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Guest post: Divest from climate change
This is a guest post by Akhil Mathew, a junior studying mathematics at Harvard. He is also a blogger at Climbing Mount Bourbaki.
Climate change is one of those issues that I heard about as a kid, and I assumed naturally that scientists, political leaders, and the rest of the world would work together to solve it. Then I grew up and realized that never happened.
Carbon dioxide emissions are continuing to rise and extreme weather is becoming normal. Meanwhile, nobody in politics seems to want to act, even when major scientific organizations — and now the World Bank — have warned us in the strongest possible terms that the current path towards 
A little frustrated, I decided to show up last fall at my school’s umbrella environmental group to hear about the various programs. Intrigued by a curious-sounding divestment campaign, I went to the first meeting. I had zero knowledge of or experience with the climate movement, and did not realize what it was going to become.
Divestment from fossil fuel companies is a simple and brilliant idea, popularized by Bill McKibben’s article “Global Warming’s Terrifying New Math.” As McKibben observes, there are numerous reasons to divest, both ethical and economic. The fossil fuel reserves of these companies — a determinant of their market value — are five(!) times what scientists estimate can be burned to stay within 2 degree warming.
Investing in fossil fuels is therefore a way of betting on climate change. It’s especially absurd for universities to invest in them, when much of the research on climate change took place there. The other side of divestment is symbolic. It’s not likely that Congress will be able to pass a cap-and-trade or carbon tax system anytime soon, especially when fossil fuel companies are among the biggest contributors to political campaigns.
A series of university divestments would draw attention to the problem. It would send a message to the world: that fossil fuel companies should be shunned, for basing their business model on climate change and then for lying about its dangers. This reason echoes the apartheid divestment campaigns of the 1980s. With support from McKibben’s organization 350.org, divestment took off last fall to become a real student movement, and today, over 300 American universities have active divestment campaigns from their students. Four universities — Unity College, Hampshire College, Sterling College, and College of the Atlantic — have already divested. Divestment is spreading both to Canadian universities and to other non-profit organizations. We’ve been covered in the New York Times, endorsed by Al Gore, and, on the other hand, recently featured in a couple of rants by Fox News.
Divest Harvard
At Harvard, we began our fall semester with a small group of us quietly collecting student petition signatures, mostly by waiting outside the dining halls, but occasionally by going door-to-door among dorms. It wasn’t really clear how many people supported us: we received a mix of enthusiasm, indifference, and occasional amusement from other students.
But after enough time, we made it to 1,000 petition signatures. That was enough to allow us to get a referendum on the student government ballot. The ballot is primarily used to elect student government leaders, but it was our campaign that rediscovered the use of referenda as a tool of student activism. (Following us, two other worthy campaigns — one on responsible investment more generally and one about sexual assault — also created their own referenda.)
After a week of postering and reaching out to student groups, our proposition—that Harvard should divest—won with 72% of the undergraduate student vote. That was a real turning point for us. On the one hand, having people vote on a referendum isn’t the same as engaging in the one-on-one conversations that we did when convincing people to sign our petition. On the other hand, the 72% showed that we had a real majority in support.
The statistic was quickly picked up by the media, since we were the first school to win a referendum on divestment (UNC has since had a winning referendum with 77% support). That was when the campaign took off. People began to take us seriously.
The Harvard administration, which had previously said that they had no intention of considering divestment, promised a serious, forty-five minute meeting with us. We didn’t get what we had aimed for — a private meeting with President Drew Faust — but we had acquired legitimacy from the administration. We were hopeful that we might be able to negotiate a compromise, and ended our campaign last fall satisfied, plotting the trajectory of our campaign at our final meeting.
The spring semester started with a flurry of additional activity and new challenges. On the one hand, we had to plan for the meeting with the administration—more precisely, the Corporation Committee on Social Responsibility. (The CCSR is the subgroup of the Harvard Corporation that decides on issues such as divestment.)
But we also knew that the fight couldn’t be won solely within the system. We had to work on building support on campus, from students and faculty, with rallies and speakers; we also had to reach out to alumni and let them know about our campaign. Fortunately, the publicity generated last semester had brought in a larger group of committed students, and we were able to split our organization into working groups to handle the greater responsibilities.
In Februrary, we got our promised meeting with three members the administration. With three representatives from our group meeting with the CCSR, we had a rally with about 40 people outside to show support:
In the meeting, the administration representatives reiterated their concern about climate change, but questioned divestment as a tool.
Unfortunately, since the meeting, they have continued to reiterate their “presumption against divestment” (a phrase they have used with previous movements). This is the debate we—and students across the nation—are going to have to win.
Divestment alone isn’t going to slow the melting of the Arctic, but it’s a powerful tool to draw attention to climate change and force action from our political system—as it did against apartheid in the 1980s.
And there isn’t much time left. One of the most inspirational things I’ve heard this semester was at the Forward on Climate rally in Washington, D.C. last month, which most of our group attended. Addressing a crowd of 40,000 people, Bill McKibben said “All I ever wanted to see was a movement of people to stop climate change, and now I’ve seen it.”
To me, that’s one of the exciting and hopeful aspects about divestment—that it’s a movement of the people. It’s fundamentally an issue of social justice that we’re facing, and our group’s challenge is to convince Harvard to take it seriously enough to stand up against the fossil fuel industry.
In the meantime, our campaign has been trying to build support from student groups, alumni, and faculty. In a surprise turnaround, one of our members convinced alumnus Al Gore to declare his support for the divestment movement at a recent event on campus. We organized a teach-in the Tuesday before last featuring writer and sociologist Juliet Schor.
On April 11, we will be holding a large rally outside Massachusetts Hall to close out the year and to show support for divestment; we’ll be presenting our petition signatures to the administration. Here’s our most recent picture, taken for the National Day of Action, with some supportive friends from the chess club:
Thanks to Joseph Lanzillo for proofreading a draft of this post.
Giving isn’t the secret
I don’t know if you read this article (h/t Radhika Sainath) on a hyperactive professor and Organizational Psychology researcher, Adam Grant, who always helps people when they ask and has a theory about giving. He claims that generous giving is the answer to getting ahead and feeling and being successful.
Well, as a “strategic giver” myself, let me tell you that giving isn’t the way to get ahead. Not as expressed by Grant, anyway*.
If you look carefully at the story, it reveals a bunch of things. Here are a few of them:
- Grant has a stay-at-home wife who deals with the kids all the time. Even so, she doesn’t seem all that psyched about how much time he devotes to helping other people (“Sometimes I tell him, ‘Adam — just say no,’ ”).
- He works all the time and misses sleep to get stuff done.
- He engages in high-profile strategic helping – he helps colleagues and students.
- Moreover, he does it in exaggerated and dramatic ways, leading to people talking about him and thanking him profusely, generally giving him attention.
- Considering that his area of research is how to get people to work hard and be more efficient through helping each other, this attention directly in line with his goal of gaining status.
- Just to be clear, he isn’t researching how to get other people to have high status like him, but rather how to get people to work harder in boring-ass jobs.
Put it all together, and you’ve got this disconnect between the way he applies “helping” to himself and to the subjects in his research.
He researches people in call centers, for example, and figures out how to get them to really believe in their work by seeing someone who benefitted from the associated scholarship program. But working harder doesn’t get them more status, it just makes them tired. The other examples in the article are similar. Actually some of them get grosser. Here’s a tasty excerpt from the article:
Jerry Davis, a management professor who taught Grant at the University of Michigan and is generally a fan of [Adam Grant]’s work, couldn’t help making a pointed critique about its inherent limits when they were on a panel together: “So you think those workers at the Apple factory in China would stop committing suicide if only we showed them someone who was incredibly happy with their iPhone?”
So what does he means by “giving” when he’s considering other people? Working really hard in a dead-end job? Kinda reminds me of this review of Sheryl Sandberg’s “Lean In” book, written by ex-Facebook disgruntled speech writer Kate Losse. Here’s my favorite line from that bitter essay:
For Sandberg, pregnancy must be converted into a corporate opportunity: a moment to convince a woman to commit further to her job. Human life as a competitor to work is the threat here, and it must be captured for corporate use, much in the way that Facebook treats users’ personal activities as a series of opportunities to fill out the Facebook-owned social graph.
In other words, Grant, like Sandberg, is selling us a message of working really hard with the underlying promise that it will make us successful, especially if we do it because we just love working really hard.
What?
First, it really matters what you work on and who you are helping. If you are not a strategic helper, you end up wasting your time for no good reason. How many times have we seen people who end up doing their job plus someone else’s job, without any thanks or extra money?
If you work really hard on a project which nobody cares about, nobody appreciates it. True.
And if you aren’t a political animal, able to smell out the projects and people that are worth working on extra hard and helping, then you’re pretty much out of luck.
But let’s take one step back from the terrible advice being given by Grant and Sandberg. What are their actual goals? Is it possible that they really think just by working extra hard at whatever shit corporate job we have will leave us successful and fulfilled? Are they that blind to other people’s options? Do they really know nobody in their private lives who found fulfillment by quitting their dead-end corporate job and became a poor but happy poet?
Here’s what Kate Losse says, and I think she hit the nail on the head:
Sandberg is betting that for some women, as for herself, the pursuit of corporate power is desirable, and that many women will ramp up their labor ever further in hopes that one day they, too, will be “in.” And whether or not those women make it, the companies they work for will profit by their unceasing labor.
Similarly, Grant’s personal academic success comes from getting people to work harder. His incentive is to get you to work harder, not be fulfilled. Just to be clear.
* I actually do think giving is a wonderful thing, but certainly not exclusively at work, and it’s not a secret.
Value-added model doesn’t find bad teachers, causes administrators to cheat
There’ve been a couple of articles in the past few days about teacher Value-Added Testing that have enraged me.
If you haven’t been paying attention, the Value-Added Model (VAM) is now being used in a majority of the states (source: the Economist):
But it gives out nearly random numbers, as gleaned from looking at the same teachers with two scores (see this previous post). There’s a 24% correlation between the two numbers. Note that some people are awesome with respect to one score and complete shit on the other score:
Final thing you need to know about the model: nobody really understands how it works. It relies on error terms of an error-riddled model. It’s opaque, and no teacher can have their score explained to them in Plain English.
Now, with that background, let’s look into these articles.
First, there’s this New York Times article from yesterday, entitled “Curious Grade for Teachers: Nearly All Pass”. In this article, it describes how teachers are nowadays being judged using a (usually) 50/50 combination of classroom observations and VAM scores. This is different from the past, which was only based on classroom observations.
What they’ve found is that the percentage of teachers found “effective or better” has stayed high in spite of the new system – the numbers are all over the place but typically between 90 and 99 percent of teachers. In other words, the number of teachers that are fingered as truly terrible hasn’t gone up too much. What a fucking disaster, at least according to the NYTimes, which seems to go out of its way to make its readers understand how very much high school teachers suck.
A few things to say about this.
- Given that the VAM is nearly a random number generator, this is good news – it means they are not trusting the VAM scores blindly. Of course, it still doesn’t mean that the right teachers are getting fired, since half of the score is random.
- Another point the article mentions is that failing teachers are leaving before the reports come out. We don’t actually know how many teachers are affected by these scores.
- Anyway, what is the right number of teachers to fire each year, New York Times? And how did you choose that number? Oh wait, you quoted someone from the Brookings Institute: “It would be an unusual profession that at least 5 percent are not deemed ineffective.” Way to explain things so scientifically! It’s refreshing to know exactly how the army of McKinsey alums approach education reform.
- The overall article gives us the impression that if we were really going to do our job and “be tough on bad teachers,” then we’d weight the Value-Added Model way more. But instead we’re being pussies. Wonder what would happen if we weren’t pussies?
The second article explained just that. It also came from the New York Times (h/t Suresh Naidu), and it was a the story of a School Chief in Atlanta who took the VAM scores very very seriously.
What happened next? The teachers cheated wildly, changing the answers on their students’ tests. There was a big cover-up, lots of nasty political pressure, and a lot of good people feeling really bad, blah blah blah. But maybe we can take a step back and think about why this might have happened. Can we do that, New York Times? Maybe it had to do with the $500,000 in “performance bonuses” that the School Chief got for such awesome scores?
Let’s face it, this cheating scandal, and others like it (which may never come to light), was not hard to predict (as I explain in this post). In fact, as a predictive modeler, I’d argue that this cheating problem is the easiest thing to predict about the VAM, considering how it’s being used as an opaque mathematical weapon.
Aunt Pythia’s advice
Many thanks to Aunt Orthoptera for her fascinating, insect-related advice from last week.
Aunt Pythia is psyched to be back, is psyched to refer to herself in the third person, and is psyched to continue her sex and dating advice far beyond what anyone asked for or wants.
If you don’t know what you’re in for, go here for past advice columns and here for an explanation of the name Pythia. Most importantly,
Please submit your smutty sex questions at the bottom of this column!
——
Aunt Pythia,
After how long without any sign of interest from any member of the (or an) appropriate sex should you give up on trying to date? Also, how do you get over a crush on someone who likes you as a friend and who you want to be friends with when breaking off contact is not an option?
Forever Alone Probably
Dear FAP,
Thank you so much for the question. Over my vacation I read The Game: Penetrating the Secret Society of Pickup Artists by Neil Strauss and I’m dying to talk about it. You’ve given me the perfect excuse to do just that!
According to the book, if you’re a man, you should do a bunch of things to get laid with “really hot chicks”, among them:
- Get used to people saying no to you – don’t dwell on one relationship.
- Dress and appear confident, which means think about how your appearance and actions come off to other people.
- Ignore your “target” (i.e. the really hot chick you’re interested in) until you’ve …
- won the admiration of the alpha male of the group by doing magic tricks (no shit).
- Learn how to “neg” your target once you deign to pay attention to her, which means insult her in playful (read: obnoxious) ways such as (not a hyperbole) carrying around a piece of lint so you can pretend you found it on her outfit and then say, “How long has this been on your shoulder?”
- Once you have her attention, have interesting things to say and…
- generally pay her attention and know how to talk.
- Make it clear that you’re interested in continuing to spend time with her (without be creepy).
Here’s my take on this weird and disturbing set of instructions: it’s not rocket science that it works but it’s unduly evil.
The pick-up artists who studied up and tested out these techniques collected a lot of data and tried out a lot of things. They started out completely dweeby and socially awkward and ended up being able to hold a conversation with a women in a nightclub. They were essentially on-the-ground data scientists.
But they made a classic mistake of data scientists, namely they overfit. They came to the conclusion that they needed to do magic tricks and be assholes to get laid. But just because that didn’t prevent them from getting laid, it doesn’t mean they needed to do that. My theory is that it was a replacement for actually having something interesting to say.
Let me give advice to anyone, man or woman, that I think will help you in terms of meeting people and dating. In fact, this is also my advice for people who aren’t interested in dating but who want to be able to engage socially in any situation.
It’s easy – I’ll just add one thing they forgot about (having a life) and which they replaced by a bunch of unnecessary, stupid and quasi-evil shit:
- Get used to people saying no to you – don’t dwell on one relationship (unless it’s making you happy to do so!).
- Dress and appear confident, which means think about how your appearance and actions come off to other people.
- Work on being an interesting person with cool life goals.
- Once you have someone’s attention, have interesting things to say and…
- generally pay attention to that person and know how to talk.
- Make it clear that you’re interested in continuing to spend time with that person (without be creepy).
Now, to answer your questions.
I don’t think you should give up if you’re actually interested in dating. But I do think you should think about getting over your crush, or at least ignoring your crush sometimes (not the person, the feelings) so that you actually allow yourself to meet other people and find them fascinating. Otherwise, like it or not, you’ll close yourself off to new people and experiences.
Next, keep in mind that the most exciting things to whatever new person you’ve just met is that a) you’re interested in them, b) you’re paying attention to them, and c) you want to spend more time with them whilst d) you’re an interesting person with cool life goals.
About the crush: it won’t seem tragic to have a crush on someone who doesn’t reciprocate when you also have other romantic relationships brewing. In fact it’ll seem cool and awesome to be near someone that attractive.
I hope that helps!
Aunt Pythia
——
Dear Aunt Pythia,
I’ve sometimes noticed on receipts instead of just a single charge for something, a single charge plus a duplicate, plus a credit to nullify the duplicate. This has happened often enough to make me suspicious that these duplicate/credits aren’t appearing by accident. I’ve only noticed it happening at venues owned by large corporations. The most recent occurrence was a checking deposit at a bank, that appeared in duplicate on my statement along with the usual credit/retraction. I wonder, do these fake transactions serve some books-cooking purpose?
Curious Observer-Participant
Dear COP,
I’ve decided to answer this question even though it has nothing to do with sex because, first of all, it’s a fascinating observation and second of all, I think it could do with a bit of data collecting.
Readers, please check your receipts for the next few days for this weird phenomenon. And accountant readers, please explain this weird phenomenon if it does indeed indicate a book-cooking purpose.
Auntie P
——
Dear Aunt Orthoptera,
I have spent the last couple of years modelling the beheavioural neuroscience behind the always respectable Acrididae. Recently, I came across this strange species of primate called Homo sapien. Unlike regular folks who rub their legs against each other when they are gregarious, these primates like to make sounds at each other or send symbols or pictures!
I would very much like to study them and was wondering which part of industrial data science would find my skill set (math, biology, neuroscience) useful. Also have you seen my cousin Melanoplus spretus? I haven’t seen him in a while.
Solitary Schistocerca americana
p.s. here’ my picture:
Dear SSa,
I decided to answer your letter even though it’s addressed to Aunt Orthoptera because it’s about sex.
I wanted to repeat a point about the mating rituals of humans which my friend Laura made recently. Namely, every man she knows somehow knows about The Game: Penetrating the Secret Society of Pickup Artists by Neil Strauss (see above description), even though most women have never heard of it.
At the same time, every woman she knows somehow knows about another book called All the Rules: Time-tested Secrets for Capturing the Heart of Mr. Right by Ellen Fein, even though no man has ever heard of that. [Aunt Pythia’s personal note: I’d never heard of the latter book either]
Both of these are more or less instruction manuals for getting what you want from the opposite sex. Or maybe a better way of describing them would be manipulation manuals. Not much there in terms of adult honesty and saying what you really feel. Makes you wonder if we’re so very different from grasshoppers rubbing their legs together after all.
Food for thought!
Aunt Pythia
——
Please please please submit questions!
WTF is happening in Cyprus?
One thing I kept track of while I was away was the ongoing, intensely interesting situation in Cyprus. For those of you who have been following it just as closely, this will not be new, and please correct me if you think I’ve gotten something wrong.
Background
Cyprus banks have recently gotten deeply in trouble, partly because of their heavy investment in Greek government bonds which as you remember were semi-defaulted on in spite of them being “risk-weighted” at zero, and partly because of an enormous amount of Russian money they hold (Russian businessmen enjoy lowering their taxes by funneling their money to Cyprus), which created a severely bloated financial sector.
To be fair, just having deposits of rich Russian businessmen doesn’t make you fragile. But it’s just not done in banking, I guess, to simply hold on to money – you have to invest it somewhere, and they invested poorly.
To get an idea of how bloated the finance sector is and how badly the banks were hurting, if the Cyprus government was to give them the money they need, it would be 70% of GDP, and they’re already about 90% of GDP in debt. Even so, that’s only 17.2 billion Euros, or a bit more than twice Steven Cohen’s personal fortune ($10 billion) even after his firm, SAC Capital Advisors, settled with the SEC for insider trading “without admitting nor denying wrongdoing”.
What are the options?
- Do we ask the government of Cyprus to prop up the failing banks? Then it (the government) would be underwater and people would stop investing in its bonds and we’d need a bailout of the government. In other words, we’d just be handing the hot potato to the people.
- Does the EU or IMF loan money to government to give to the banks?
- Or to banks directly? Either way this would feel wrong to the northern European taxpayers, who would be essentially bailing out a bunch of Russian businessmen. Europeans are suffering from bailout fatigue, and German elections are coming up, making this even stronger.
- Or do we make the banks deal with their solvency issues themselves? After all, their shareholders, bond holders, and depositors all represent money they have which they can theoretically keep.
- Or some combination? Actually, all plans below are combo plans, whereby the banks make themselves solvent and then, after that, the EU/IMF team kicks in a few billion euros. Whether it will be enough money after the ricochet effects of the plan is not at all clear.
Plan #1: anti-FDIC insurance.
The plan as of more than a week ago was to take money from all the accounts as well as bond holders and shareholders. This included even the so-called insured deposits of accounts below 100,000 Euros.
So normal people, who thought their money was insured, would be paying 6.7% of their savings into a so-called “bail-in” fund, and people with more money in their accounts would be paying 9.9%.
This was across-the-board, by the way, for all Cyprus banks, independent of how much trouble a given bank was in. The banks closed down before this was announced so people couldn’t grab their money.
Compare that to the US version of a bailout from 2008, when shareholders got partially screwed, bondholders were left whole, deposits were untouched, but taxpayers were on the hook (and still are).
Plan #1 was baldfaced: it was saying to the average person in Cyprus, “Hey we fucked up the banks, can we take your money to fix it?”. It was incredible that anyone thought it would work. The ramifications of such an anti-FDIC insurance would be immediate and contagious, namely everyone in any related country would immediately start pulling their money out of banks. Why keep your money in an institution where you’re surely losing 7% when you can hide your money in a suitcase with only a small chance of it getting stolen?
Reaction by public: Hell No
Needless to say, the people in Cyprus didn’t like the plan. In fact, they strongly objected to directly paying for the mistakes of rich bankers and to protect Russians. They protested loudly and the Cypriotic politicians heard them, and voted down plan #1.
Plan #2
Since plan #1 failed, how about we just take money from uninsured depositors? Oh, and also make it bank-specific. So the banks that are in bigger poo-poo would seize more of their deposits than the banks that were in less poo-poo. That makes sense, and seems to be the current plan.
Problems with the current plan
There are a few problems with the new plan. But mostly they are what I’d call transition costs versus long-term problems. Easy for me to say, since I don’t live in Cyprus.
Rich people moving their money
First, rich people everywhere will no longer park lots of money in uninsured accounts in weak banks. Rich people have lots of options, though, so don’t feel too bad for them. They will instead put their money into lots of little accounts in lots of places, each of which will be insured. If this means they distribute their money over more banks, this is good for the banking system because it diversifies the capital and we’d end up with lots of biggish banks instead of a few enormous banks.
I’m not sure what the technical rules are, though. Say I’m stinking rich. Can I open 15 Bank of America accounts, each with $250K and so FDIC-insured? If I can’t do that for my local Bank of America branch, can I use Bank of America subsidiaries? Are the rules the same in the US and Europe? These rules are all of a sudden more important.
This is a transition cost, and within a few months all of the rich people will have their accounts insured or hidden.
Job losses
Second, there will be severe job losses in the bloated finance sector in Cyprus. Right now there are protests by workers from Laiki Bank, which is the worst off Cyprus bank, because they’re poised to lose their jobs. Again, it’s easy for me to say since I don’t live in Cyprus, but that’s what happens when you have an industry that’s too big – at some point it gets smaller and people lose jobs. I was around when the same thing happened to fisherman off the coast of New England, and it wasn’t pretty.
Again, though, it’s transitional. At some point the number of people working in banks in Cyprus will be reasonable. The question is whether they will have found another industry to replace finance.
Capital controls
The banks re-opened today, and of course people are standing in line to get cash, but things generally seem calm.
The big problem for businesses in Cyprus is that various “temporary” capital controls (which just means limits on taking money out of the country and on taking money from your bank) have been put into place that may lead to long-term problems.
Update (hat tip commenter badmax): many Russians already took their money out before the capital controls were imposed.
Euros don’t flow into and out of Cyprus effortlessly anymore, so the so-called monetary union has been broken. Depending on how quickly those rules are removed, and how quickly Cyprus comes up with other things to do, this could be a huge problem for the country.
Take-aways
- What’s become blatantly clear by following this process is that there is no actual process. Things are being made up as they go along by a bunch of economists and finance ministers. A lot of faith in their abilities was lost permanently when they hatched plan #1 which was so obviously stupid.
- Going back to that stupid plan, whereby normal depositors were supposed to pay for the mistakes of banks at the expense of their insured deposits. It was so bald-faced that the citizens rebelled, and politicians listened. So just to be clear, there has been actual input by average people in this process. The economists and finance ministers have lost face and the people have found a voice.
- This is not to say that the Cyprus people are sitting pretty. They are not, and by some estimates the economy of Cyprus is poised to contract by 20%. This may lead to more bailouts or Cyprus leaving the Eurozone for good.
Leila Schneps is a mystery writer!
I’m back! I missed you guys bad.
My experience with Seattle in the last 8 days has convinced me of something I rather suspected, namely I’m a huge New York snob and can’t exist happily anywhere else. I will spare you the details (they have to do with cars, subways, and being an asshole pedestrian) but suffice it to say, glad to be home.
Just a few caveats on complaining about my vacation:
- I enjoyed visiting the University of Washington and giving the math colloquium there as well as a “Math Day” talk where I showed kids the winning strategy for Nim (as well as other impartial two-player games) following my notes from last summer.
- I enjoyed reading Leon and Becky’s guest posts. Thanks guys!
- And then there was the time spent with my darling family. Of course, goes without saying, it’s always magical to get to the point where your kids have invented a whole new language of insults after you’ve outlawed certain words: “Shut your fidoodle, you syncopathic lardle!”
Of all the topics I want to write about today, I’ve decided to go with the most immediate and surprising one : Leila Schneps is now a mystery writer! How cool is that? She’s written a book with her daughter, Math on Trial: How Numbers Get Used and Abused in the Courtroom, currently in stock and available on Amazon. And she wrote an op-ed for the New York Times talking about it (hat tip Chris Wiggins).
I know Leila from having been her grad student assistant at the GWU Summer Program for Women in Math the first year it existed, in 1995. She taught undergrads about Galois cohomology and interpreted elements of 
I love the premise of the book she’s written. She finds a bunch of historical examples where mathematics is used in trials to the detriment of justice, and people get unfairly jailed (or, less often, let free). From the op-ed (emphasis mine):
Decades ago, the Harvard law professor Laurence H. Tribe wrote a stinging denunciation of the use of mathematics at trial, saying that the “overbearing impressiveness” of numbers tends to “dwarf” other evidence. But we neither can nor should throw math out of the courtroom. Advances in forensics, which rely on data analysis for everything from gunpowder to DNA, mean that quantitative methods will play an ever more important role in judicial deliberations.
The challenge is to make sure that the math behind the legal reasoning is fundamentally sound. Good math can help reveal the truth. But in inexperienced hands, math can become a weapon that impedes justice and destroys innocent lives.
Go Leila!
Data science code of conduct, Evgeny Morozov
I’m going on an 8-day long trip to Seattle with my family this morning and I’m taking the time off from mathbabe. But don’t fret! I have a crack team of smartypants skeptics who are writing for me while I’m gone. I’m very much looking forward to seeing what Leon and Becky come up with.
In the meantime, I’ll leave you with two things I’m reading today.
First, a proposed Data Science Code of Professional Conduct. I don’t know anything about the guys at Rose Business Technologies who wrote it except that they’re from Boulder Colorado and have had lots of fancy consulting gigs. But I am really enjoying their proposed Data Science Code. An excerpt from the code after they define their terms:
(c) A data scientist shall rate the quality of evidence and disclose such rating to client to enable client to make informed decisions. The data scientist understands that evidence may be weak or strong or uncertain and shall take reasonable measures to protect the client from relying and making decisions based on weak or uncertain evidence.
(d) If a data scientist reasonably believes a client is misusing data science to communicate a false reality or promote an illusion of understanding, the data scientist shall take reasonable remedial measures, including disclosure to the client, and including, if necessary, disclosure to the proper authorities. The data scientist shall take reasonable measures to persuade the client to use data science appropriately.
(e) If a data scientist knows that a client intends to engage, is engaging or has engaged in criminal or fraudulent conduct related to the data science provided, the data scientist shall take reasonable remedial measures, including, if necessary, disclosure to the proper authorities.
(f) A data scientist shall not knowingly:
- fail to use scientific methods in performing data science;
- fail to rank the quality of evidence in a reasonable and understandable manner for the client;
- claim weak or uncertain evidence is strong evidence;
- misuse weak or uncertain evidence to communicate a false reality or promote an illusion of understanding;
- fail to rank the quality of data in a reasonable and understandable manner for the client;
- claim bad or uncertain data quality is good data quality;
- misuse bad or uncertain data quality to communicate a false reality or promote an illusion of understanding;
- fail to disclose any and all data science results or engage in cherry-picking;
Read the whole Code of Conduct here (and leave comments! They are calling for comments).
Second, my favorite new Silicon Valley curmudgeon is named Evgeny Morozov, and he recently wrote an opinion column in the New York Times. It’s wonderfully cynical and makes me feel like I’m all sunshine and rainbows in comparison – a rare feeling for me! Here’s an excerpt (h/t Chris Wiggins):
Facebook’s Mark Zuckerberg concurs: “There are a lot of really big issues for the world that need to be solved and, as a company, what we are trying to do is to build an infrastructure on top of which to solve some of these problems.” As he noted in Facebook’s original letter to potential investors, “We don’t wake up in the morning with the primary goal of making money.”
Such digital humanitarianism aims to generate good will on the outside and boost morale on the inside. After all, saving the world might be a price worth paying for destroying everyone’s privacy, while a larger-than-life mission might convince young and idealistic employees that they are not wasting their lives tricking gullible consumers to click on ads for pointless products. Silicon Valley and Wall Street are competing for the same talent pool, and by claiming to solve the world’s problems, technology companies can offer what Wall Street cannot: a sense of social mission.
Read the whole thing here.
Modeling in Plain English
I’ve been enjoying my new job at Johnson Research Labs, where I spend a majority of the time editing my book with my co-author Rachel Schutt. It’s called Doing Data Science (now available for pre-purchase at Amazon), and it’s based on these notes I took last semester at Rachel’s Columbia class.
Recently I’ve been working on Brian Dalessandro‘s chapter on logistic regression. Before getting into the brass tacks of that algorithm, which is especially useful when you are trying to predict a binary outcome (i.e. a 0 or 1 outcome like “will click on this ad”), Brian discusses some common constraints to models.
The one that’s particularly interesting to me is what he calls “interpretability”. His example of an interpretability constraint is really good: it turns out that credit card companies have to be able to explain to people why they’ve been rejected. Brain and I tracked down the rule to this FTC website, which explains the rights of consumers who own credit cards. Here’s an excerpt where I’ve emphasized the key sentences:
You Also Have The Right To…
- Have credit in your birth name (Mary Smith), your first and your spouse’s last name (Mary Jones), or your first name and a combined last name (Mary Smith Jones).
- Get credit without a cosigner, if you meet the creditor’s standards.
- Have a cosigner other than your spouse, if one is necessary.
- Keep your own accounts after you change your name, marital status, reach a certain age, or retire, unless the creditor has evidence that you’re not willing or able to pay.
- Know whether your application was accepted or rejected within 30 days of filing a complete application.
- Know why your application was rejected. The creditor must tell you the specific reason for the rejection or that you are entitled to learn the reason if you ask within 60 days. An acceptable reason might be: “your income was too low” or “you haven’t been employed long enough.” An unacceptable reason might be “you didn’t meet our minimum standards.” That information isn’t specific enough.
- Learn the specific reason you were offered less favorable terms than you applied for, but only if you reject these terms. For example, if the lender offers you a smaller loan or a higher interest rate, and you don’t accept the offer, you have the right to know why those terms were offered.
- Find out why your account was closed or why the terms of the account were made less favorable, unless the account was inactive or you failed to make payments as agreed.
The result of this rule is that credit card companies must use simple models, probably decision trees, to make their rejection decisions.
It’s a new way to think about modeling choice, to be sure. It doesn’t necessarily make for “better” decisions from the point of view of the credit card company: random forests, a generalization of decision trees, are known to be more accurate, but are arbitrarily more complicated to explain.
So it matters what you’re optimizing for, and in this case the regulators have decided we’re optimizing for interpretability rather than accuracy. I think this is appropriate, given that consumers are at the mercy of these decisions and relatively powerless to act against them (although the FTC site above gives plenty of advice to people who have been rejected, mostly about how to raise their credit scores).
Three points to make about this. First, I’m reading the Bankers New Clothes, written by Anat Admati and Martin Hellwig (h/t Josh Snodgrass), which is absolutely excellent – I’m planning to write up a review soon. One thing they explain very clearly is the cost of regulation (specifically, higher capital requirements) from the bank’s perspective versus from the taxpayer’s perspective, and how it genuinely seems “expensive” to a bank but is actually cost-saving to the general public. I think the same thing could be said above for the credit card interpretability rule.
Second, it makes me wonder what else one could regulate in terms of plain english modeling. For example, what would happen if we added that requirement to, say, the teacher value-added model? Would we get much-needed feedback to teachers like, “You don’t have enough student participation”? Oh wait, no. The model only looks at student test scores, so would only be able to give the following kind of feedback: “You didn’t raise scores enough. Teach to the test more.”
In other words, what I like about the “Modeling in Plain English” idea is that you have to be able to first express and second back up your reasons for making decisions. It may not lead to ideal accuracy on the part of the modeler but it will lead to much greater clarity on the part of the modeled. And we could do with a bit more clarity.
Finally, what about online loans? Do they have any such interpretability rule? I doubt it. In fact, if I’m not wrong, they can use any information they can scrounge up about someone to decide on who gets a loan, and they don’t have to reveal their decision-making process to anyone. That seems unreasonable to me.
Aunt Pythia’s advice – sex edition
I’m afraid the concept of “giving advice” has been taken down a notch this week, considering how many ridiculous examples we have right now of people are giving advice as a way of congratulating themselves. It’s enough to confuse an advice columnist and put her into an existential angst spiral.
However, it’s not going to stop Aunt Pythia!
At most it will divert her to talk exclusively about something that nobody doesn’t love reading, namely sex. It’s a tried and true last resort of the advice columnist: let out the dirty laundry of yourself and everybody who dares bare themselves to you. I don’t see where this could go wrong.
Having said that, I’m not promising to be exclusive like this every week. I’ll probably cheat on you people every now and then and answer questions about how to get a job in data science or something. Also, my guest advice columnist next week, Aunt Orthoptera, will answer whatever questions she chooses (from a grasshopper’s perspective, of course).
By the way, if you don’t know what you’re in for, go here for past advice columns and here for an explanation of the name Pythia. Most importantly,
Please submit your smutty sex questions at the bottom of this column!
——
Dear Aunt Pythia,
How can I make compatible my sexual attraction for dominant women and my fear of being controlled?
Horny in Montana
Dear Horny,
Let me start out by admitting honestly that I have no direct advice for you. I just don’t know how to resolve issues surrounding sexuality, and I’d be deeply skeptical of anybody who claims to be able to do so.
Sexuality is a crazy thing, a super entrenched and powerful force, and there’s just nothing and nobody who can change it for you once it’s on a roll. Sometimes people seem to be able to change it for themselves, mainly by repressing it, but that’s always so amazing, not to mention deeply threatening, I wouldn’t proffer it as advice.
I sometimes think of my own sexuality as having a personality, and an agenda, that I can only observe, not control. The best case scenario for me has evolved into trying not to be too judgmental of it and to and make sure nothing unsafe happens. I’m like a benign referee of my own dirty urges.
Having said that, I have two pieces of indirect advice for you. First, it would probably be useful to separate sex play with “normal life” and realize that you can ask someone to dominate you in the bedroom, and even pretend to control you, and even actually control you, whilst remaining nothing like that outside the bedroom. That’s totally normal and common and it might help in the sense that you’d actually have control over being controlled: it would happen if and when you wanted it.
The second piece of advice I have it totally selfish, namely, please don’t blame the women of the world for your unresolved problems. Just because you’re both attracted and afraid of these dominant women doesn’t mean they have a responsibility to deal with your confusion and frustration. Don’t take it out on them.
I hope that helps,
Aunt Pythia
——
Dear Aunt Pythia,
What would you say to a woman who told you that she is not able to make a commitment to anyone because she regularly finds herself in search of romance (not originating from sexual desires) with other people? Do you think this is a common behavior?
Itchy Litchi
Dear Itchy,
There are three stages of understanding in this story, at least for me.
First, you know yourself (I’ll refer to “you” even though you might have been asking on behalf of someone else) pretty well if you avoid commitment based on a theoretical understanding of your roaming eye. Most people I know throw themselves into commitment in spite of really good evidence that they won’t be able to sustain it, due to their cognitive biases.
Second, you claim your romantic urges for other people are not sexual. Theoretically this may be true, but in my experience romantic urges are always sexual if you probe deep enough or if they get strong enough. So either I’m a sex maniac (possible) or else you’re in denial about those nonsexual romantic urges.
Third, let’s put the above two together: A) you know yourself deeply, and B) you’re in total denial. The second conclusion makes me rethink the first, honestly, and I come to the conclusion that the first conclusion was wrong. You aren’t avoiding commitment because you know yourself so well, but rather because you’re avoiding commitment for some reason. Maybe you’re afraid of commitment? Maybe you’re afraid of sexual urges, which is why you both avoid commitment and avoid admitting your romantic urges are sexual?
Finally, if this question was actually written by, say, a man who wanted to understand the reasoning a woman gave him for why she couldn’t commit to him: she just wasn’t that into you. And yes that’s a very common behavior.
I hope that helps!
Auntie P
——
Dear Aunt Pythia,
I just studied the “Authentic Women’s Penis Size Preference Chart” (I say “studied” because I need to convert everything to metric units to make any sense of it) and, while – unlike many men, I am told – I am not too concerned about length, I feel that the ideal circumference IS REALLY BIG, at least for a man’s penis. Is this for real? Are women looking all their life for that eluding ideal-sized penis or am I just unlucky?
Concerned Reader
Dear Concerned,
Once again here’s the chart for the readers who missed it last time:
To answer your primary question, it’s not the length, it’s the girth. A truer statement has never been said. Of course, there are exceptions to that rule, namely if the length is truly miniscule.
Now, I do have some comforting words for you, you’ll be happy to know. Namely, my guess is that women responding to this very scientific poll had a biased measurement error. Namely, they didn’t have (probably) an erect penis handy and a flexible measuring tape as well, by their side, whilst answering the poll (apologies to the women who did!).
So what they did is they eyeballed the “circumference” measurement by imaging holding a penis in their hand like an OK sign:
And then, since it’s hard to measure a circle, they then straightened out their fingers. The reason this is so biased is that your fingers and thumb are actually quite a bit longer once you’ve stopped making the OK sign.
There may be a measurement bias of up to 50% on this. Probably not, but I’m trying to make you feel better.
I hope that helps!
Aunt Pythia
——
Please please please submit questions! Especially if they are grasshopper-related!
Data audits and data strategies
There are lots of start-up companies out there that want to have a data team, because they heard somewhere that they should leverage big data, but they don’t know what it really means, what they can expect from such a team, or how to get started. They also don’t really know how to hire qualified people, or what qualifications to look for.
Finally, they often don’t know what kinds of questions are answerable through data, nor what data they should be collecting to answer those questions. So even if they did manage to hire a data scientist or a data team, those guys might be literally sitting on their hands for six months until they have enough data to start work.
It’s a common situation and could end up a big waste time and money. What these companies need is something I like to call a “data audit” followed by a “data strategy”.
Data Audit
First thing’s first. Do you actually need a data team? Is your company a data science company or is it a traditional-style company that happens to collect data? It would be a waste of resources to form a data team you don’t need. There’s no reason every single company needs to consider itself part of the big data revolution just to be cool.
Here’s how you tell. Let’s say that, as of now, you’re using incoming data to monitor and report on what’s happening with the business and to keep tabs on various indicators to make sure things aren’t going to hell. Absolutely every company should do this, but it honestly could be set up by a good data analyst working closely with the end-users, i.e. the business peeps.
What are the high-level goals of using data in the business? In particular, is there a way that, if you could really know how customers or clients were interacting with your product, that you would change the product to respond to the data? Because that feedback loop is the hallmark of a true data science engine (versus data analytics).
Here are some extreme examples to give you an idea of what I’m talking about. If you make shoes, then you need data to see how sales are and which shoes are getting sold faster so you can kick up production in certain areas. You need to see how sales are seasonal so you know to stop making quite so many shoes at a certain point in the deep of winter. But that’s about it, and you should be able to make do with data analysis.
If, on the other hand, you are building a recommendation engine, say for music, then you need to constantly refresh and improve your recommendation model. Your model is your product, and you need a data team.
Not all examples are this easy. Sometimes you can use new kinds of data models to improve your product even if it seems somewhat traditional, depending on how much data you are able to collect about how your clients use your product. It all depends on what kinds of questions you are asking and what data you have access to. Of course, you might want to go out and collect data that you hadn’t bothered to do before, which could bring you from the first category to the second.
Say you decide you really are a data science company, or want to be one. What’s next?
Pose a bunch of questions you think you’ll need to answer and a bunch of data you think should be useful to answer them.
The heart of a data audit is a (preliminary) plan for choosing, collecting, and storing data, as well as figuring out the initial shape of the data pipeline and infrastructure. Do you store data in the cloud? Is it unstructured or do you set up some overnight jobs to put stuff into some type of database? Do you aggregate data and throw some stuff away, or do you keep absolutely everything?
The most important issue above is whether you’re collecting enough data. Truth be told, you could probably throw it all into an unstructured pile on S3 for now and figure out pipelines later. It might not be the best way to do it but if you are short for time and attention, it’s possible, and storage is cheap. But make sure you’re collecting the right stuff!
You’d be surprised how many startups want to ask good questions about their customers to improve their product, and have gone to some trouble to figure out what those questions are, but don’t bother to collect the relevant information. They might do things like count the number of users, or collect a timestamp for whenever a user logs in, but they don’t actually keep track of the interaction. It’s essential that you collect pertinent information if you want to use this data to check things are working or to predict people’s desires or needs.
So if you think customers might be all ditching your site at critical moments, then definitely tag their departure as well as their arrival, and keep track of where they were and what they were doing when they bailed.
Note I’m not necessarily being creepy here. You definitely want to know how people interact with your product and your site, and it doesn’t need to be personal information you’re collecting about your users. It could be kept aggregate. You could find out that 45% of people leave your site when you ask them for their phone number, and then you might decide it’s not worth it to do that.
Speaking of creepy, another critical thing to consider during your data audit is privacy controls and encryption methods. Are you saving data legally? Are you protecting it legally? Are you informing your users appropriately about how and what data will be stored? Are you planning to remain consistent with your stated privacy policy? Do you respect people’s “Do Not Track” option?
At the end of a data audit, you might still have a vague idea of what exactly you can do with your data, but you should have a bunch of possible ideas, as well as guesses at what kind of attributes would contribute to the kind of behavior you’re considering tracking.
Then, after you start collecting high-quality data and figuring out the basic questions you care about, you will probably have to wait a few weeks or months to start training and implementing your models. This is a good time to make sure your data infrastructure is in place and doesn’t have major bugs.
Data Strategy
Ok, now you’ve collected lots of data and you also have a bunch of questions you think may be answerable. It’s time to prioritize your questions and form a plan. For each question on your list, you’ll need to think about the following issues:
- Is it a monitor or an algorithm?
- Is it short-term, one-time analysis or should you set it up as a dashboard?
- How much data will you need to train the model?
- What is your expectation of the signal in the data you’re collecting?
- How useful will the results of the model be considering the range of signal and the quality of the answer?
- Do you need to go find proxy data? Should you start now?
- Which algorithms should you consider?
- What’s your evaluation method?
- Is it scalable?
- Can you do a baby version first or does it only make sense to go deep?
- Can you do a simpler version of it that’s much cheaper to build?
- How long will it probably take to train?
- How fast can it update?
- Will it be a pain to integrate it to the realtime system?
- What are the costs if it doesn’t work?
- What are the costs of not trying it? What else could you be doing with that time?
- How is the feedback loop expected to work?
- What is the impact of this model on the users?
- What is the impact of this model on the world at large? This is especially important if you’re creepy. Don’t be creepy.
Also, you need a team to build your models. How do you hire? Who do you hire? Some of these answers depend on your above plan. If there’s a lot of realtime updating for your models you’ll need more data engineers and fewer pure modelers. If you need excellent-looking results from your work you’ll need more data viz nerds.
You should consider hiring a consultant just to interview for you. It’s really hard to interview for data scientists if nobody is an expert in data science, and you might end up with someone who knows how to sounds smart but can’t build anything. Or you could end up with someone who can build anything but has no idea what their choices really mean.
The ultimate goal at the end of a data audit and strategy is to end up with a reasonable expectation of what having a data science team will accomplish, how long it will take, how deep an investment it is, and how to do it.
“The problem here is not the message. The problem is the messenger.”
Today’s post is basically going to consist of me wishing I’d written this Gawker piece which was actually written by Hamilton Nolan and was entitled “It Would Be Great if Millionaires Would Not Lecture Us on ‘Living With Less’”.
To enjoy it as much as I did, you’d have to read this New York Times Opinion piece first, in which Graham Hill, who made a bajillion dollars in the dot com era, realizes he had too much stuff and now has less stuff and is telling us how great it is. Most cloying line: “the things I consumed ended up consuming me.”
At the risk of quoting Nolan’s entire article (the title of my post is his), let me start you with this:
There is something about achieving great financial success that seduces people into believing that they are life coaches. This problem seems particularly endemic to the tech millionaire set. You are not simply Some Fucking Guy Who Sold Your Internet Company For a Lot of Money; you are a lifestyle guru, with many important and penetrating insight about How to Live that must be shared with the common people.
We would humbly request that this stop.
I’ll skip over some parts and get to where he talks about Amanda Palmer:
The problem here is not the message. The problem is the messenger. More specifically, it is the messenger using his own life as supporting evidence for the message. Were Graham Hill to simply write a fact-based essay arguing that Americans should cut down on material possessions in order to save the environment and gain peace of mind, he would doubtless hear a chorus of support. But for Graham Hill, a young millionaire who was fortunate enough to sell his “pre-Netscape browser” at the high point of the internet bubble, to say to the average American, “My journey through the perils of great wealth has bestowed me with wisdom that is directly applicable to you” is simply false. It is no wonder that Hill loved the recent TED talk by millionaire musician Amanda Palmer, in which she argued that it was perfectly fair for her to, for example, accept a free night of lodging in the home of poor Honduran immigrants and not pay them for it, because the beauty of her music is payment enough. Both are insulated enough from the realities of personal finance to forget about them entirely.
True! And I’d add more in the Amanda Palmer case. She and I went to the same high school and I have known her since she was in 7th grade.
I’ll tell you what. She’s not your average artist. She’s hugely exhibitionist. This has worked great for her, but is not a typical artistic personality. In fact she’s essentially a cult leader. So yes, when you’re an artist/ cult leader, it makes sense to “let your fans pay you”. But if you’re a typical starving, introverted, sensitive soul, then not so much. How can she speak for all artists and ask them to do stuff just like her? Or rather, why does she think it would scale?
Mind you, I’m guilty of this problem too. When I give advice, which I do all the time, I pretty much always tell people what works for me. But my evidence that the same approach would work for them is slight.
That begs the question, how do we do better than this? How do we tailor our advice to make it useful?
I kind of hate TED talks
The good
There are good things about TED talks. It’s nice to have a thoughtful articulate person saying something a little bit new and a little bit different. OK I’m done.
The annoying
Then there are annoying things about TED talks. People are so ridiculously polished. No idea is that perfect! Rumor has it that, after getting professionally trained for their TED performances, the producers then remove all the “umms” and awkward silences to make it even more perfect. Yuck.
Here’s one way to think about it: TED talks aren’t as good as blogs because they’re not interactive – the audience is expected to receive and not talk back. That’s why I prefer to blog in my underwear and bathrobe, imagining my friends on their living room sofas, also wearing pajamas, and objecting to my stupidity. And that’s why I like the feedback and the comments. It makes my ideas better.
At the same time, TED talks are not as deep as books, where you have enough time and space to actually think through an argument. How could you really develop a deep thought in 20 minutes? You just can’t.
Instead, you have a manipulation of the past which often result in simulated emotional responses, much like how the soundtrack to Amy Tan’s “The Joy Luck Club” makes me cry every time I hear it, no matter what emotional state I’m actually in.
The essence of what’s annoying about TED talks is perfectly parodied by Onion Talks, especially this one:
The evil
But what I really hate about TED talks is the curating of ideas that it represents. I realize that any gatekeeper will do this, but I’m particularly concerned about the TED byline, “Ideas Worth Spreading”. According to whom?
Who gets invited to those things? Whose ideas are interesting but non-threatening enough for the TED audience?
And how often do other, rawer ideas get ignored? How appealing do I have to make my idea to rich people in order to be an insider in this mini self-congratulatory universe?
Here’s an example of what I’m talking about written by a woman who was uninvited to give a TED talk under suspicious circumstances (with a follow-up here). Granted, it’s a TEDx situation, but it’s the same problem. The paragraph I worry about most:
Looking back, I must admit that upon learning of this invitation some of my colleagues and I questioned TEDx Manhattan’s commitment to serving as a platform for looking at our food system from a non-privileged perspective. Changing the Way We Eat is not a venue for the common person. The website makes no mention of available scholarships to enable low-income people or students to attend the pricey one day conference. Not only must attendees pay $135 for the privilege of sitting and listening, they also have to apply, explaining why they deserve to be part of the audience and then hope to be selected! Unless the Glynwood Institute does real serious targeted outreach to communities of color (which I haven’t seen and was the primary purpose of my screening party), their set up is going to result in the exclusion of low-income and people of color, regardless of whether it is intentional. I received feedback from a past attendee that presenters referenced poor people and people of color only as being the recipients of charity or service. I think Changing the Way We Eat needed to hear my voice in order to change the way the mainstream food movement thinks about poverty, food access, hunger, and food system change.
Black Scholes and the normal distribution
There have been lots of comments and confusion, especially in this post, over what people in finance do or do not assume about how the markets work. I wanted to dispel some myths (at the risk of creating more).
First, there’s a big difference between quantitative trading and quantitative risk. And there may be a bunch of other categories that also exist, but I’ve only worked in those two arenas.
Markets are not efficient
In quantitative trading, nobody really thinks that “markets are efficient.” That’s kind of ridiculous, since then what would be the point of trying to make money through trading? We essentially make money because they aren’t. But of course that’s not to say they are entirely inefficient. Some approaches to removing inefficiency, and some markets, are easier than others. There can be entire markets that are so old and well-combed-over that the inefficiencies (that people have thought of) have been more or less removed and so, to make money, you have to be more thoughtful. A better way to say this is that the inefficiencies that are left are smaller than the transaction costs that would be required to remove them.
It’s not clear where “removing inefficiency” ends and where a different kind of trading begins, by the way. In some sense all algorithmic trades that work for any amount of time can be thought of as removing inefficiency, but then it becomes a useless concept.
Also, you can see from the above that traders have a vested interest to introduce new kinds of markets to the system, because new markets have new inefficiencies that can be picked off.
This kind of trading is very specific to a certain kind of time horizon as well. Traders and their algorithms typically want to make money in the average year. If there’s an inefficiency with a time horizon of 30 years it may still exist but few people are patient enough for it (I should add that we also probably don’t have good enough evidence that they’d work, considering how quickly the markets change). Indeed the average quant shop is going in the opposite direction, of high speed trading, for that very reason, to find the time horizon at which there are still obvious inefficiencies.
Black-Scholes
A long long time ago, before Black Monday in 1987, people didn’t know how to price options. Then Black-Scholes came out and traders started using the Black-Scholes (BS) formula and it worked pretty well, until Black Monday came along and people suddenly realized the assumptions in BS were ridiculous. Ever since then people have adjusted the BS formula. Everyone.
There are lots of ways to think about how to adjust the formula, but a very common one is through the volatility smile. This allows us to remove the BS assumption of constant volatility (of the underlying stock) and replace it with whatever inferred volatility is actually traded on in the market for that strike price and that maturity. As this commenter mentioned, the BS formula is still used here as a convenient reference to do this calculation. If you extend your consideration to any maturity and any strike price (for the same underlying stock or thingy) then you get a volatility surface by the same reasoning.
Two things to mention. First, you can think of the volatility smile/ surface as adjusting the assumption of constant volatility, but you can also ascribe to it an adjustment of the assumption of a normal distribution of the underlying stock. There’s really no way to extricate those two assumptions, but you can convince yourself of this by a thought experiment: if the volatility stays fixed but the presumed shape of the distribution of the stocks gets fatter-tailed, for example, then option prices (for options that are far from the current price) will change, which will in turn change the implied volatility according to the market (i.e. the smile will deepen). In other words, the smile adjusts for more than one assumption.
The other thing to mention: although we’ve done a relatively good job adjusting to market reality when pricing an option, when we apply our current risk measures like Value-at-Risk (VaR) to options, we still assume a normal distribution of risk factors (one of the risk factors, if we were pricing options, would be the implied volatility). So in other words, we might have a pretty good view of current prices, but it’s not at all clear we know how to make reasonable scenarios of future pricing shifts.
Ultimately, this assumption of normal distributions of risk factors in calculating VaR is actually pretty important in terms of our view of systemic risks. We do it out of computational convenience, by the way. That and because when we use fatter-tailed assumptions, people don’t like the answer.
Team Turnstile: how do NYC neighborhoods recover from extreme weather events?
I wanted to give you the low-down on a data hackathon I participated in this weekend, which was sponsored by the NYU Institute for Public Knowledge on the topic of climate change and social information. We were assigned teams and given a very broad mandate. We had only 24 hours to do the work, so it had to be simple.
Our team consisted of Venky Kannan, Tom Levine, Eric Schles, Aaron Schumacher, Laura Noren, Stephen Fybish, and me.
We decided to think about the effects of super storms on different neighborhoods. In particular, to measure the recovery time of the subway ridership in various neighborhoods using census information. Our project was inspired by this “nofarehikes” map of New York which tries to measure the impact of a fare hike on the different parts of New York. Here’s a copy of our final slides.
Also, it’s not directly related to climate change, but rather rests on the assumption that with climate change comes more frequent extreme weather events, which seems to be an existing myth (please tell me if the evidence is or isn’t there for that myth).
We used three data sets: subway ridership by turnstile, which only exists since May 2010, the census of 2010 (which is kind of out of date but things don’t change that quickly) and daily weather observations from NOAA.
Using the weather map and relying on some formal definitions while making up some others, we came up with a timeline of extreme weather events:
Then we looked at subway daily ridership to see the effect of the storms or the recovery from the storms:
We broke it down to individual stations. Here’s a closeup around Sandy:
Then we used the census tracts to understand wealth in New York:
And of course we had to know which subway stations were in which census tracts. This isn’t perfect because we didn’t have time to assign “empty” census tracts to some nearby subway station. There are on the order of 2,000 census tracts but only on the order of 800 subway stations. But again, 24 hours isn’t alot of time, even to build clustering algorithms.
Finally, we attempted to put the data together to measure which neighborhoods have longer-than-expected recovery times after extreme weather events. This is our picture:
Interestingly, it looks like the neighborhoods of Manhattan are most impacted by severe weather events, which is not in line with our prior [Update: I don’t think we actually computed the impact on a given resident, but rather just the overall change in rate of ridership versus normal. An impact analysis would take into account the relative wealth of the neighborhoods and would probably look very different].
There are tons of caveats, I’ll mention only a few here:
- We didn’t have time to measure the extent to which the recovery time took longer because the subway stopped versus other reasons people might not sure the subway. But our data is good enough to do this.
- Our data might have been overwhelmingly biased by Sandy. We’d really like to do this with much longer-term data, but the granular subway ridership data has not been available for long. But the good news is we can do this from now on.
- We didn’t have bus data at the same level, which is a huge part of whether someone can get to work, especially in the outer boroughs. This would have been great and would have given us a clearer picture.
- When someone can’t get to work, do they take a car service? How much does that cost? We’d love to have gotten our hands on the alternative ways people got to work and how that would impact them.
- In general we’d have like to measure the impact relative to their median salary.
- We would also have loved to have measured the extent to which each neighborhood consisted of salary versus hourly wage earners to further understand how a loss of transportation would translate into an impact on income.
Modeling fraud in the financial system
Today we have a guest post by Dan Tedder. Actually it’s a letter he sent me after listening to my EconTalk podcast with Russ Roberts which he kindly agreed to let me post. Dan’s bio is below the letter.
I think this letter is profound (although I don’t completely agree about the Markov stuff), because it points out something that I see as a commonly held blindspot by people who think about regulation and modeling. Namely, that any systemic risk model of the financial system that doesn’t take account of lying isn’t worth the memory it takes up on a computer.
That brings us to the following question: can we incorporate lies into models? Can we anticipate and model fraud itself, in addition to the underlying system? Or do we give up on models and rely on skeptical people to ferret out lies? Or possibly some hybrid?
——
Hi Cathy,
I really liked your interview, and I think you are right on in pointing to a lack of ethics. I would say further that what we need is rigorous honesty in all aspects of the financial system. I agree with your objections to conflicts of interest. Allowing such conflicts to exist demonstrates a lack of rigorous honesty on the part of the participants. In my opinion a lot of bankers and folks on Wall Street should be headed to jail. The inability of the SEC to file charges and prosecute them further demonstrates the lack of honesty and character in the financial system and the government. So why am I telling you things you already know?
My father was a successful businessman. Years ago I was invited to invest in an ice cream franchise by another faculty member. I spent several days developing models using Excel. Finally, I decided to talk to my father. I called him and he immediately asked me to tell him about the present owners and their accounting. I told him the husband was in jail and accounting was five years behind. Further, his wife was probably taking money out of the till.
He stopped me right there, and pointed out that I needed to look no further. The present owners were not honest and therefore the opportunity was too risky. No telling what liabilities they had incurred and passed on to the franchise. I felt like an idiot. My modeling was a total waste of time because it assumed the present owners were honest. In fact, they were dishonest and no defensible model could be constructed based upon their accounting or lack thereof.
I think the complexity of our present financial problems will largely disappear if we try to focus more on the obvious. First, it is obvious that bankers, accountants, modelers, and other participants must be rigorously honest. Second, George Box, a statistician at the University of Wisconsin, studied the stock market and found through time series analysis that stock market prices are Markov processes. So in modeling stock prices we need only worry about today and tomorrow. The best indicator of tomorrow’s price is today’s price. The best indicator of what will happen tomorrow is where we are today, and probably our models of the larger process should also be Markovian. Third, apply the KISS method, “Keep it simple, stupid.” Instead of worrying about the mathematical model, worry about the honesty of the participants. The financial system cannot tolerate dishonesty. Making sure the bankers are honest will go a long way toward balancing the books.
Regards, Dan
——
Daniel William Tedder is Associate Professor Emeritus, School of Chemical and Biomolecular Engineering, and Adjunct Professor, School of Mechanical Engineering, both at the Georgia Institute of Technology. He attended Kenyon College and received a Bachelor’s in Chemical Engineering at the Georgia Institute of Technology. He obtained MS and PhD degrees in Chemical Engineering at the University of Wisconsin, Madison. He was a staff engineer in the Chemical Technology Division of the Oak Ridge National Laboratory before joining the faculty at Georgia Tech. He served as an independent technical reviewer at the Nuclear Regulatory Commission after retiring from Georgia Tech. He has numerous publications, has edited 11 books, and has authored one book, Preliminary Chemical Process Design and Economics, which is available from Amazon. He is an expert in chemical separations and in actinide partitioning, an advanced method for radioactive waste management.
Aunt Pythia’s advice
You’ve stumbled upon yet another week’s worth of worthy questions that will be awkwardly sidestepped by mathbabe’s alter ego Aunt Pythia.
By the way, if you don’t know what you’re in for, go here for past advice columns and here for an explanation of the name Pythia. Most importantly,
Please submit your question at the bottom of this column!
I’ve officially run out of questions so this is for real.
Please come up with something before I do.
——
Dear Aunt Pythia,
I just moved to NYC from a small university town, and I’m finding it much harder to meet nerd girls. Most of the nerd hangout spots that I’ve found are male dominated, and I meet mostly artists at the bars and coffee shops. Do you have any suggestions beyond trolling the nearest physics department?
Nice, Easygoing Roamer Drawn Swiftly Around Real, Engaging Hackers On Town
Dear NERDSAREHOT,
Let me suggest you enroll in Meetup yesterday and sign yourself up for all the nerd meetups you can find. There are plenty of cute nerd girls who go to those, and it’s a perfect situation for you to ask someone to have a beer afterwards. Also consider getting involved in weekend hackathons, which attract lots of nerd girls as well.
By the way, these events are still male dominated, but that’s a good thing. Nerd girls should have their pick. It’s one of the many advantages of being a nerd girl and it aint going away.
Aunt Pythia
——
Dear Aunt Pythia,
I recently got a job as a data scientist, and I’m feeling like my stats skills are woefully inadequate. I have a master’s in pure math and I work as a programmer, but I’ve never taken a statistics class. What books would you recommend I read to get up to speed on statistics? I’m looking for something with examples that’s applicable to my work (not too much definition/theorem/proof), but that isn’t scared of the math.
Regretting Spurning Statistics
Dear RSS,
Congratulations! Can you write back and tell everyone how you got the job? Guest post?
Honestly I learned stats (the stuff I know anyway) by reading wikipedia extensively. It’s surprisingly good. Also, the book I’m writing with Rachel Schutt will contain some good explanations of how stats is used in data science, thanks of course to Rachel, not me. She’s working on the causality chapter right now.
In general my advice to you is, draw lots of pictures, including a histogram as well as a time-value scatter plot of every data set you use, and every data set you generate as well. You’d be surprised by how quickly you learn the statistics that is relevant to your dataset when you’re intimately familiar with its properties.
Good luck!
Aunt Pythia
——
Dear Aunt Pythia,
I have been reading up on regression to the mean originally as described by Galton. He notes that the sons’ height data had reduced variance versus the height data of the preceding fathers’ generation. If this is so, wouldn’t the grandsons’ generation have even more reduced variance in height compared with the 2nd generations’ height…and so on down the generation lineage. Therefore wouldn’t the variance in succeeding generations get narrower and narrower and approach some limit? Where am I going wrong with this, or am I misunderstanding something?
MeanIQ
Dear MeanIQ,
Thanks for bringing my attention to this, it’s clearly an important historical part of linear regression and I’d never heard of it.
You’re absolutely right to think that Galton was wrong. Galton’s working theory was that two people have children by averaging their characteristics, which is just not how genetics works (as we now know). Not only would what you say be true, that after a few generations everyone would be the exact same height, but we’d also see that, if you went backwards in time, there’d be people of arbitrary height, tall and short.
As for why he saw larger variance in older generations, my best guess is that he had a selection bias. Maybe the decreasing variance he observed was due to environmental factors such as the quality and size of the local food supply, where the “current” generation were localized (and so more consistent) but the “older” generation had come from various other places where they were either better fed or less well fed, which would lead to an increased variance.
There’s another totally different interpretation for the phrase “regression to the mean” which is also confusing though. Namely, the idea that if your first measurement of something is extreme, then your second measurement will tend to be less so. The problem with this is that you have to have a notion of “extreme” in the first place. And if you do, then it’s kind of obvious (and also kind of dumb).
Aunt Pythia
——
Dear Aunt Pythia,
Is the Mathbabe religious?
I really like the new mathbabe logo/marque. The typeface is totally flapper and I really like those bulbous upside down B’s, and the offsetting of the bottom text in order to give the text texture. But when I look at the symbolology of the whole logo/marque I can’t help but wonder if the Mathbabe is religious. The T looks like a deproportioned Greek cross, and the alpha above it suggests that there should be an omega below it somewhere. So clearly the new logo/marque has some Christian symbolology, and my eyes keep looking for more. Maybe the A’s are three sided figures that represent the Trinity, and the M represents a firmament that has fallen, and therefore symbolologizes our fallen state.
Anyway, it’s cool if you are religious, as lots of great mathematicians were devout people, and some were even priests, like Bayes. And if you’re not that’s cool too. I see you describe sex both profanely and sacredly, so I know you are a spiritual person. And it’s cool if you don’t want to answer either. I respect that religion is a personal matter. Just saw your new logo/marque and was wondering.
Semi-semiotic
Dear Semi-semiotic,
Honestly I have so little religious background that I am not even sure if you’re kidding (but the “symbolologizes” kind of gives you away).
For the record, my parents were atheists who made fun of me when I told them I believed in God in first grade (I think I learned about the idea of God from a babysitter). One of their favorite stories of my childhood is when my first grade teacher, a devout Catholic, called up my parents in alarm over my essay which said “I believe in God but please don’t tell my parents” and my mom was like, “Har har that’s a good one, thanks” and hung up on her. Not that my mom is a rude person, she isn’t.
Two more points: First, I plan to refer to myself in third person from now on as “The Mathbabe”, and second, when did I ever refer to sex sacredly? That’s bullshit. Blasphemy even.
Aunt Pythia
——
Please please please submit questions, thanks! I’m desperate!
Unintended Consequences of Journal Ranking
I just read this paper, written by Björn Brembs and Marcus Munafò and entitled “Deep Impact: Unintended consequences of journal rank”. It was recently posted on the Computer Science arXiv (h/t Jordan Ellenberg).
I’ll give you a rundown on what it says, but first I want to applaud the fact that it was written in the first place. We need more studies like this, which examine the feedback loop of modeling at a societal level. Indeed this should be an emerging scientific or statistical field of study in its own right, considering how many models are being set up and deployed on the general public.
Here’s the abstract:
Much has been said about the increasing bureaucracy in science, stifling innovation, hampering the creativity of researchers and incentivizing misconduct, even outright fraud. Many anecdotes have been recounted, observations described and conclusions drawn about the negative impact of impact assessment on scientists and science. However, few of these accounts have drawn their conclusions from data, and those that have typically relied on a few studies. In this review, we present the most recent and pertinent data on the consequences that our current scholarly communication system has had on various measures of scientific quality (such as utility/citations, methodological soundness, expert ratings and retractions). These data confirm previous suspicions: using journal rank as an assessment tool is bad scientific practice. Moreover, the data lead us to argue that any journal rank (not only the currently-favored Impact Factor) would have this negative impact. Therefore, we suggest that abandoning journals altogether, in favor of a library-based scholarly communication system, will ultimately be necessary. This new system will use modern information technology to vastly improve the filter, sort and discovery function of the current journal system.
The key points in the paper are as follows:
- There’s a growing importance of science and trust in science
- There’s also a growing rate (x20 from 2000 to 2010) of retractions, with scientific misconduct cases growing even faster to become the majority of retractions (to an overall rate of 0.02% of published papers)
- There’s a larger and growing “publication bias” problem – in other words, an increasing unreliability of published findings
- One problem: initial “strong effects” get published in high-ranking journal, but subsequent “weak results” (which are probably more reasonable) are published in low-ranking journals
- The formal “Impact Factor” (IF) metric for rank is highly correlated to “journal rank”, defined below.
- There’s a higher incidence of retraction in high-ranking (measured through “high IF”) journals.
- “A meta-analysis of genetic association studies provides evidence that the extent to which a study over-estimates the likely true effect size is positively correlated with the IF of the journal in which it is published”
- Can the higher retraction error in high-rank journal be explained by higher visibility of those journals? They think not. Journal rank is bad predictor for future citations for example. [mathbabe inserts her opinion: this part needs more argument.]
- “…only the most highly selective journals such as Nature and Science come out ahead over unselective preprint repositories such as ArXiv and RePEc”
- Are there other measures of excellence that would correlate with IF? Methodological soundness? Reproducibility? No: “In fact, the level of reproducibility was so low that no relationship between journal rank and reproducibility could be detected.
- More about Impact Factor: The IF is a metric for the number of citations to articles in a journal (the numerator), normalized by the number of articles in that journal (the denominator). Sounds good! But:
- For a given journal, IF is not calculated but is negotiated – the publisher can (and does) exclude certain articles (but not citations). Even retroactively!
- The IF is also not reproducible – errors are found and left unexplained.
- Finally, IF is likely skewed by the fat-tailedness of citations (certain articles get lots, most get few). Wouldn’t a more robust measure be given by the median?
Conclusion
- Journal rank is a weak to moderate predictor of scientific impact
- Journal rank is a moderate to strong predictor of both intentional and unintentional scientific unreliability
- Journal rank is expensive, delays science and frustrates researchers
- Journal rank as established by IF violates even the most basic scientific standards, but predicts subjective judgments of journal quality
Long-term Consequences
- “IF generates an illusion of exclusivity and prestige based on an assumption that it will predict subsequent impact, which is not supported by empirical data.”
- “Systemic pressures on the author, rather than increased scrutiny on the part of the reader, inflate the unreliability of much scientific research. Without reform of our publication system, the incentives associated with increased pressure to publish in high-ranking journals will continue to encourage scientiststo be less cautious in their conclusions (or worse), in an attempt to market their research to the top journals.”
- “It is conceivable that, for the last few decades, research institutions world-wide may have been hiring and promoting scientists who excel at marketing their work to top journals, but who are not necessarily equally good at conducting their research. Conversely, these institutions may have purged excellent scientists from their ranks, whose marketing skills did not meet institutional requirements. If this interpretation of the data is correct, we now have a generation of excellent marketers (possibly, but not necessarily also excellent scientists) as the leading figures of the scientific enterprise, constituting another potentially major contributing factor to the rise in retractions. This generation is now in charge of training the next generation of scientists, with all the foreseeable consequences for the reliability of scientific publications in the future.
The authors suggest that we need a new kind of publishing platform. I wonder what they’d think of the Episciences Project.
Poseurs should not own the backlash against data science poseurs
I’ve noticed a recent trend in coverage of data science. Namely, there’s backlash against the hype and the over-promising, intentional or not, of data science and data scientists. People are beginning to develop smell tests for big data and raise incredulous eyebrows at certain claims.
This is a good thing. We data scientists should welcome the backlash, first because it’s inevitable, and second because it allows us to have a much-needed conversation about how to behave and what is reasonable to claim or even hope for with respect to big data. There is a poseur problem in big data, after all.
But, fellow data nerds, let’s take this as a cue to start an internal discussion about data science skepticism. Let’s make sure that it’s coming from our community, or at least the surrounding technical community, rather than from yet another set of poseurs who don’t actually know what data is and would only serve to lampoon and discredit our emerging field rather than improve it. We should be the ones leading the charge and admitting when we’re full of shit. We need to own the backlash.
Let me give you an example. A serious data scientist friend of mine recently got asked to be interviewed as part of a conversation on data science skepticism. After thinking hard about what her contribution could be, she wrote back to accept the offer, but was then told she was “off the hook” because they’d found someone else who was “perfect for the assignment.” It turned out to be a journalist who had previously interviewed her. That was his credential for this conversation.
But how can you actually have informed skepticism if you are not yourself an expert?
Another example. David Brooks recently wrote a column wherein he declared himself a data science skeptic and then followed that up by referring to no fewer than eight random statistical studies that made no coherent sense and had no overall point. My conclusion: this is the wrong man to lead the charge against poseurs in data science.
If we are going to rebel against big data soundbites, let’s not do it in soundbites. Instead, let’s talk to people on the inside, who see specific problems in the field and are willing to talk openly about them.
I liked the recent Strata talk by Kate Crawford entitled “Untangling Algorithmic Illusions from Reality in Big Data” (h/t Alan Fekete) which discusses bias in data using very concrete examples, and asks us to examine the objectivity of our “facts”.
For example, she talked about a smart phone app that finds potholes in Boston and report them to the City, and how on the one hand it was cool but on the other it would mean that, if naively applied, richer neighborhoods like Lincoln would get better services than Roxbury. She explained an important point: data analysis is not objective, which most people know. But often the data itself is not either – it was collected in a certain way with particular selection biases.
We need more conversations like this or else we will be leaving a hole which will be filled with loud, uninformed skeptics who would be right to raise the alarm.
One last thing. I’m aware that tons of people, especially serious academic statisticians and computer scientists, criticize data scientists for a totally different reason, namely that we are overly self-promoting (although academics have their own status plays).
But I don’t apologize for that. The truth is, a data scientist is a hybrid between a business person and a researcher. And this is a good thing, not a bad thing: it means the world gets direct access to the modeler, and can challenge any hyperbolic claims by asking for details, rather than having to go through a marketing person who acts (usually quite poorly) as a nerd interpreter. I for one would rather represent my work directly to the world (and be called a self-promoter) then to be kept in the back room.
WTF happened to feminism?!
I usually don’t talk about feminism per se, because honestly I usually don’t think about it. Thanks to role models like my mom, who was an MIT co-ed in the ’60’s and an original nerd, helping develop the internet at Bolt Beranek and Newman and teaching computer science at UMass Boston, I’ve never for one second doubted my personal right to be a thoughtful, argumentative, and ambitious woman. I learned from my mom, and from other trailblazers, that I can pursue my personal interests and trust that the world will welcome my contributions.
Two events in the past week have made me think about how confusing this message has become for today’s growing girls, however.
First, the Sheryl Sandberg thing. To be honest, I haven’t read the book. But I have read this Washington Post article describing the book, and here’s my take on it: a corporate branding campaign loosely tied to women, but mostly pushing forward the agenda of how to be a company drone. From the article:
Sandberg’s understanding of leadership so perfectly internalizes the power structures of institutions created and dominated by men that it cannot conceive of women’s leadership outside of those narrow spaces. Does this also explain why, for Sandberg, the biggest threat to our ability to occupy a position of leadership is a woman’s desire to have a child? This is what men have been telling us for years.
Sandberg may miss so many women in her movement simply because her brand of gender equity is almost entirely privatized, doled out from employer to employee. Women, she advises, will find their way to the top through telling employers upfront about their childbearing plans, through learning how to negotiate pay raises (say “we” instead of “I,” Sandberg cautions, though the collective here is the corporation), through comportment exercises, as taught through Lean In’s web videos.
Like I said in this post, wouldn’t an actual feminist agenda include saying “The hell with this!” to a corporation that is so stifling that all our imaginations could bring us is better maternity leave negotiation tactics with the Borg? Resistance is futile, man!
Here’s the second thing that pissed me off this week. Harvard MBA Rachel Greenwald tells women what makes men not call back after a date.
Answer? As it turns out, anything where you have an opinion and they feel intimidated by you. Solution? Dumb it down, sex it up, and act like a toy. That way, in her words, you’ll be empowered, because they’re all calling you back, and the choice is yours. The choice, I’d add, from a long list of wimps. No thank you.
The video:
Can we do better than this, people??
A blogging parliament
Last night I found myself watching Steve Waldman’s talk at the 2011 Economic Bloggers Forum at the Kaufman Foundation. I’m a big fan of Waldman’s blog Interfluidity. His talk was interesting and thought-provoking, like his writing. I suggest you watch it.
After expressing outrage at the failure of control systems and the political system after the financial crisis, Waldman asks the question, why are we where we are? His answer: there’s a monopoly of power in this country even as information itself is increasingly available. The monopoly of power is extremely correlated, of course, to the rising wealth inequality, beautifully visualized in this recent video (h/t Leon Kautsky, Debika Shome) by Politizane.
The solution, he hopes, may include the blogosphere (although it’s not a perfect place either, with its own revolving doors, weird incentives and possibly conflicts of interest). The work of bloggers is valuable social capital, Steve argues, so how do we deploy it?
Steve introduced the concept of policy entrepreneurs, which have three characteristics:
- They are sources of information in the form policy ideas. They possible even write laws.
- They have some kind of certification in order to cover the policy maker’s ass.
- They exert some kind of influence on policy makers, to create incentives for their policy goals.
In other words, a policy entrepreneur is someone in the business of shaping policy makers’ agendas.
If you stop there, you might think “lobbyist,” and you’d be right. But the problem with our current lobbyist system is not the above three characteristics, but rather that it’s a such a closed system. In other words, you essentially need to be rich to be an influential lobbyist (or at least, as an influential lobbyist, you are backed by enormous wealth), but then that increases the monopolistic nature of political power. It doesn’t solve our “monopoly of power” problem.
The question becomes, is there a way for normal people, or groups of people, to be policy entrepreneurs?
One possible solution, Waldman suggests, is to from a parliament of bloggers. Since groups are taken seriously, can bloggers form official groups in which they gain consensus around a topic and issue policy?
An intriguing idea, and I like it because it’s not really abstract: if bloggers decided to try this, they could literally just form a group, call ourselves a name, and start issuing policy proposals. Of course they’d probably not get anywhere unless we had influence or leverage.
Does something like this already exist? The closest thing I can think of is the hacker group Anonymous – although they might not be bloggers, they might be. They’re anonymous. I’m going to guess they are active on the web even if they don’t specifically blog. In any case, let’s see if they qualify as policy entrepreneurs in the above sense.
- They don’t issue specific policy proposals, but they certainly object clearly to policies they don’t like.
- Their credentials lie in their unparalleled ability to take control of information systems.
- Likewise, their leverage is fierce in this domain.
In all, I don’t think Anonymous fits the bill – they’re too devoted to anarchy to deliver policy in the sense that Waldman suggests, and their tools are too crude to make fine points. This might have to do with the nature of hackers in general (keeping in mind that Anonymous stand for something far more extreme than the average hacker), which I read about in an essay by Paul Graham yesterday (h/t Chris Wiggins):
Here’s another problem: aren’t bloggers in general kind of their own 1%? Is policy via a “parliament of bloggers” not enough of an improvement to the current system of insiders?
What about if Occupy got into the idea of being a vehicle of policy entrepreneurship? Even though we tend not to support specific political candidates in Occupy, we do consistently think about policy and decide whether to endorse a given bill or policy proposal. Could we, instead of commenting on existing policy, start thinking about proposing new policy, even to the point of writing new laws?
On the one hand such work requires enormously long discussions and difficult-to-obtain consensus, but on the other hand we have the knowledge, the abilities, and the moral persuasion. Do we have the influence? And would Occupiers think exerting influence on policy in the current corrupt system tantamount to selling out?
mathbabe 