, that I was prepared for what comes next: a firm pat-down by a TSA employee wearing non-latex gloves, who uses the back of his hand when patting down the inside of the thighs.
After the pat-down, the TSA agent swabbed his hands with some cotton-like material and put the swab in the machine that supposedly checks for explosive residue. The machine beeped. “We’re going to need to pat you down again, this time in private,” the agent said.
Having been selected before for so-called “random” checks, I assumed that this was another such check.
“What do you mean, ‘in private’? Can’t we just do this out here?”
“No, this is a different kind of pat-down, and we can’t do that in public.” When I asked him why this pat-down was different, he wouldn’t tell me. When I asked him specifically why he couldn’t do it in public, he said “Because it would be obscene.”
Naturally, I balked at the thought of going somewhere behind closed doors where a person I just met was going to touch me in “obscene” ways. I didn’t know at the time (and the agent never bothered to tell me) that the TSA has a policy that requires two agents to be present during every private pat-down. I’m not sure if that would make me feel more or less comfortable.
Noticing my hesitation, the agent offered to have his supervisor explain the procedure in more detail. He brought over his supervisor, a rather harried man who, instead of explaining the pat-down to me, rather rudely explained to me that I could either submit immediately to a pat-down behind closed-doors, or he could call the police.
At this point, I didn’t mind having to leave the secure area and go back through security again (this time not opting out of the machines), but I didn’t particularly want to get the cops involved. I told him, “Okay, fine, I’ll leave”.
“You can’t leave here.”
“Are you detaining me, then?” I’ve been through enough “know your rights” training to know how to handle police searches; however, TSA agents are not law enforcement officials. Technically, they don’t even have the right to detain you against your will.
“We’re not detaining you. You just can’t leave.” My jaw dropped.
“Either you’re detaining me, or I’m free to go. Which one is it?” I asked.
He glanced for a moment at my backpack, then snatched it out of the conveyor belt. “Okay,” he said. “You can leave, but I’m keeping your bag.”
I was speechless. My bag had both my work computer and my personal computer in it. The only way for me to get it back from him would be to snatch it back, at which point he could simply claim that I had assaulted him. I was trapped.
While we waited for the police to arrive, I took my phone and quickly tried to call my parents to let them know what was happening. Unfortunately, my mom’s voicemail was full, and my dad had never even set his up.
“Hey, what’s he doing?” One of the TSA agents had noticed I was touching my phone. “It’s probably fine; he’s leaving anyway,” another said.
The cops arrived a few minutes later, spoke with the TSA agents for a moment, and then came over and gave me one last chance to submit to the private examination. “Otherwise, we have to escort you out of the building.” I asked him if he could be present while the TSA agent was patting me down.
“No,” he explained, “because when we pat people down, it’s to lock them up.”
I only realized the significance of that explanation later. At this point, I didn’t particularly want to miss my flight. Foolishly, I said, “Fine, I’ll do it.”
The TSA agents and police escorted me to a holding room, where they patted me down again – this time using the front of their hands as they passed down the front of my pants. While they patted me down, they asked me some basic questions.
“What’s the purpose of your travel?”
“Personal,” I responded, (as opposed to business).
“Are you traveling with anybody?”
“My parents are on their way to LA right now; I’m meeting them there.”
“How long is your trip?”
“What will you be doing?”
Mentally, I sighed. There wasn’t any other way I could answer this next question.
“We’ll be visiting some temples.” He raised his eyebrow, and I explained that the next week was a religious holiday, and that I was traveling to LA to observe it with my family.
After patting me down, they swabbed not only their hands, but also my backpack, shoes, wallet, and belongings, and then walked out of the room to put it through the machine again. After more than five minutes, I started to wonder why they hadn’t said anything, so I asked the police officer who was guarding the door. He called over the TSA agent, who told me,
“You’re still setting off the alarm. We need to call the explosives specialist”.
I waited for about ten minutes before the specialist showed up. He walked in without a word, grabbed the bins with my possessions, and started to leave. Unlike the other agents I’d seen, he wasn’t wearing a uniform, so I was a bit taken aback.
“What’s happening?” I asked.
“I’m running it through the x-ray again,” he snapped. “Because I can. And I’m going to do it again, and again, until I decide I’m done”. He then asked the TSA agents whether they had patted me down. They said they had, and he just said, “Well, try again”, and left the room. Again I was told to stand with my legs apart and my hands extended horizontally while they patted me down all over before stepping outside.
The explosives specialist walked back into the room and asked me why my clothes were testing positive for explosives. I told him, quite truthfully, “I don’t know.” He asked me what I had done earlier in the day.
“Well, I had to pack my suitcase, and also clean my apartment.”
“I moved my stuff from my old apartment to my new one”.
“What did you eat this morning?”
“Nothing,” I said. Only later did I realize that this made it sound like I was fasting, when in reality, I just hadn’t had breakfast yet.
“Are you taking any medications?”
The other TSA agents stood and listened while the explosives specialist and asked every medication I had taken “recently”, both prescription and over-the-counter, and asked me to explain any medical conditions for which any prescription medicine had been prescribed. Even though I wasn’t carrying any medication on me, he still asked for my complete “recent” medical history.
“What have you touched that would cause you to test positive for certain explosives?”
“I can’t think of anything. What does it say is triggering the alarm?” I asked.
“I’m not going to tell you! It’s right here on my sheet, but I don’t have to tell you what it is!” he exclaimed, pointing at his clipboard.
I was at a loss for words. The first thing that came to my mind was, “Well, I haven’t touched any explosives, but if I don’t even know what chemical we’re talking about, I don’t know how to figure out why the tests are picking it up.”
He didn’t like this answer, so he told them to run my belongings through the x-ray machine and pat me down again, then left the room.
I glanced at my watch. Boarding would start in fifteen minutes, and I hadn’t even had anything to eat. A TSA officer in the room noticed me craning my neck to look at my watch on the table, and he said, “Don’t worry, they’ll hold the flight.”
As they patted me down for the fourth time, a female TSA agent asked me for my baggage claim ticket. I handed it to her, and she told me that a woman from JetBlue corporate security needed to ask me some questions as well. I was a bit surprised, but agreed. After the pat-down, the JetBlue representative walked in and cooly introduced herself by name.
She explained, “We have some questions for you to determine whether or not you’re permitted to fly today. Have you flown on JetBlue before?”
“Maybe about ten times,” I guessed.
“Ten what? Per month?”
“No, ten times total.”
She paused, then asked,
“Will you have any trouble following the instructions of the crew and flight attendants on board the flight?”
“No.” I had no idea why this would even be in doubt.
“We have some female flight attendants. Would you be able to follow their instructions?”
I was almost insulted by the question, but I answered calmly, “Yes, I can do that.”
“Okay,” she continued, “and will you need any special treatment during your flight? Do you need a special place to pray on board the aircraft?”
Only here did it hit me.
“No,” I said with a light-hearted chuckle, trying to conceal any sign of how offensive her questions were. “Thank you for asking, but I don’t need any special treatment.”
She left the room, again, leaving me alone for another ten minutes or so. When she finally returned, she told me that I had passed the TSA’s inspection. “However, based on the responses you’ve given to questions, we’re not going to permit you to fly today.”
I was shocked. “What do you mean?” were the only words I could get out.
“If you’d like, we’ll rebook you for the flight tomorrow, but you can’t take the flight this afternoon, and we’re not permitting you to rebook for any flight today.”
I barely noticed the irony of the situation – that the TSA and NYPD were clearing me for takeoff, but JetBlue had decided to ground me. At this point, I could think of nothing else but how to inform my family, who were expecting me to be on the other side of the country, that I wouldn’t be meeting them for dinner after all. In the meantime, an officer entered the room and told me to continue waiting there. “We just have one more person who needs to speak with you before you go.” By then, I had already been “cleared” by the TSA and NYPD, so I couldn’t figure out why I still needed to be questioned. I asked them if I could use my phone and call my family.
“No, this will just take a couple of minutes and you’ll be on your way.” The time was 12.35.
He stepped out of the room – for the first time since I had been brought into the cell, there was no NYPD officer guarding the door. Recognizing my short window of opportunity, I grabbed my phone from the table and quickly texted three of my local friends – two who live in Brooklyn, and one who lives in Nassau County – telling them that I had been detained by the TSA and that I couldn’t board my flight. I wasn’t sure what was going to happen next, but since nobody had any intention of reading me my Miranda rights, I wanted to make sure people knew where I was.
After fifteen minutes, one of the police officers marched into the room and scolded, “You didn’t tell us you have a checked bag!” I explained that I had already handed my baggage claim ticket to a TSA agent, so I had in fact informed someone that I had a checked bag. Looking frustrated, he turned and walked out of the room, without saying anything more.
After about twenty minutes, another man walked in and introduced himself as representing the FBI. He asked me many of the same questions I had already answered multiple times – my name, my address, what I had done so far that day. etc.
He then asked, “What is your religion?”
“How religious are you? Would you describe yourself as ‘somewhat religious’ or ‘very religious’?”
I was speechless from the idea of being forced to talk about my the extent of religious beliefs to a complete stranger. “Somewhat religious”, I responded.
“How many times a day do you pray?” he asked. This time, my surprise must have registered on my face, because he quickly added, “I’m not trying to offend you; I just don’t know anything about Hinduism. For example, I know that people are fasting for Ramadan right now, but I don’t have any idea what Hindus actually do on a daily basis.”
I nearly laughed at the idea of being questioned by a man who was able to admit his own ignorance on the subject matter, but I knew enough to restrain myself. The questioning continued for another few minutes. At one point, he asked me what cleaning supplies I had used that morning.
“Well, some window cleaner, disinfectant -” I started, before he cut me off.
“This is important,” he said, sternly. “Be specific.” I listed the specific brands that I had used.
Suddenly I remembered something: the very last thing I had done before leaving was to take the bed sheets off of my bed, as I was moving out. Since this was a dorm room, to guard against bedbugs, my dad (a physician) had given me an over-the-counter spray to spray on the mattress when I moved in, over two months previously. Was it possible that that was still active and triggering their machines?
“I also have a bedbug spray,” I said. “I don’t know the name of it, but I knew it was over-the-counter, so I figured it probably contained permethrin.” Permethrin is an insecticide, sold over-the-counter to kill bed bugs and lice.
“Perm-what?” He asked me to spell it.
After he wrote it down, I asked him if I could have something to drink. “I’ve been here talking for three hours at this point,” I explained. “My mouth is like sandpaper”. He refused, saying
“We’ll just be a few minutes, and then you’ll be able to go.”
“Do you have any identification?” I showed him my drivers license, which still listed my old address. “You have nothing that shows your new address?” he exclaimed.
“Well, no, I only moved there on Thursday.”
“What about the address before that?”
“I was only there for two months – it was temporary housing for work”. I pulled my NYU ID out of my wallet. He looked at it, then a police officer in the room took it from him and walked out.
“What about any business cards that show your work address?” I mentally replayed my steps from the morning, and remembered that I had left behind my business card holder, thinking I wouldn’t need it on my trip.
“No, I left those at home.”
“You have none?”
“Well, no, I’m going on vacation, so I didn’t refill them last night.” He scoffed. “I always carry my cards on me, even when I’m on vacation.” I had no response to that – what could I say?
“What about a direct line at work? Is there a phone number I can call where it’ll patch me straight through to your voicemail?”
“No,” I tried in vain to explain. “We’re a tech company; everyone just uses their cell phones”. To this day, I don’t think my company has a working landline phone in the entire office – our “main line” is a virtual assistant that just forwards calls to our cell phones. I offered to give him the name and phone number of one of our venture partners instead, which he reluctantly accepted.
Around this point, the officer who had taken my NYU ID stormed into the room.
“They put an expiration sticker on your ID, right?” I nodded. “Well then why did this ID expire in 2010?!” he accused.
I took a look at the ID and calmly pointed out that it said “August 2013” in big letters on the ID, and that the numbers “8/10” meant “August 10th, 2013”, not “August, 2010”. I added, “See, even the expiration sticker says 2013 on it above the date”. He studied the ID again for a moment, then walked out of the room again, looking a little embarrassed.
The FBI agent resumed speaking with me. “Do you have any credit cards with your name on them?” I was hesitant to hand them a credit card, but I didn’t have much of a choice. Reluctantly, I pulled out a credit card and handed it to him. “What’s the limit on it?” he said, and then, noticing that I didn’t laugh, quickly added, “That was a joke.”
He left the room, and then a series of other NYPD and TSA agents came in and started questioning me, one after the other, with the same questions that I’d already answered previously. In between, I was left alone, except for the officer guarding the door.
At one point, when I went to the door and asked the officer when I could finally get something to drink, he told me, “Just a couple more minutes. You’ll be out of here soon.”
“That’s what they said an hour ago,” I complained.
“You also said a lot of things, kid,” he said with a wink. “Now sit back down”.
I sat back down and waited some more. Another time, I looked up and noticed that a different officer was guarding the door. By this time, I hadn’t had any food or water in almost eighteen hours. I could feel the energy draining from me, both physically and mentally, and my head was starting to spin. I went to the door and explained the situation the officer. “At the very least, I really need something to drink.”
“Is this a medical emergency? Are you going to pass out? Do we need to call an ambulance?” he asked, skeptically. His tone was almost mocking, conveying more scorn than actual concern or interest.
“No,” I responded. I’m not sure why I said that. I was lightheaded enough that I certainly felt like I was going to pass out.
“Are you diabetic?”
“No,” I responded.
Again he repeated the familiar refrain. “We’ll get you out of here in a few minutes.” I sat back down. I was starting to feel cold, even though I was sweating – the same way I often feel when a fever is coming on. But when I put my hand to my forehead, I felt fine.
One of the police officers who questioned me about my job was less-than-familiar with the technology field.
“What type of work do you do?”
“I work in venture capital.”
“Venture Capital – is that the thing I see ads for on TV all the time?” For a moment, I was dumbfounded – what venture capital firm advertises on TV? Suddenly, it hit me.
“Oh! You’re probably thinking of Capital One Venture credit cards.” I said this politely and with a straight face, but unfortunately, the other cop standing in the room burst out laughing immediately. Silently, I was shocked – somehow, this was the interrogation procedure for confirming that I actually had the job I claimed to have.
Another pair of NYPD officers walked in, and one asked me to identify some landmarks around my new apartment. One was, “When you’re facing the apartment, is the parking on the left or on the right?” I thought this was an odd question, but I answered it correctly. He whispered something in the ear of the other officer, and they both walked out.
The onslaught of NYPD agents was broken when a South Asian man with a Homeland Security badge walked in and said something that sounded unintelligible. After a second, I realized he was speaking Hindi.
“Sorry, I don’t speak Hindi.”
“Oh!” he said, noticeably surprised at how “Americanized” this suspect was. We chatted for a few moments, during which time I learned that his family was Pakistani, and that he was Muslim, though he was not fasting for Ramadan. He asked me the standard repertoire of questions that I had been answering for other agents all day.
Finally, the FBI agent returned.
“How are you feeling right now?” he asked. I wasn’t sure if he was expressing genuine concern or interrogating me further, but by this point, I had very little energy left.
“A bit nauseous, and very thirsty.”
“You’ll have to understand, when a person of your… background walks into here, travelling alone, and sets off our alarms, people start to get a bit nervous. I’m sure you’ve been following what’s been going on in the news recently. You’ve got people from five different branches of government all in here – we don’t do this just for fun.”
He asked me to repeat some answers to questions that he’d asked me previously, looking down at his notes the whole time, then he left. Finally, two TSA agents entered the room and told me that my checked bag was outside, and that I would be escorted out to the ticketing desks, where I could see if JetBlue would refund my flight.
It was 2:20PM by the time I was finally released from custody. My entire body was shaking uncontrollably, as if I were extremely cold, even though I wasn’t. I couldn’t identify the emotion I was feeling. Surprisingly, as far as I could tell, I was shaking out of neither fear nor anger – I felt neither of those emotions at the time. The shaking motion was entirely involuntary, and I couldn’t force my limbs to be still, no matter how hard I concentrated.
In the end, JetBlue did refund my flight, but they cancelled my entire round-trip ticket. Because I had to rebook on another airline that same day, it ended up costing me about $700 more for the entire trip. Ironically, when I went to the other terminal, I was able to get through security (by walking through the millimeter wave machines) with no problem.
I spent the week in LA, where I was able to tell my family and friends about the entire ordeal. They were appalled by the treatment I had received, but happy to see me safely with them, even if several hours later.
I wish I could say that the story ended there. It almost did. I had no trouble flying back to NYC on a red-eye the next week, in the wee hours of August 12th. But when I returned home the next week, opened the door to my new apartment, and looked around the room, I couldn’t help but notice that one of the suitcases sat several inches away from the wall. I could have sworn I pushed everything to the side of the room when I left, but I told myself that I may have just forgotten, since I was in a hurry when I dropped my bags off.
When I entered my bedroom, a chill went down my spine: the photograph on my wall had vanished. I looked around the room, but in vain. My apartment was almost completely empty; there was no wardrobe it could have slipped under, even on the off-chance it had fallen.
To this day, that photograph has not turned up. I can’t think of any “rational” explanation for it. Maybe there is one. Maybe a burglar broke into my apartment by picking the front door lock and, finding nothing of monetary value, took only my picture. In order to preserve my peace-of-mind, I’ve tried to convince myself that that’s what happened, so I can sleep comfortably at night.
But no matter how I’ve tried to rationalize this in the last week and a half, nothing can block out the memory of the chilling sensation I felt that first morning, lying on my air mattress, trying to forget the image of large, uniformed men invading the sanctuary of my home in my absence, wondering when they had done it, wondering why they had done it.
In all my life, I have only felt that same chilling terror once before – on one cold night in September twelve years ago, when I huddled in bed and tried to forget the terrible events in the news that day, wondering why they they had happened, wondering whether everything would be okay ever again.
This is a guest post from Jordan Ellenberg, a professor of mathematics at the University of Wisconsin. Jordan’s book, How Not To Be Wrong, comes out in May 2014. It is crossposted from his blog, Quomodocumque, and tweeted about at @JSEllenberg.
Cathy posted some cool data yesterday coming from the new visualization features of the magnificent Stacks Project. Summary: you can make a directed graph whose vertices are the 10,445 tagged assertions in the Stacks Project, and whose edges are logical dependency. So this graph (hopefully!) doesn’t have any directed cycles. (Actually, Cathy tells me that the Stacks Project autovomits out any contribution that would create a logical cycle! I wish LaTeX could do that.)
Given any assertion v, you can construct the subgraph G_v of vertices which are the terminus of a directed path starting at v. And Cathy finds that if you plot the number of vertices and number of edges of each of these graphs, you get something that looks really, really close to a line.
Why is this so? Does it suggest some underlying structure? I tend to say no, or at least not much — my guess is that in some sense it is “expected” for graphs like this to have this sort of property.
Because I am trying to get strong at sage I coded some of this up this morning. One way to make a random directed graph with no cycles is as follows: start with N edges, and a function f on natural numbers k that decays with k, and then connect vertex N to vertex N-k (if there is such a vertex) with probability f(k). The decaying function f is supposed to mimic the fact that an assertion is presumably more likely to refer to something just before it than something “far away” (though of course the stack project is not a strictly linear thing like a book.)
Here’s how Cathy’s plot looks for a graph generated by N= 1000 and f(k) = (2/3)^k, which makes the mean out-degree 2 as suggested in Cathy’s post.
Pretty linear — though if you look closely you can see that there are really (at least) a couple of close-to-linear “strands” superimposed! At first I thought this was because I forgot to clear the plot before running the program, but no, this is the kind of thing that happens.
Is this because the distribution decays so fast, so that there are very few long-range edges? Here’s how the plot looks with f(k) = 1/k^2, a nice fat tail yielding many more long edges:
My guess: a random graph aficionado could prove that the plot stays very close to a line with high probability under a broad range of random graph models. But I don’t really know!
Update: Although you know what must be happening here? It’s not hard to check that in the models I’ve presented here, there’s a huge amount of overlap between the descendant graphs; in fact, a vertex is very likely to be connected all but c of the vertices below it for a suitable constant c.
I would guess the Stacks Project graph doesn’t have this property (though it would be interesting to hear from Cathy to what extent this is the case) and that in her scatterplot we are not measuring the same graph again and again.
It might be fun to consider a model where vertices are pairs of natural numbers and (m,n) is connected to (m-k,n-l) with probability f(k,l) for some suitable decay. Under those circumstances, you’d have substantially less overlap between the descendant trees; do you still get the approximately linear relationship between edges and nodes?
This is a guest post by my friend Laura Strausfeld.
As an unlicensed psychotherapist, here’s my take on why Huma Abedin is supporting her husband Anthony Weiner’s campaign for mayor:
It’s all about the kid.
Jordan Weiner is 19 months old. When he’s 8 or 9—or 5, and wearing google glasses—maybe he’ll google his name and read about his father’s penis. Either that, or one of his buddies at school may ask him about his father’s penis. Jordan might then ask his mommy and daddy about his father’s penis and they’ll tell him either 1) your daddy was a great politician, but had to resign from Congress because he admitted to showing people his penis, which we recommend you don’t do, especially when you’re a grownup and on twitter; or 2) your daddy was a great politician and ran a very close race for mayor—that’s right, your daddy was almost mayor of New York City!—but he lost because people said he showed people his penis and that’s none of anybody’s business.
Let’s look at this from Huma’s perspective. She’s got a child for a husband, with a weird sexual addiction that on the positive side, doesn’t appear to carry the threat of STDs. But her dilemma is not about her marriage. The marriage is over. What she cares about is Jordan. And this is where she’s really fucked. Whatever happens, Anthony will always be her child’s father.
That bears repeating. You’ve got a child you love more than anything in the world, will sacrifice anything for, and will always now be stigmatized as the son of a celebrity-sized asshole. What are your choices?
The best scenario for Huma is if Anthony becomes mayor. Then she can divorce his ass, get primary custody and protect her child from growing up listening to penis jokes about his loser father. There will be jokes, but at least they’ll be about the mayor’s penis. And with a whole lot of luck, they might even be about how his father’s penis was a lot smaller in the mind of the public than his policies.
Weiner won’t get my vote, however. And for that, I apologize to you, Jordan. You have my sympathy, Huma.
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.’
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 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:
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.
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.
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.
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.
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.
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.
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?
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.
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.
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.
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?
This is a guest post by Eugene Stern.
Sometimes you learn just as much from a bad analogy as from a good one. At least you learn what people are thinking.
The other day I read this response to this NYT article. The original article asked whether the Common Core-based school reforms now being put in place in most states are really a good idea. The blog post criticized the article for failing to break out four separate elements of the reforms: standards (the Core), curriculum (what’s actually taught), assessment (testing), and accountability (evaluating how kids and educators did). If you have an issue with the reforms, you’re supposed to say exactly which aspect you have an issue with.
But then, at the end of the blog post, we get this:
A track and field metaphor might help: The standard is the bar that students must jump over to be competitive. The curriculum is the training program coaches use to help students get over the bar. The assessment is the track meet where we find out how high everyone can jump. And the accountability system is what follows after its all over and we want to figure out what went right, what went wrong, and what it will take to help kids jump higher.
In track, jumping over the bar is the entire point. You’re successful if you clear the bar, you’ve failed if you don’t. There are no other goals in play. So the standard, the curriculum, and the assessment might be nominally different, but they’re completely interdependent. The standard is defined in terms of the assessment, and the only curriculum that makes sense is training for the assessment.
Education has a lot more to it. The Common Core is a standard covering two academic dimensions: math and English/language arts/literacy. But we also want our kids learning science, and history, and music, and foreign languages, and technology, as well as developing along non-academic dimensions: physically, socially, morally, etc. (If a school graduated a bunch of high academic achievers that couldn’t function in society, or all ended up in jail for insider trading, we probably wouldn’t call that school successful.)
In Cathy’s terminology from this blog post, the Common Core is a proxy for the sum total of what we care about, or even just for the academic component of what we care about.
Then there’s a second level of proxying when we go from the standard to the assessment. The Common Core requirements are written to require general understanding (for example: kindergarteners should understand the relationship between numbers and quantities and connect counting to cardinality). A test that tries to measure that understanding can only proxy it imperfectly, in terms of a few specific questions.
Think that’s obvious? Great! But hang on just a minute.
The real trouble with the sports analogy comes when we get to the accountability step and forget all the proxying we did. “After it’s all over and we want to figure out what went right (and) what went wrong,” we measure right and wrong in terms of the assessment (the test). In sports, where the whole point is to do well on the assessment, it may make sense to change coaches if the team isn’t winning. But when we deny tenure to or fire teachers whose students didn’t do well enough on standardized tests (already in place in New York, now proposed for New Jersey as well), we’re treating the test as the whole point, rather than a proxy of a proxy. That incentivizes schools to narrow the curriculum to what’s included in the standard, and to teach to the test.
We may think it’s obvious that sports and education are different, but the decisions we’re making as a society don’t actually distinguish them.
This is a guest post by Rachel Law, a conceptual artist, designer and programmer living in Brooklyn, New York. She recently graduated from Parsons MFA Design&Technology. Her practice is centered around social myths and how technology facilitates the creation of new communities. Currently she is writing a book with McKenzie Wark called W.A.N.T, about new ways of analyzing networks and debunking ‘mapping’.
Let’s start with a timely question. How would you like to be able to change how you are identified by online networks? We’ll talk more about how you’re currently identified below, but for now just imagine having control over that process for once – how would that feel? Vortex is something I’ve invented that will try to make that happen.
Namely, Vortex is a data management game that allows players to swap cookies, change IPs and disguise their locations. Through play, individuals experience how their browser changes in real time when different cookies are equipped. Vortex is a proof of concept that illustrates how network collisions in gameplay expose contours of a network determined by consumer behavior.
What happens when users are allowed to swap cookies?
These cookies, placed by marketers to track behavioral patterns, are stored on our personal devices from mobile phones to laptops to tablets, as a symbolic and data-driven signifier of who we are. In other words, to the eyes of the database, the cookies are us. They are our identities, controlling the way we use, browse and experience the web. Depending on cookie type, they might follow us across multiple websites, save entire histories about how we navigate and look at things and pass this information to companies while still living inside our devices.
If we have the ability to swap cookies, the debate on privacy shifts from relying on corporations to follow regulations to empowering users by giving them the opportunity to manage how they want to be perceived by the network.
What are cookies?
The corporate technological ability to track customers and piece together entire personal histories is a recent development. While there are several ways of doing so, the most common and prevalent method is with HTTP cookies. Invented in 1994 by a computer programmer, Lou Montulli, HTTP cookies were originally created with the shopping cart system as a way for the computer to store the current state of the session, i.e. how many items existed in the cart without overloading the company’s server. These session histories were saved inside each user’s computer or individual device, where companies accessed and updated consumer history constantly as a form of ‘internet history’. Information such as where you clicked, how to you clicked, what you clicked first, your general purchasing history and preferences were all saved in your browsing history and accessed by companies through cookies.
Cookies were originally implemented to the general public without their knowledge until the Financial Times published an article about how they were made and utilized on websites without user knowledge on February 12th, 1996 . This revelation led to a public outcry over privacy issues, especially since data was being gathered without the knowledge or consent of users. In addition, corporations had access to information stored on personal computers as the cookie sessions were stored on your computer and not their servers.
At the center of the debate was the issue on third-party cookies, also known as “persistent” or “tracking” cookies. When you are browsing a webpage, there may be components on the page that are hosted on the same server, but different domain. These external objects then pass cookies to you if you click an image, link or article. They are then used by advertising and media mining corporations to track users across multiple sites to garner more knowledge about the users browsing patterns to create more specific and targeted advertising.
In August 2013, Wall Street Journal ran an article on how Mac users were being unfairly targeted by travel site Orbitz with advertisements that were 13% more expensive than PC users. New York Times followed it up with a similar article in November 2012 about how the data collected and re-sold to advertisers. These advertisers would analyze users buying habits to create micro-categories where the personal experiences were tailored to maximize potential profits.
What does that mean for us?
The current state of today’s internet is no longer the same as the carefree 90s of ‘internet democracy’ and utopian ‘cyberspace’. Mediamining exploits invasive technologies such as IP tracking, geolocating and cookies to create specific advertisements targeted to individuals. Browsing is now determined by your consumer profile what you see, hear and the feeds you receive are tailored from your friends’ lists, emails, online purchases etc. The ‘Internet’ does not exist. Instead, it is many overlapping filter bubbles which selectively curate us into data objects to be consumed and purchased by advertisers.
This information, though anonymous, is built up over time and used to track and trace an individual’s history – sometimes spanning an entire lifetime. Who you are, and your real name is irrelevant in the overall scale of collected data, depersonalizing and dehumanizing you into nothing but a list of numbers on a spreadsheet.
The superstore Target, provides a useful case study for data profiling in its use of statisticians on their marketing teams. In 2002, Target realized that when a couple is expecting a child, the way they shop and purchase products changes. But they needed a tool to be able to see and take advantage of the pattern. As such, they asked mathematicians to come up with algorithms to identify behavioral patterns that would indicate a newly expectant mother and push direct marketing materials their way. In a public relations fiasco, Target had sent maternity and infant care advertisements to a household, inadvertedly revealing that their teenage daughter was pregnant before she told her parents .
This build-up of information creates a ‘database of ruin’, enough information that marketers and advertisers know more about your life and predictive patterns than any single entity. Databases that can predict whether you’re expecting, or when you’ve moved, or what stage of your life or income level you’re at… information that you have no control over where it goes to, who is reading it or how it is being used. More importantly, these databases have collected enough information that they know secrets such as family history of illness, criminal or drug records or other private information that could potentially cause harm upon the individual data point if released – without ever needing to know his or her name.
What happens now is two terrifying possibilities:
- Corporate databases with information about you, your family and friends that you have zero control over, including sensitive information such as health, criminal/drug records etc. that are bought and re-sold to other companies for profit maximization.
- New forms of discrimination where your buying/consumer habits determine which level of internet you can access, or what kind of internet you can experience. This discrimination is so insidious because it happens on a user account level which you cannot see unless you have access to other people’s accounts.
Here’s a visual describing this process:
What can Vortex do, and where can I download a copy?
As Vortex lives on the browser, it can manage both pseudo-identities (invented) as well as ‘real’ identities shared with you by other users. These identity profiles are created through mining websites for cookies, swapping them with friends as well as arranging and re-arranging them to create new experiences. By swapping identities, you are essentially ‘disguised’ as someone else – the network or website will not be able to recognize you. The idea is that being completely anonymous is difficult, but being someone else and hiding with misinformation is easy.
This does not mean a death knell for online shopping or e-commerce industries. For instance, if a user decides to go shoe-shopping for summer, he/she could equip their browser with the cookies most associated and aligned with shopping, shoes and summer. Targeted advertising becomes a targeted choice for both advertisers and users. Advertisers will not have to worry about misinterpreting or mis-targeting inappropriate advertisements i.e. showing tampon advertisements to a boyfriend who happened to borrow his girlfriend’s laptop; and at the same time users can choose what kind of advertisements they want to see. (i.e. Summer is coming, maybe it’s time to load up all those cookies linked to shoes and summer and beaches and see what websites have to offer; or disable cookies it completely if you hate summer apparel.)
Currently the game is a working prototype/demo. The code is licensed under creative commons and will be available on GitHub by the end of summer. I am trying to get funding to make it free, safe & easy to use; but right now I’m broke from grad school and a proper back-end to be built for creating accounts that is safe and cannot be intercepted. If you have any questions on technical specs or interest in collaborating to make it happen – particularly looking for people versed in python/mongodb, please email me: Rachel@milkred.net.
This guest post is by Sue VanHattum, who blogs at Math Mama Writes. She teaches math at Contra Costa College, a community college in the Bay Area, and is working on a book titled Playing With Math: Stories from Math Circles, Homeschoolers, and Passionate Teachers, which will be published soon.
Here’s the Pythagorean Theorem:
In a right triangle, where the lengths of the legs are given by and , and the length of the hypotenuse is given by , we have
Do you remember when you first learned about it? Do you remember when you first proved it?
I have no idea when or where I first saw it. It feels like something I’ve always ‘known’. I put known in quotes because in math we prove things, and I used the Pythagoeran Theorem for way too many years, as a student and as a math teacher, before I ever thought about proving it. (It’s certainly possible I worked through a proof in my high school geometry class, but my memory kind of sucks and I have no memory of it.)
It’s used in beginning algebra classes as part of terrible ‘pseudo-problems’ like this:
Two cars start from the same intersection with one traveling southbound while the other travels eastbound going 10 mph faster. If after two hours they are 10 times the square root of 24 [miles] apart, how fast was each car traveling?
After years of working through these problems with students, I finally realized I’d never shown them a proof (this seems terribly wrong to me now). I tried to prove it, and didn’t really have any idea how to get started.
This was 10 to 15 years ago, before Google became a verb, so I searched for it in a book. I eventually found it in a high school geometry textbook. Luckily it showed a visually simple proof that stuck with me. There are hundreds of proofs, many of them hard to follow.
There is something wrong with an education system that teaches us ‘facts’ like this one and knocks the desire for deep understanding out of us. Pam Sorooshian, an unschooling advocate, said in a talk to other unschooling parents:
Relax and let them develop conceptual understanding slowly, over time. Don’t encourage them to memorize anything – the problem is that once people memorize a technique or a ‘fact’, they have the feeling that they ‘know it’ and they stop questioning it or wondering about it. Learning is stunted.
She sure got my number! I thought I knew it for all those years, and it took me decades to realize that I didn’t really know it. This is especially ironic – the reason it bears Pythagoras’ name is because the Pythagoreans were the first to prove it (that we know of).
It had been used long before Pythagoras and the Greeks – most famously by the Egyptians. Egyptian ‘rope-pullers’ surveyed the land and helped build the pyramids, using a taut circle of rope with 12 equally-spaced knots to create a 3-4-5 triangle: since this is a right triangle, giving them the right angle that’s so important for building and surveying.
Ever since the Greeks, proof has been the basis of all mathematics. To do math without understanding why something is true really makes no sense.
Nowadays I feel that one of my main jobs as a math teacher is to get students to wonder and to question. But my own math education left me with lots of ‘knowledge’ that has nothing to do with true understanding. (I wonder what else I have yet to question…) And beginning algebra students are still using textbooks that ‘give’ the Pythagorean Theorem with no justification. No wonder my Calc II students last year didn’t know the difference between an example and a proof.
Just this morning I came across an even simpler proof of the Pythagorean Theorem than the one I have liked best over the past 10 to 15 years. I was amazed that I hadn’t seen it before. Well, perhaps I had seen it but never took it in before, not being ready to appreciate it. I’ll talk about it below.
My old favorite goes like this:
- Draw a square.
- Put a dot on one side (not at the middle).
- Put dots at the same place on each of the other 3 sides.
- Connect them.
- You now have a tilted square inside the bigger square, along with 4 triangles. At this point, you can proceed algebraically or visually.
- big square = small tilted square + 4 triangles
- Move the triangles around.
- What was is now
- Also check out Vi Hart’s video showing a paper-folding proof (with a bit of ripping). It’s pretty similar to this one.
This is an even more visual proof, although it might take a few geometric remarks to make it clear. In any right triangle, the two acute (less than 90 degrees) angles add up to 90 degrees. Is that enough to see that the original triangle, triangle A, and triangle B are all similar? (Similar means they have exactly the same shape, though they may be different sizes.) Which makes the ‘houses with asymmetrical roofs’ also all similar. Since the big ‘house’ has an ‘attic’ equal in size to the two other ‘attics’, its ‘room’ must also be equal in area to the two other ‘rooms’. Wow! (I got this language from Alexander Bogomolny’s blog post about it, which also tells a story about young Einstein discovering this proof.
Since all three houses are similar (exact same shape, different sizes), the size of the room is some given multiple of the size of the attic. More properly, area(square) = area(triangle), where is the same for all three figures. The square attached to triangle (whose area we will say is also ) has area , similarly for the square attached to triangle . But note that which is the area of the square attached to the triangle labeled . But , and , so and it also equals giving us what we sought:
I stumbled on the article in which this appeared (The Step to Rationality, by R. N. Shepard) while trying to find an answer to a question I have about centroids. I haven’t answered my centroid question yet, but I sure was sending out some google love when I found this.
What I love about this proof is that the triangle stay central in our thoughts throughout, and the focus stays on area, which is what this is really about. It’s all about self-similarity, and that’s what makes it so beautiful.
I think that, even though this proof is simpler in terms of steps than my old favorite, it’s a bit harder to see conceptually. So I may stick with the first one when explaining to students. What do you think?
This is a guest post by Eugene Stern.
A big reason I love this blog is Cathy’s war on crappy models. She has posted multiple times already about the lousy performance of models that rate teachers based on year-to-year changes in student test scores (for example, read about it here). Much of the discussion focuses on the model used in New York City, but such systems have been, or are being, put in place all over the country. I want to let you know about the version now being considered for use across the river, in New Jersey. Once you’ve heard more, I hope you’ll help me try to stop it.
A little background if you haven’t heard about this before. Because it makes no sense to rate teachers based on students’ absolute grades or test scores (not all students start at the same place each year), the models all compare students’ test scores against some baseline. The simplest thing to do is to compare each student’s score on a test given at the end of the school year against their score on a test given at the end of the previous year. Teachers are then rated based on how much their students’ scores improved over the year.
Comparing with the previous year’s score controls for the level at which students start each year, but not for other factors beside the teacher that affect how much they learn. This includes attendance, in-school environment (curriculum, facilities, other students in the class), out-of-school learning (tutoring, enrichment programs, quantity and quality of time spent with parents/caregivers), and potentially much more. Fancier models try to take these into account by comparing each student’s end of year score with a predicted score. The predicted score is based both on the student’s previous score and on factors like those above. Improvement beyond the predicted score is then attributed to the teacher as “value added” (hence the name “value-added models,” or VAM) and turned into a teacher rating in some way, often using percentiles. One such model is used to rate teachers in New York City.
It’s important to understand that there is no single value-added model, rather a family of them, and that the devil is in the details. Two different teacher rating systems, based on two models of the predicted score, may perform very differently – both across the board, and in specific locations. Different factors may be more or less important depending on where you are. For example, income differences may matter more in a district that provides few basic services, so parents have to pay to get extracurriculars for their kids. And of course the test itself matters hugely as well.
Testing the VAM models
Teacher rating models based on standardized tests have been around for 25 years or so, but two things have happened in the last decade:
- Some people started to use the models in formal teacher evaluation, including tenure decisions.
- Some (other) people started to test the models.
This did not happen in the order that one would normally like. Wanting to make “data-driven decisions,” many cities and states decided to start rating teachers based on “data” before collecting any data to validate whether that “data” was any good. This is a bit like building a theoretical model of how cancer cells behave, synthesizing a cancer drug in the lab based on the model, distributing that drug widely without any trials, then waiting around to see how many people die from the side effects.
The full body count isn’t in yet, but the models don’t appear to be doing well so far. To look at some analysis of VAM data in New York City, start here and here. Note: this analysis was not done by the city but by individuals who downloaded the data after the city had to make it available because of disclosure laws.
I’m not aware of any study on the validity of NYC’s VAM ratings done by anyone actually affiliated with the city – if you know of any, please tell me. Again, the people preaching data don’t seem willing to actually use data to evaluate the quality of the systems they’re putting in place.
Assuming you have more respect for data than the mucky-mucks, let’s talk about how well the models actually do. Broadly, two ways a model can fail are being biased and being noisy. The point of the fancier value-added models is to try to eliminate bias by factoring in everything other than the teacher that might affect a student’s test score. The trouble is that any serious attempt to do this introduces a bunch of noise into the model, to the degree that the ratings coming out look almost random.
You’d think that a teacher doesn’t go from awful to great or vice versa in one year, but the NYC VAM ratings show next to no correlation in a teacher’s rating from one year to the next. You’d think that a teacher either teaches math well or doesn’t, but the NYC VAM ratings show next to no correlation in a teacher’s rating teaching a subject to one grade and their rating teaching it to another – in the very same year! (Gary Rubinstein’s blog, linked above, documents these examples, and a number of others.) Again, this is one particular implementation of a general class of models, but using such noisy data to make significant decisions about teachers’ careers seems nuts.
What’s happening in New Jersey
With all this as background, let’s turn to what’s happening in New Jersey.
You may be surprised that the version of the model proposed by Chris Christie‘s administration (the education commissioner is Christie appointee Chris Cerf, who helped put VAM in place in NYC) is about the simplest possible. There is no attempt to factor out bias by trying to model predicted scores, just a straight comparison between this year’s standardized test score and last year’s. For an overview, see this.
In more detail, the model groups together all students with the same score on last year’s test, and represents each student’s progress by their score on this year’s test, viewed as a percentile across this group. That’s it. A fancier version uses percentiles calculated across all students with the same score in each of the last several years. These can’t be calculated explicitly (you may not find enough students that got exactly the same score each the last few years), so they are estimated, using a statistical technique called quantile regression.
By design, both the simple and the fancy version ignore everything about a student except their test scores. As a modeler, or just as a human being, you might find it silly not to distinguish between a fourth grader in a wealthy suburb who scored 600 on a standardized test from a fourth grader in the projects with the same score. At least, I don’t know where to find a modeler who doesn’t find it silly, because nobody has bothered to study the validity of using this model to rate teachers. If I’m wrong, please point me to a study.
Politics and SGP
But here we get into the shell game of politics, where rating teachers based on the model is exactly the proposal that lies at the end of an impressive trail of doubletalk. Follow the bouncing ball.
These models, we are told, differ fundamentally from VAM (which is now seen as somewhat damaged goods politically, I suspect). While VAM tried to isolate teacher contribution, these models do no such thing – they are simply measuring student progress from year to year, which, after all, is what we truly care about. The models have even been rebranded with a new name: student growth percentiles, or SGP. SGP is sold as just describing student progress rather than attributing it to teachers, there can’t be any harm in that, right? – and nothing that needs validation, either. And because SGP is such a clean methodology – if you’re looking for a data-driven model to use for broad “educational assessment,” don’t get yourself into that whole VAM morass, use SGP instead!
Only before you know it, educational assessment turns into, you guessed it, rating teachers. That’s right: because these models aren’t built to rate teachers, they can focus on the things that really matter (student progress), and thus end up being – wait for it – much better for rating teachers! War is peace, friends. Ignorance is strength.
Creators of SGP
You can find a good discussion of SGP’s and their use in evaluation here, and a lot more from the same author, the impressively prolific Bruce Baker, here. Here’s a response from the creators of SGP. They maintain that information about student growth is useful (duh), and agree that differences in SGP’s should not be attributed to teachers (emphasis mine):
Large-scale assessment results are an important piece of evidence but are not sufficient to make causal claims about school or teacher quality.
SGP and teacher evaluations
But guess what?
The New Jersey Board of Ed and state education commissioner Cerf are putting in place a new teacher evaluation code, to be used this coming academic year and beyond. You can find more details here and here.
Summarizing: for math and English teachers in grades 4-8, 30% of their annual evaluation next year would be mandated by the state to come from those very same SGP’s that, according to their creators, are not sufficient to make causal claims about teacher quality. These evaluations are the primary input in tenure decisions, and can also be used to take away tenure from teachers who receive low ratings.
The proposal is not final, but is fairly far along in the regulatory approval process, and would become final in the next several months. In a recent step in the approval process, the weight given to SGP’s in the overall evaluation was reduced by 5%, from 35%. However, the 30% weight applies next year only, and in the future the state could increase the weight to as high as 50%, at its discretion.
Modeler’s Note #1: the precise weight doesn’t really matter. If the SGP scores vary a lot, and the other components don’t vary very much, SGP scores will drive the evaluation no matter what their weight.
Modeler’s Note #2: just reminding you again that this data-driven framework for teacher evaluation is being put in place without any data-driven evaluation of its effectiveness. And that this is a feature, not a bug – SGP has not been tested as an attribution tool because we keep hearing that it’s not meant to be one.
In a slightly ironic twist, commissioner Cerf has responded to criticisms that SGP hasn’t been tested by pointing to a Gates Foundation study of the effectiveness of… value-added models. The study is here. It draws pretty positive conclusions about how well VAM’s work. A number of critics have argued, pretty effectively, that the conclusions are unsupported by the data underlying the study, and that the data actually shows that VAM’s work badly. For a sample, see this. For another example of a VAM-positive study that doesn’t seem to stand up to scrutiny, see this and this.
Modeler’s Role Play #1
Say you were the modeler who had popularized SGP’s. You’ve said that the framework isn’t meant to make causal claims, then you see New Jersey (and other states too, I believe) putting a teaching evaluation model in place that uses SGP to make causal claims, without testing it first in any way. What would you do?
So far, the SGP mavens who told us that “Large-scale assessment results are an important piece of evidence but are not sufficient to make causal claims about school or teacher quality” remain silent about the New Jersey initiative, as far as I know.
Modeler’s Role Play #2
Now you’re you again, and you’ve never heard about SGP’s and New Jersey’s new teacher evaluation code until today. What do you do?
I want you to help me stop this thing. It’s not in place yet, and I hope there’s still time.
I don’t think we can convince the state education department on the merits. They’ve made the call that the new evaluation system is better than the current one or any alternatives they can think of, they’re invested in that decision, and we won’t change their minds directly. But we can make it easier for them to say no than to say yes. They can be influenced – by local school administrators, state politicians, the national education community, activists, you tell me who else. And many of those people will have more open minds. If I tell you, and you tell the right people, and they tell the right people, the chain gets to the decision makers eventually.
I don’t think I could convince Chris Christie, but maybe I could convince Bruce Springsteen if I met him, and maybe Bruce Springsteen could convince Chris Christie.
I thought we could start with a manifesto – a direct statement from the modeling community explaining why this sucks. Directed at people who can influence the politics, and signed by enough experts (let’s get some big names in there) to carry some weight with those influencers.
Can you help? Help write it, sign it, help get other people to sign it, help get it to the right audience. Know someone whose opinion matters in New Jersey? Then let me know, and help spread the word to them. Use Facebook and Twitter if it’ll help. And don’t forget good old email, phone calls, and lunches with friends.
Or, do you have a better idea? Then put it down. Here. The comments section is wide open. Let’s not fall back on criticizing the politicians for being dumb after the fact. Let’s do everything we can to keep them from doing this dumb thing in the first place.
Shame on us if we can’t make this right.
This is a guest post by Marc Joffe, the principal consultant at Public Sector Credit Solutions, an organization that provides data and analysis related to sovereign and municipal securities. Previously, Joffe was a Senior Director at Moody’s Analytics for more than a decade.
Note to readers: for a bit of background on the SEC Credit Ratings Roundtable and the Franken Amendment see this recent mathbabe post.
I just returned from Washington after participating in the SEC’s Credit Ratings Roundtable. The experience was very educational, and I wanted to share what I’ve learned with readers interested in financial industry reform.
First and foremost, I learned that the Franken Amendment is dead. While I am not a proponent of this idea – under which the SEC would have set up a ratings agency assignment authority – I do welcome its intentions and mourn its passing. Thus, I want to take some time to explain why I think this idea is dead, and what financial reformers need to do differently if they want to see serious reforms enacted.
The Franken Amendment, as revised by the Dodd Frank conference committee, tasked the SEC with investigating the possibility of setting up a ratings assignment authority and then executing its decision. Within the SEC, the responsibility for Franken Amendment activities fell upon the Office of Credit Ratings (OCR), a relatively new creature of the 2006 Credit Rating Agency Reform Act.
OCR circulated a request for comments – posting the request on its web site and in the federal register – a typical SEC procedure. The majority of serious comments OCR received came from NRSROs and others with a vested interest in perpetuating the status quo or some close approximation thereof. Few comments came from proponents of the Franken Amendment, and some of those that did were inarticulate (e.g., a note from Joe Sixpack of Anywhere, USA saying that rating agencies are terrible and we just gotta do something about them).
OCR summarized the comments in its December 2012 study of the Franken Amendment. Progressives appear to have been shocked that OCR’s work product was not an originally-conceived comprehensive blueprint for a re-imagined credit rating business. Such an expectation is unreasonable. SEC regulators sit in Washington and New York; not Silicon Valley. There is little upside and plenty of political downside to taking major risks. Regulators are also heavily influenced by the folks they regulate, since these are the people they talk to on a day-to-day basis.
Political theorists Charles Lindblom and Aaron Wildavsky developed a theory that explains the SEC’s policymaking process quite well: it is called incrementalism. Rather than implement brand new ideas, policymakers prefer to make marginal changes by building upon and revising existing concepts.
While I can understand why Progressives think the SEC should “get off its ass” and really fix the financial industry, their critique is not based in the real world. The SEC is what it is. It will remain under budget pressure for the forseeable future because campaign donors want to restrict its activities. Staff will always be influenced by financial industry players, and out-of-the-box thinking will be limited by the prevailing incentives.
Proponents of the Franken Amendment and other Progressive reforms have to work within this system to get their reforms enacted. How? The answer is simple: when a request for comment arises they need to stuff the ballot box with varying and well informed letters supporting reform. The letters need to place proposed reforms within the context of the existing system, and respond to anticipated objections from status quo players. If 20 Progressive academics and Occupy-leaning financial industry veterans had submitted thoughtful, reality-based letters advocating the Franken Amendment, I believe the outcome would have been very different. (I should note that Occupy the SEC has produced a number of comment letters, but they did not comment on the Franken Amendment and I believe they generally send a single letter).
While the Franken Amendment may be dead, I am cautiously optimistic about the lifecycle of my own baby: open source credit rating models. I’ll start by explaining how I ended up on the panel and then conclude by discussing what I think my appearance achieved.
The concept of open source credit rating models is extremely obscure. I suspect that no more than a few hundred people worldwide understand this idea and less than a dozen have any serious investment in it. Your humble author and one person on his payroll, are probably the world’s only two people who actually dedicated more than 100 hours to this concept in 2012.
That said, I do want to acknowledge that the idea of open source credit rating models is not original to me – although I was not aware of other advocacy before I embraced it. Two Bay Area technologists started FreeRisk, a company devoted to open source risk models, in 2009. They folded the company without releasing a product and went on to more successful pursuits. FreeRisk left a “paper” trail for me to find including an article on the P2P Foundation’s wiki. FreeRisk’s founders also collaborated with Cate Long, a staunch advocate of financial markets transparency, to create riski.us – a financial regulation wiki.
In 2011, Cathy O’Neil (a.k.a. Mathbabe) an influential Progressive blogger who has a quantitative finance background ran a post about the idea of open source credit ratings, generating several positive comments. Cathy also runs the Alternative Banking group, an affiliate of Occupy Wall Street that attracts a number of financially literate activists.
I stumbled across Cathy’s blog while Googling “open source credit ratings”, sent her an email, had a positive phone conversation and got an invitation to address her group. Cathy then blogged about my open source credit rating work. This too was picked up on the P2P Foundation wiki, leading ultimately to a Skype call with the leader of the P2P Foundation, Michel Bauwens. Since then, Michel – a popularizer of progressive, collaborative concepts – has offered a number of suggestions about organizations to contact and made a number of introductions.
Most of my outreach attempts on behalf of this idea – either made directly or through an introduction – are ignored or greeted with terse rejections. I am not a proven thought leader, am not affiliated with a major research university and lack a resume that includes any position of high repute or authority. Consequently, I am only a half-step removed from the many “crackpots” that send around their unsolicited ideas to all and sundry.
Thus, it is surprising that I was given the chance to address the SEC Roundtable on May 14. The fact that I was able to get an invitation speaks well of the SEC’s process and is thus worth recounting. In October 2012, SEC Commissioner Dan Gallagher spoke at the Stanford Rock Center on Corporate Governance. He mentioned that the SEC was struggling with the task of implementing Dodd Frank Section 939A, which calls for the replacement of credit ratings in federal regulations, such as those that govern asset selection by money market funds.
After his talk, I pitched him the idea of open source credit ratings as an alternative creditworthiness standard that would satisfy the intentions of 939A. He suggested that I write to Tom Butler, head of the Office of Credit Ratings (OCR) and copy him. This led to a number of phone calls and ultimately a presentation to OCR staff in New York in January. Staff members that joined the meeting were engaged and asked good questions. I connected my proposal to an earlier SEC draft regulation which would have required structured finance issuers to publish cashflow waterall models in Python – a popular open source language.
I walked away from the meeting with the perception that, while they did not want to reinvent the industry, OCR staff were sincerely interested in new ideas that might create incremental improvements. That meeting led to my inclusion in the third panel of the Credit Ratings Roundtable.
For me, the panel discussion itself was mostly positive. Between the opening statement, questions and discussion, I probably had about 8 minutes to express my views. I put across all the points I hoped to make and even received a positive comment from one of the other panelists. On the downside, only one commissioner attended my panel – whereas all five had been present at the beginning of the day when Al Franken, Jules Kroll, Doug Peterson and other luminaries held the stage.
The roundtable generated less media attention than I expected, but I got an above average share of the limited coverage relative to the day’s other 25 panelists. The highlight was a mention in the Wall Street Journal in its pre-roundtable coverage.
Perhaps the fact that I addressed the SEC will make it easier for me to place op-eds and get speaking engagements to promote the open source ratings concept. Only time will tell. Ultimately, someone with a bigger reputation than mine will need to advocate this concept before it can progress to the next level.
Also, the idea is now part of the published record of SEC deliberations. The odds of it getting into a proposed regulation remain long in the near future, but these odds are much shorter than they were prior to the roundtable.
Political scientist John Kingdon coined the term “policy entrepreneurs” to describe people who look for and exploit opportunities to inject new ideas into the policy discussion. I like to think of myself as a policy entrepreneur, although I have a long way to go before I become a successful one. If you have read this far and also have strongly held beliefs about how the financial system should improve, I suggest you apply the concepts of incrementalism and policy entrepreneurship to your own activism.
This is a guest post by Adam Obeng, a Ph.D. candidate in the Sociology Department at Columbia University. His work encompasses computational social science, social network analysis and sociological theory (basically anything which constitutes an excuse to sit in front of a terminal for unadvisably long periods of time). This post is Copyright Adam Obeng 2013 and licensed under a (Creative Commons Attribution-ShareAlike 3.0 Unported License). Crossposted on adamobeng.com.
Eben Moglen’s delivery leaves you in no doubt as to the sincerity of this sentiment. Stripy-tied, be-hatted and pocked-squared, he took to the stage at last week’s IDSE Seminar Series event without slides, but with engaging — one might say, prosecutorial — delivery. Lest anyone doubt his neckbeard credentials, he let slip that he had participated in the development of almost certainly the first networked email system in the United States, as well as mentioning his current work for the Freedom Box Foundation and the Software Freedom Law Center.
A superorganism called humankind
The content was no less captivating than the delivery: we were invited to consider the world where every human consciousness is connected by an artificial extra-skeletal nervous system, linking everyone into a new superorganism. What we refer to as data science is the nascent study of flows of neural data in that network. And having access to the data will entirely transform what the social sciences can explain: we will finally have a predictive understanding of human behaviour, based not on introspection but empirical science. It will do for the social sciences what Newton did for physics.
The reason the science of the nervous system – “this wonderful terrible art” – is optimised to study human behaviour is because consumption and entertainment are a large part of economic activity. The subjects of the network don’t own it. In a society which is more about consumption than production, the technology of economic power will be that which affects consumption. Indeed, what we produce becomes information about consumption which is itself used to drive consumption. Moglen is matter-of-fact: this will happen, and is happening.
And it’s also ineluctable that this science will be used to extend the reach of political authority, and it has the capacity to regiment human behaviour completely. It’s not entirely deterministic that it should happen at a particular place and time, but extrapolation from history suggests that somewhere, that’s how it’s going to be used, that’s how it’s going to come out, because it can. Whatever is possible to engineer will eventually be done. And once it’s happened somewhere, it will happen elsewhere. Unlike the components of other super-organisms, humans possess consciousness. Indeed, it is the relationship between sociality and consciousness that we call the human condition. The advent of the human species-being threatens that balance.
The Oppenheimer moment
Moglen’s vision of the future is, as he describes it, both familiar and strange. But his main point, is as he puts it, very modest: unless you are sure that this future is absolutely 0% possible, you should engage in the discussion of its ethics.
First, when the network is wrapped around every human brain, privacy will be nothing more than a relic of the human past. He believes that privacy is critical to creativity and freedom, but really the assumption that privacy – the ability to make decisions independent of the machines – should be preserved is axiomatic.
What is crucial about privacy is that it is not personal, or even bilateral, it is ecological: how others behave determine the meaning of the actions I take. As such, dealing with privacy requires an ecological ethics. It is irrelevant whether you consent to be delivered poisonous drinking water, we don’t regulate such resources by allowing individuals to make desicions about how unsafe they can afford their drinking water to be. Similarly, whether you opt in or opt out of being tracked online is irrelevant.
The existing questions of ethics that science has had to deal with – how to handle human subjects – are of no use here: informed consent is only sufficient when the risks to investigating a human subject produce apply only to that individual.
These ethical questions are for citizens, but perhaps even more so for those in the business of making products from personal information. Whatever goes on to be produced from your data will be trivially traced back to you. Whatever finished product you are used to make, you do not disappear from it. What’s more, the scientists are beholden to the very few secretive holders of data.
Consider, says Moglen,the question of whether punishment deters crime: there will be increasing amounts of data about it, but we’re not even going to ask – because no advertising sale depends on it. Consider also, the prospect of machines training humans, which is already beginning to happen. The Coursera business model is set to do to the global labour market what Google did to the global advertising market: auctioning off the good learners, found via their learning patterns, to to employers. Granted, defeating ignorance on a global scale is within grasp. But there are still ethical questions here, and evil is ethics undealt with.
One of the criticisms often levelled at techno-utopians is that the enabling power of technology can very easily be stymied by the human factors, the politics, the constants of our species, which cannot be overwritten by mere scientific progress. Moglen could perhaps be called a a techno-dystopian, but he has recognised that while the technology is coming, inevitably, how it will affect us depends on how we decide to use it.
But these decisions cannot just be made at the individual level, Moglen pointed out, we’ve changed everything except the way people think. I can’t say that I wholeheartedly agree with either Moglen’s assumptions or his conclusions, but he is obviously asking important questions, and he has shown the form in which they need to be asked.
Another doubt: as a social scientist, I’m also not convinced that having all these data available will make all human behaviour predictable. We’ve catalogued a billion stars, the Large Hadron Collider has produced a hundred thousand million million bytes of data, and yet we’re still trying to find new specific solutions to the three-body problem. I don’t think that just having more data is enough. I’m not convinced, but I don’t think it’s 0% possible.
This post is Copyright Adam Obeng 2013 and licensed under a (Creative Commons Attribution-ShareAlike 3.0 Unported License).
This is a guest post by Kaisa Taipale. Kaisa got a BS at Caltech, a Ph.D. in math at the University of Minnesota, was a post-doc at MSRI, an assistant professor at St. Olaf College 2010-2012, and is currently visiting Cornell, which is where I met here a couple of weeks ago, and where she told me about her cool visualizations of math Ph.D. emigration patterns and convinced her to write a guest post. Here’s Kaisa on a bridge:
Math data and viz
I was inspired by this older post on Mathbabe, about visualizing the arXiv postings of various math departments.
It got me thinking about tons of interesting questions I’ve asked myself and could answer with visualizations: over time, what’s been coolest on the arXiv? are there any topics that are especially attractive to hiring institutions? There’s tons of work to do!
I had to start somewhere though, and as I’m a total newbie when it comes to data analysis, I decided to learn some skills while focusing on a data set that I have easy non-technical access to and look forward to reading every year. I chose the AMS Annual Survey. I also wanted to stick to questions really close to my thoughts over the last two years, namely the academic job search.
I wanted to learn to use two tools, R and Circos. Why Circos? See the visualizations of college major and career path here - it’s pretty! I’ve messed around with a lot of questions, but in this post I’ll look at two and a half.
Where do graduating PhDs from R1 universities end up, in the short term? I started with graduates of public R1s, as I got my PhD at one.
The PhD-granting institutions are colored green, while academic institutions granting other degrees are in blue. Purple is for business, industry, government, and research institutions. Red is for non-U.S. employment or people not seeking — except for the bright red, which is still seeking. Yellow rounds things out at unknown. Remember, these figures are for immediate plans after graduation rather than permanent employment.
While I was playing with this data (read “learning how to use the reshape and ggplot2 packages”) I noticed that people from private R1s tend to end up at private R1s more often. So I graphed that too.
Does the professoriate in the audience have any idea if this is self-selection or some sort of preference on the part of employers? Also, what happened between 2001 and 2003? I was still in college, and have no idea what historical events are at play here.
Where mathematicians go
For any given year, we can use a circular graph to show us where people go. This is a more clumped version of the above data from 2010 alone, plotted using Circos. (Supplemental table E.4 from the AMS report online.)
The other question – the question current mathematicians secretly care more about, in a gossipy and potentially catty way – is what fields lead to what fate. We all know algebra and number theory are the purest and most virtuous subjects, and applied math is for people who want to make money or want to make a difference in the world.
[On that note, you might notice that I removed statistics PhDs in the visualization below, and I also removed some of the employment sectors that gained only a few people a year. The stats ribbons are huge and the small sectors are very small, so for looks alone I took them out.]
Higher resolution version available here.
I wish I could animate a series of these to show this view over time as well. Let me know if you know how to do that! Another nice thing I could do would be to set up a webpage in which these visualizations could be explored in a bit more depth. (After finals.)
- I haven’t computed any numbers for you
- the graphs from R show employment in each field by percentage of graduates instead of total number per category;
- it’s hard to show both data over time and all the data one could explore. But it’s a start.
I should finish with a shout-out to Roger Peng and Jeff Leek, though we’ve never met: I took Peng’s Computing for Data Analysis and much of Leek’s Data Analysis on Coursera (though I’m one of those who didn’t finish the class). Their courses and Stack Overflow taught me almost everything I know about R. As I mentioned above, I’m pretty new to this type of analysis.
What questions would you ask? How can I make the above cooler? Did you learn anything?
This is a guest post by Josh Snodgrass.
As the Mathbabe noted recently, a lot of companies are collecting a lot of information about you. Thanks to two Firefox add-ons – Collusion (hat tip to Cathy) and NoScript — you can watch the process and even interfere with it to a degree.
Collusion is a beautiful app that creates a network graph of the various companies that have information about your web activity. Here is an example.
On this graph, I can see that nytimes.com has sent info on me to 2mdn.net, linkstorm.net, serving-sys.com, nyt.com and doubleclick.net. Who are these guys? All I know is that they know more about me than I know about them.
Doubleclick is particularly well-informed. They have gotten information on me from nytimes.com, yahoo.com and ft.com. You may not be able to see it on the picture but there are faint links between the nodes. Some (few) of the nodes are sites I have visited. Most of the nodes, especially some of the central ones are data collectors such as doubleclick and googleanalytics. They have gotten info from sites I’ve visited.
This graph is pretty sparse because I cleared all of my cookies recently. If I let it go for a week and the graph will be so crowded it won’t all fit on a screen.
Pretty much everyone is sharing info about me (and presumably you, too). And, I do mean everyone. Mathbabe is a dot near the top. Collusion tells me that mathbabe.org has shared info with google.com, wordpress.com, wp.com, 52shadesofgreed.com, youtube.com and quantserve.com. Google has passed the info on to googleusercontent.com and gstatic.com
I can understand why. WordPress and presumably wp.com are hosting her blog. Google is providing search capabilities. 52shadesofgreed has an ad posted (You can still buy the decks but even better, come to Alt-Banking meetings and get one free). Youtube is providing some content. It is all innocent enough in a way but it means my surfing is being tracked even on non-commercial sites.
These are the conveniences of modern life. Try blocking all cookies and you will find it pretty inconvenient to use the internet. It would be nice to be selective about cookies but that seems very hard. All of this is happening even though I’ve told my browser not to allow third-party cookies. If you look at cookie policies, it seems you have two alternatives:
- Block all cookies and the site won’t work very well
- Allow cookies and we will send your info to whomever we choose (within the law, of course).
So, it would be nice if there were a law that constrained what they do. My impression is that we Americans have virtually no protection. Europe is better from what I understand.
I’m trying to access a site and there are scripts waiting to run from:
- Po.st Scorecard.com
Clearly a lot of those are about tracking me or showing me ads. As with cookies, if you block all the scripts, the site probably won’t function properly. But the great thing about NoScript is that is makes it easy to allow scripts one by one. So, you can allow the ones that look more legitimate until the site works well enough. Also, you can allow them temporarily.
NoScript and Collusion are great. But mostly they are making me more aware of all the tracking that is going on. And they are also making it clear how hard it is to keep your privacy.
This isn’t just on the internet. Years ago, an economist had an idea about having people put boxes on their cars that would track where they went and charge them for driving, particularly in high congestion times and places. The motivation was to reduce travel that causes a lot of pollution while no one is going anywhere. But people ridiculed the idea. Who would let themselves be tracked everywhere they went.
Well, 40 years later, nearly everyone who has a car has an EZ-pass. And, even if you don’t, they will take a picture of your license plate and keep it on file. All in the name of improving traffic flow.
And, if you use credit cards, there are some big companies that have records of your spending.
What to do about this?
I don’t know.
I like conveniences. Keeping your privacy is hard. DuckDuckGo is a search engine that doesn’t track you (another hat tip to Cathy). But their search results are not as good as Google’s.
Google has all these nice tools that are free. Even if you don’t use them, the web sites you visit surely do. And if they do, google is getting information from them, about you.
This experience has made me even more of a fan of Firefox and add-ons available in it. But what else should I use. And, none of these tools is going to be perfect.
What information gets tracked? A lot of privacy policies say they don’t give out identifying information. But how can we tell?
Just keeping on top of what is going on is hard. For example: what are LSOs? They seem to be a kind of “supercookies”. And Better Privacy seems to be an add-on to help with them.
“Our emails may contain a single, campaign-unique “web beacon pixel” to tell us whether our emails are opened and verify any clicks through to links or advertisements within the email”
Who knew that a pixel could do so much?
The truth is, I want to see these sites. So I am enabling scripts (some of them, as few as I can). The question is how to make the tradeoff. Figuring that out is time consuming. I’ve got better things to do with my life.
I’m going to go read a book.
This is a guest post by Becky Jaffe.
Today is National Poem in your Pocket Day, a good day to wear extra pockets.
April also just so happens to be National Poetry Month and Mathematics Awareness Month. Good gods, such abundance! In celebration of the marriage of the left and right hemispheres of the brain, I bring you a selection of poems dedicated to the fine art of mathematics – everything from the mystical to the sassy. Enjoy!
from Treatise on Infinite Series by Jacob Bernoulli
Even as the finite encloses an infinite series
And in the unlimited limits appear,
So the soul of immensity dwells in minutia
And in narrowest limits no limits inhere.
What joy to discern the minute in infinity!
The vast to perceive in the small, what divinity!
A Biblical version of pi
Is recorded by some unknown guy
In “Kings,” * where he mentions
A basin’s dimensions –
Not exact, but a pretty good try.
* I Kings 7:23
Sir Isaac Newton by Paul Ritger
While studying pressures and suctions,
Sir Isaac performed some deductions,
“Fill a mug to the brim, it
Will then reach a limit,
So easily determined by fluxions.”
A New Solution to an Old Problem by Eleanor Ninestein
The Topologist’s child was quite hyper
‘Til she wore a Moebius diaper.
The mess on the inside
Was thus on the outside
And it was easy for someone to wipe her.
Threes by John Atherton
I think that I shall never c
A # lovelier than 3;
For 3 < 6 or 4,
And than 1 it’s slightly more.
All things in nature come in 3s,
Like … , trio’s, Q.E.D.s;
While $s gain more dignity
if augmented 3 x 3 –
A 3 whose slender curves are pressed
By banks, for compound interest;
Oh, would that, paying loans or rent,
My rates were only 3%!
3² expands with rapture free,
And reaches toward infinity;
3 complements each x and y,
And intimately lives with pi.
A circle’s # of °
Are best ÷ up by 3s,
But wrapped in dim obscurity
Is the square root of 3.
Atoms are split by men like me,
But only God is 1 in 3.
You disintegrate my differential,
You dislocate my focus.
My pulse goes up like an exponential
whenever you cross my locus.
Without you, sets are null and void –
so won’t you be my cardioid?
An Integral Limerick by Betsy Devine and Joel E. Cohen
Here’s a limerick –
Which, of course, translates to:
Integral z-squared dz
from 1 to the cube root of 3
times the cosine
of three pi over 9
equals log of the cube root of ‘e’.
PROF OF PROFS By Geoffrey Brock
I was a math major—fond of all things rational.
It was the first day of my first poetry class.
The prof, with the air of a priest at Latin mass,
told us that we could “make great poetry personal,”
could own it, since poetry we memorize sings
inside us always. By way of illustration
he began reciting Shelley with real passion,
but stopped at “Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!”—
because, with that last plosive, his top denture
popped from his mouth and bounced off an empty chair.
He blinked, then offered, as postscript to his lecture,
a promise so splendid it made me give up math:
“More thingth like that will happen in thith clath.”
The last poem in today’s guest post is by a mathematician who proved the Kissing Circles Theorem, which states that if four circles are all tangent to each other, then they must intersect at six distinct points. Frederick Soddy wrote up his proof in the form of a poem, published in 1936 in Nature magazine.
The Kiss Precise By Frederick Soddy
For pairs of lips to kiss maybe
Involves no trigonometry.
This not so when four circles kiss
Each one the other three.
To bring this off the four must be
As three in one or one in three.
If one in three, beyond a doubt
Each gets three kisses from without.
If three in one, then is that one
Thrice kissed internally.
Four circles to the kissing come.
The smaller are the benter.
The bend is just the inverse of
The distance form the center.
Though their intrigue left Euclid dumb
There’s now no need for rule of thumb.
Since zero bend’s a dead straight line
And concave bends have minus sign,
The sum of the squares of all four bends
Is half the square of their sum.
To spy out spherical affairs
An oscular surveyor
Might find the task laborious,
The sphere is much the gayer,
And now besides the pair of pairs
A fifth sphere in the kissing shares.
Yet, signs and zero as before,
For each to kiss the other four
The square of the sum of all five bends
Is thrice the sum of their squares.
in Nature, June 20, 1936
The publication of this proof was followed six months later with an additional verse by Thorold Gosset, who generalized the case.
The Kiss Precise (generalized) by Thorold Gosset
And let us not confine our cares
To simple circles, planes and spheres,
But rise to hyper flats and bends
Where kissing multiple appears,
In n-ic space the kissing pairs
Are hyperspheres, and Truth declares,
As n + 2 such osculate
Each with an n + 1 fold mate
The square of the sum of all the bends
Is n times the sum of their squares.
in Nature, January 9, 1937.
This was further amended by Fred Lunnon, who added a final verse:
The Kiss Precise (Further Generalized) by Fred Lunnon
How frightfully pedestrian
My predecessors were
To pose in space Euclidean
Each fraternising sphere!
Let Gauss’ k squared be positive
When space becomes elliptic,
And conversely turn negative
For spaces hyperbolic:
Squared sum of bends is sum times n
Of twice k squared plus squares of bends.
These three raised the bar for presentation of mathematical proof and dialogue, throwing down the gauntlet to modern mathematicians to versify their findings. Who, dear readers, is up for the challenge?
Happy Poem in Your Pocket Day!
This is a guest post by Justin Wedes. A graduate of the University of Michigan with degrees in Physics and Linguistics with High Honors, Justin has taught formerly truant and low-income youth in subjects ranging from science to media literacy and social justice activism. A founding member of the New York City General Assembly (NYCGA), the group that brought you Occupy Wall Street, Justin continues his education activism with the Grassroots Education Movement, Class Size Matters, and now serves as the Co-Principal of the Paul Robeson Freedom School.
Yesterday was tax day, when millions of Americans fulfilled that annual patriotic ritual that funds roads, schools, libraries, hospitals, and all those pesky social services that regular people rely upon each day to make our country liveable.
Millions of Americans, yes, but not ALL Americans.
Some choose to help fund roads, schools, libraries, hospitals in other places instead. Like the Cayman Islands.
Don’t get me wrong – I love Caymanians. Beautifully hospitable people they are, and they enjoy arguably the most progressive taxes in the world: zero income tax and only the rich pay when they come to work – read “cook the books” – on their island for a few days a year. School is free, health care guaranteed to all who work. It’s a beautiful place to live, wholly subsidized by the 99% in developed countries like yours and mine.
When they stash their money abroad and don’t pay taxes while doing business on our land, using our workforce and electrical grids and roads and getting our tax incentives to (not) create jobs, WE pay.
We small businesses.
I went down to the Caymans myself to figure out just how easy it is to open an offshore tax haven and start helping Caymanians – and myself – rather than Americans.
Here’s what happened:
This is a guest post by Julia Evans. Julia is a data scientist & programmer who lives in Montréal. She spends her free time these days playing with data and running events for women who program or want to — she just started a Montréal chapter of pyladies to teach programming, and co-organize a monthly meetup called Montréal All-Girl Hack Night for women who are developers.
I asked mathbabe a question a few weeks ago saying that I’d recently started a data science job without having too much experience with statistics, and she asked me to write something about how I got the job. Needless to say I’m pretty honoured to be a guest blogger here :) Hopefully this will help someone!
Last March I decided that I wanted a job playing with data, since I’d been playing with datasets in my spare time for a while and I really liked it. I had a BSc in pure math, a MSc in theoretical computer science and about 6 months of work experience as a programmer developing websites. I’d taken one machine learning class and zero statistics classes.
In October, I left my web development job with some savings and no immediate plans to find a new job. I was thinking about doing freelance web development. Two weeks later, someone posted a job posting to my department mailing list looking for a “Junior Data Scientist”. I wrote back and said basically “I have a really strong math background and am a pretty good programmer”. This email included, embarrassingly, the sentence “I am amazing at math”. They said they’d like to interview me.
The interview was a lunch meeting. I found out that the company (Via Science) was opening a new office in my city, and was looking for people to be the first employees at the new office. They work with clients to make predictions based on their data.
My interviewer (now my manager) asked me about my role at my previous job (a little bit of everything — programming, system administration, etc.), my math background (lots of pure math, but no stats), and my experience with machine learning (one class, and drawing some graphs for fun). I was asked how I’d approach a digit recognition problem and I said “well, I’d see what people do to solve problems like that, and I’d try that”.
I also talked about some data visualizations I’d worked on for fun. They were looking for someone who could take on new datasets and be independent and proactive about creating model, figuring out what is the most useful thing to model, and getting more information from clients.
I got a call back about a week after the lunch interview saying that they’d like to hire me. We talked a bit more about the work culture, starting dates, and salary, and then I accepted the offer.
So far I’ve been working here for about four months. I work with a machine learning system developed inside the company (there’s a paper about it here). I’ve spent most of my time working on code to interface with this system and make it easier for us to get results out of it quickly. I alternate between working on this system (using Java) and using Python (with the fabulous IPython Notebook) to quickly draw graphs and make models with scikit-learn to compare our results.
I like that I have real-world data (sometimes, lots of it!) where there’s not always a clear question or direction to go in. I get to spend time figuring out the relevant features of the data or what kinds of things we should be trying to model. I’m beginning to understand what people say about data-wrangling taking up most of their time. I’m learning some statistics, and we have a weekly Friday seminar series where we take turns talking about something we’ve learned in the last few weeks or introducing a piece of math that we want to use.
Overall I’m really happy to have a job where I get data and have to figure out what direction to take it in, and I’m learning a lot.