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How is math used outside academia?
Help me out, beloved readers. Brainstorm with me.
I’m giving two talks this semester on how math is used outside academia, for math audiences. One is going to be at the AGNES conference and another will be a math colloquium at Stonybrook.
I want to give actual examples, with fully defined models, where I can explain the data, the purported goal, the underlying assumptions, the actual outputs, the political context, and the reach of each model.
The cool thing about these talks is I don’t need to dumb down the math at all, obviously, so I can be quite detailed in certain respects, but I don’t want to assume my audience knows the context at all, especially the politics of the situation.
So far I have examples from finance, internet advertising, and educational testing. Please tell me if you have some more great examples, I want this talk to be awesome.
The ultimate goal of this project is probably an up-to-date essay, modeled after this one, which you should read. Published in the Notices of the AMS in January 2003, author Mary Poovey explains how mathematical models are used and abused in finance and accounting, how Enron booked future profits as current earnings and how they manipulated the energy market. From the essay:
Thus far the role that mathematics has played in these financial instruments has been as much inspirational as practical: people tend to believe that numbers embody objectivity even when they do not see (or understand) the calculations by which particular numbers are generated. In my final example, mathematical principles are still invisible to the vast majority of investors, but mathematical equations become the prime movers of value. The belief that makes it possible for mathematics to generate value is not simply that numbers are objective but that the market actually obeys mathematical rules. The instruments that embody this belief are futures options or, in their most arcane form, derivatives.
Slightly further on she explains:
In 1973 two economists produced a set of equations, the Black-Scholes equations, that provided the first strictly quantitative instrument for calculating the prices of options in which the determining variable is the volatility of the underlying asset. These equations enabled analysts to standardize the pricing of derivatives in exclusively quantitative terms. From this point it was no longer necessary for traders to evaluate individual stocks by predicting the probable rates of profit, estimating public demand for a particular commodity, or subjectively getting a feel for the market. Instead, a futures trader could engage in trades driven purely by mathematical equations and selected by a software program.
She ends with a bunch of great questions. Mind you, this was in 2003, before the credit crisis:
But what if markets are too complex for mathematical models? What if irrational and completely unprecedented events do occur, and when they do—as we know they do—what if they affect markets in ways that no mathematical model can predict? What if the regularity that all mathematical models assume effaces social and cultural variables that are not subject to mathematical analysis? Or what if the mathematical models traders use to price futures actually influence the future in ways the models cannot predict and the analysts cannot govern? Perhaps these are the only questions that can challenge the financial axis of power, which otherwise threatens to remake everything, including value, over in the image of its own abstractions. Perhaps these are the kinds of questions that mathematicians and humanists, working together, should ask and try to answer.
Columbia data science course, week 1: what is data science?
I’m attending Rachel Schutt’s Columbia University Data Science course on Wednesdays this semester and I’m planning to blog the class. Here’s what happened yesterday at the first meeting.
Syllabus
Rachel started by going through the syllabus. Here were her main points:
- The prerequisites for this class are: linear algebra, basic statistics, and some programming.
- The goals of this class are: to learn what data scientists do. and to learn to do some of those things.
- Rachel will teach for a couple weeks, then we will have guest lectures.
- The profiles of those speakers vary considerably, as do their backgrounds. Yet they are all data scientists.
- We will be resourceful with readings: part of being a data scientist is realizing lots of stuff isn’t written down yet.
- There will be 6-10 homework assignments, due every two weeks or so.
- The final project will be an internal Kaggle competition. This will be a team project.
- There will also be an in-class final.
- We’ll use R and python, mostly R. The support will be mainly for R. Download RStudio.
- If you’re only interested in learning hadoop and working with huge data, take Bill Howe’s Coursera course. We will get to big data, but not til the last part of the course.
The current landscape of data science
So, what is data science? Is data science new? Is it real? What is it?
This is an ongoing discussion, but Michael Driscoll’s answer is pretty good:
Data science, as it’s practiced, is a blend of Red-Bull-fueled hacking and espresso-inspired statistics.
But data science is not merely hacking, because when hackers finish debugging their Bash one-liners and Pig scripts, few care about non-Euclidean distance metrics.
And data science is not merely statistics, because when statisticians finish theorizing the perfect model, few could read a ^A delimited file into R if their job depended on it.
Data science is the civil engineering of data. Its acolytes possess a practical knowledge of tools & materials, coupled with a theoretical understanding of what’s possible.
Driscoll also refers to Drew Conway’s Venn diagram of data science from 2010:
We also may want to look at Nathan Yau’s “sexy skills of data geeks” from his “Rise of the Data Scientist” in 2009:
- Statistics – traditional analysis you’re used to thinking about
- Data Munging – parsing, scraping, and formatting data
- Visualization – graphs, tools, etc.
But wait, is data science a bag of tricks? Or is it just the logical extension of other fields like statistics and machine learning?
For one argument, see Cosma Shalizi’s posts here and here and my posts here and here, which constitute an ongoing discussion of the difference between a statistician and a data scientist.
Also see ASA President Nancy Geller’s 2011 Amstat News article, “Don’t shun the ‘S’ word,” where she defends statistics.
One thing’s for sure, in data science, nobody hands you a clean data set, and nobody tells you what method to use. Moreover, the development of the field is happening in industry, not academia.
In 2011, DJ Patil described how he and Jeff Hammerbacher, in 2008, coined the term data scientist. However, in 2001, William Cleveland wrote a paper about data science (see Nathan Yau’s post on it here).
So data science existed before data scientists? Is this semantics, or does it make sense?
It begs the question, can you define data science by what data scientists do? Who gets to define the field, anyway? There’s lots of buzz and hype – does the media get to define it, or should we rely on the practitioners, the self-appointed data scientists? Or is there some actual authority? Let’s leave these as open questions for now.
Columbia just decided to start an Institute for Data Sciences and Engineering with Bloomberg’s help. The only question is why there’s a picture of a chemist on the announcement. There are 465 job openings in New York for data scientists last time we checked. That’s a lot. So even if data science isn’t a real field, it has real jobs.
Note that most of the job descriptions ask data scientists to be experts in computer science, statistics, communication, data visualization, and to have expert domain expertise. Nobody is an expert in everything, which is why it makes more sense to create teams of people who have different profiles and different expertise, which together, as a team, can specialize in all those things.
Here are other players in the ecosystem:
- O’Reilly and their Strata Conference
- DataKind
- Meetup groups
- VC firms like Union Square Ventures are pouring big money into data science startups
- Kaggle hosts data science competitions
- Chris Wiggins, professor of applied math at Columbia, has been instrumental in connecting techy undergrads with New York start-ups through his summer internship program HackNY.
Note: wikipedia didn’t have an entry on data science until this 2012. This is a new term if not a new subject.
How do you start a Data Science project?
Say you’re working with some website with an online product. You want to track and analyse user behavior. Here’s a way of thinking about it:
- The user interacts with product.
- The product has a front end and a back end.
- The user starts taking actions: clicks, etc.
- Those actions get logged.
- The logs include timestamps; they capture all the key user activity around the product.
- The logs then get processed in pipelines: that’s where data munging, joining, and mapreducing occur.
- These pipelines generate nice, clean, massive data sets.
- These data sets are typically keyed by user, or song (like if you work at a place like Pandora), or however you want to see your data.
- These data sets then get analyzed, modeled, etc.
- They ultimately give us new ways of understanding user behavior.
- This new understanding gets embedded back into the product itself.
- We’ve created a circular process of changing the user interaction with the product by starting with examining the user interaction with the product. This differentiates the job of the data scientist from the traditional data analyst role, which might analyze users for likelihood of purchase but probably wouldn’t change the product itself but rather retarget advertising or something to more likely buyers.
- The data scientist also reports to the CEO or head of product what she’s seeing with respect to the user, what’s happening with the user experience, what are the patterns she’s seeing. This is where communication and reporting skills, as well as data viz skills and old-time story telling skills come in. The data scientist builds the narrative around the product.
- Sometimes you have to scrape the web, to get auxiliary info, because either the relevant data isn’t being logged or it isn’t actually being generated by the users.
Profile yourself
Rachel then handed out index cards and asked everyone to profile themselves (on a relative rather than absolute scale) with respect to their skill levels in the following domains:
- software engineering,
- math,
- stats,
- machine learning,
- domain expertise,
- communication and presentation skills, and
- data viz
We taped the index cards up and got to see how everyone else thought of themselves. There was quite a bit of variation, which is cool – lots of people in the class are coming from social science.
And again, a data science team works best when different skills (profiles) are represented in different people, since nobody is good at everything. It makes me think that it might be easier to define a “data science team” than to define a data scientist.
Thought experiment: can we use data science to define data science?
We broke into small groups to think about this question. Then we had a discussion. Some ideas:
- Yes: google search data science and perform a text mining model
- But wait, that would depend on you being a usagist rather than a prescriptionist with respect to language. Do we let the masses define data science (where “the masses” refers to whatever google’s search engine finds)? Or do we refer to an authority such as the Oxford English Dictionary?
- Actually the OED probably doesn’t have an entry yet and we don’t have time to wait for it. Let’s agree that there’s a spectrum, and one authority doesn’t feel right and “the masses” doesn’t either.
- How about we look at practitioners of data science, and see how they describe what they do (maybe in a word cloud for starters), and then see how people who claim to be other things like statisticians or physics or economics describe what they do, and then we can try to use a clustering algorithm or some other model and see if, when it takes as input “the stuff I do”, it gives me a good prediction on what field I’m in.
Just for comparison, check out what Harlan Harris recently did inside the field of data science: he took a survey and used clustering to define subfields of data science, which gave rise to this picture:
It was a really exciting first week, I’m looking forward to more!
Videos and a love note
A quick post today because I gotta get these kids off to their first day of school. WOOHOOO!!
- I just learned about this video which was made at the first DataKind datadive I went to (it was called Data Without Borders then). The datadive coverage is in the first 6 minutes. It’s timely because I’m doing it again this coming weekend with NYC Parks data. I hope I see you there!
- Next, please check out my friend and fellow occupier Katya’s two videos, which she produced herself: here and here. She has a gift, no?
- Finally, readers, thanks for all the awesome comments lately (and always). I really appreciate the feedback and the thought you’ve put into them, and I’ve been learning a lot. Plus I have a lot of books to read based on your suggestions.
52 Shades of Greed cards fundraiser now up: please help! (#OWS)
The 52 Shades of Greed card deck fundraiser has begun. It’s a joint project of Alternative Banking and a collection of 26 artists and illustrators (you can learn more about the team here).
We’re trying to raise $15,000 to pay for the printing costs and the art. If we raise more money we will try to hold an art show, with talks about the financial system by Alt Banking folk.
Here’s my favorite card, it’s Larry Summers, the king of hearts:
Note he’s a liquidity fairy (I blogged about that here). Or wait, maybe it’s Jamie Dimon, jack of clubs:
Please go to the fundraiser and donate now! You can get the cards as well as other goodies. Amazing!
STEM jobs and the economy
STEM jobs
You know how we’re always hearing that not enough people major in science, technology, math, and engineering? The STEM subjects? That our country is losing pace in the competition with other countries for technology and such?
True and false. True that there are plenty of jobs for people with very strong skills in these areas. On the other hand we don’t want everyone to suddenly become a scientist/engineer/mathematician/computer nerd, because the truth is we don’t really have that many jobs. It’s not like the factory jobs of yesteryear or the agricultural jobs of yesteryesteryear.
Why? These jobs by nature are idiosyncratic and typically conclude with hugely scalable results. There’s only so many social media systems we need created, only so many air traffic control programs that need to work. After a while we might actually be done with some of this. An Detroit-sized army of engineers would not be the right tool for the job, actually, we wouldn’t know what to do with them.
So when you hear calls for more people like this, take it with a grain of salt. The truth is, they are rare now, will probably stay relatively rare, and the reason there’s so much emphasis on STEM professionals is this: having skills like that is a ticket to the elite. Let me explain why I say “elite”, which is a loaded term.
The Economy
There has been plenty of documentation of the following phenomenon: instead of lots of middle class job creation, we’ve been seeing technology-driven high-paying job creation, on the one hand, and a bunch of low-paying, person-to-person jobs like working in health care as home health aides on the other hand.
Be a nerd with me and extrapolate our current system out fifty years. What do you see happening?
Here’s what I see. Continued loss of classic middle-class jobs, continued efficiency gains with highly scaled industries run by a few super techno-savvy billionaire elites. Lots of people either jobless or working in the remaining jobs that can’t be done by computers or taken off-shore, mostly involving food and healthcare. Society has been hollowed out, once and for all.
I actually believe in this, and I don’t think it’s really avoidable. On the other hand, it could either end well or badly, depending on how we deal with it, and depending on what the standard of living is for people who have been edged out of a living by the enormous technological gains we’ve made.
Do they get well-paid for the work they do find? Do they have access to healthcare? Do they have to worry about feeding themselves and their kids? Do they get told by some hypocritical blowhard politician to man up and get a job when no jobs exist? Are they in irretrievably hopeless student debt?
Women, marriages, and the rat-race
There were two articles in the Economist a couple of issues ago which involved women. First, there was an article about marriage rates, saying they’re down all across the world, and showing this graph:
As an explanation, the Economist suggests some possibilities:
First, women are often marrying later as their professional opportunities improve. Second, thanks to increased longevity, bereaved spouses are outliving their partners for longer than the widows and widowers of yesteryear. And third, changing social attitudes in many countries mean that the payoffs of marriage—financial security, sexual relations, a stable relationship—can now often be found outside the nuptial bed.
Let’s call that last possibility the “payoff” reason for not getting married, and rephrase it like this: women are saying, I’d rather not, thanks.
The second Economist article talks about why women don’t rise to the top of companies. It gives us some numbers:
America’s biggest companies hire women to fill just over half of entry-level professional jobs. But those women fail to advance proportionally: they occupy only 28% of senior managerial posts, 14% of seats on executive committees and just 3% of chief-executive roles, according to McKinsey & Company, a consultancy.
Again, as explanation, the Economist suggests some possibilities:
Several factors hold women back at work. Too few study science, engineering, computing or maths. Too few push hard for promotion. Some old-fashioned sexism persists, even in hip, liberal industries. But the biggest obstacle (at least in most rich countries) is children.
Do you know what I’m not seeing? I’m not seeing the payoff reason listed. I’m not seeing the possibility that women decide I’d rather not, thanks.
Considering what we know about internal culture at places like McKinsey & Company and other consultancies, or finance firms, or technology firms, etc., I’m wondering why that wasn’t listed.
Remember, these are educated, smart women being hired at these companies. They have lots of options in general, so I’m not willing to to assume they are all just going home to take care of their kids once they leave their corporation. More likely, they’re leaving because they decide it’s just not going to be their best option.
And yeah, it is hard to have kids and work, but that’s not the only reason to leave a large corporation. Take for example the heroine of the article, Marissa Mayer, the new CEO of Yahoo! (emphasis mine):
Ms Mayer of Yahoo! is an inspiration to many, but a hard act to follow. She boasts of putting in 90-hour weeks at Google. She believes that “burn-out” is for wimps. She says that she will take two weeks’ maternity leave and work throughout it. If she can turn around the internet’s biggest basket case while dandling a newborn on her knee it will be the greatest triumph for working women since winning the right to wear trousers to the office (which did not happen until 1994 in California).
WTF?! She’s an inspiration to who, HR at her company? Who does that? She’s gotta be psychotic – but wait, that’s what’s selected for. I’d like to see another article come out where the Economist asks the question, Why are smart men willing to spend their lives in the quest of leading these companies, considering how awful the conditions are?
In any case, I personally would like to go on record saying Marissa Mayer is not a role model for me.
You know who is, though? This woman I met when she was 80, who had just learned to be a professional potter, and had had various totally fascinating careers before that, including as a ship-builder. She had five kids. She ran away with her current husband at 40. Since I met her she became a writer. My god, this woman is amazing.
Women, and some men, have the power to re-invent themselves, to become more and more interesting and creative as they grow older. That is, to me, inspiring. They are my role models. Keep learning! Keep exploring!
I’m not asking you to agree with me on what is inspiring, but I am asking the Economist to be consistent. If we can manage to believe that not all women see the point in getting married, then can’t we stretch ourselves, just a bit, and imagine that not all women can see the point in staying inside a corporate machine for their entire lives, slowly losing their identity and their ambition in the petty internal rat-races of the idiosyncratic culture of whatever firm they happen to belong to, just so, at the end, they can have too much money and not enough time? Sheesh.
Fair versus equal
In this multimedia presentation, Alan Honick explores the concept of fairness with archaeologist Brian Hayden. It’s entitled “The Evolution of Fairness”, and it’s published by Pacific Standard Magazine.
It’s a series of small writings and short videos which studies evidence of the emergence of inequality in the archaeological record of fishing at a place called Keatley Creek in British Columbia. While it isn’t the most convenient thing to go through, it’s worth the effort. Here are the highlights for me:
When the main concern of the people living at Keatley Creek was subsistence, their society was egalitarian – they shared everything and it wasn’t okay to hoard. Specifically, anyone found trying to game the system was ejected from society, which typically meant death.
As fishing technology improved, the average person could provide for themselves in normal times quite easily, and private ownership became acceptable and common. Those who game the system were no longer ejected, partly because the definitions were different.
At this point, Hayden suggests, people began to do things in small groups that seemed perfectly fair (“I’ll give you 20 fish loaves if you let me marry your daughter” or “Come to my feast tonight and invite me to your feast next week”) and moreover seemed like a private arrangement, until it became sufficiently widespread so that two things happened:
- The guys who didn’t have or couldn’t borrow 20 fish loaves couldn’t get married, or similarly the guys who couldn’t afford to serve a feast never entered into the feast-sharing ritual, and
- The truly rich guys would sometimes have a feast for everyone, which meant the poorer would “get something for nothing” and everyone would gain. Another way of saying this is that the poorer people would allow themselves to be coopted into the unequal system by the price of this free food. Those people who didn’t give feasts or cooperate with the free feasts were outcasts.
An interesting thing happened when Hayden goes to villages in the Mayan Highlands in Mexico and Guatemala which has similar size and social structure as the one on Keatley Creek (see the video on this page). He interviewed people about how the “rich” behaved in times of starvation. Did they take on a managerial role? Did they share and help out in bad times? This is referred to as “communitarian”.
Turns out, no, they exploited the people in the village in the hopes of having better status by the time things got better. They sold maize at exorbitant prices, took outrageous amounts of land for maize, etc. The driving force was individual self-interest.
The overall narrative describes the shifting definition of fairness as things became less and less equal, and how eventually the elite, who essentially got to define fairness, didn’t need to listen to the objections of the poor at all, because they had no power.
Sound familiar?
The author Alan Honick concludes by looking at our society and asks whether campaign finance laws, and Citizens United, is that different in effect from what we saw happening on Keatley Creek. He also points out that, because we humans are so individually obsessed with increasing our status, we can’t seem to get together to address really important issues such as global warming.
Stuff you might want to know about
I have a backlog of things to tell you about that I think are either awesome or scary but important:
- In the awesome category, my friend Anupam just started a new company that helps get volunteers get connected with animal shelters. It’s called BarkLoudly , and you can learn more about it here.
- Again in the awesome category, there’s been progress on seeing if scientific claims can be reproduced. This is for lab experiments, which I’d think would be harder than what I want to do for data models, but what do I know. It’s called The Reproducibility Initiative, run by the Science Exchange, and you can also read about it in this article from Slate.
- On the scary side, read this article by my friend Moe on the questionable constitutionality of student debt laws in this country, and this article on how hard it is to get rid of student debt even through the “undue hardship” route, which involves a “certainty of hopelessness” test. Outrageous.
- Also in the scary category, an argument against the new pill for HIV, written by an entertaining blogger.
Automated call centers and superorganisms
Once upon a time there were people who worked in the insurance office and you could talk to them on the phone or even in person (annoying emphasis intentional).
Now everything is online and you need to call an automated call center to try to conduct business if there’s been an accident or they made a mistake or if you have a question which isn’t “how much do I owe the insurance company?”.
Recently my friend Becky got stuck in the penetralia of an automated call center and she likened the experience to the life of an ant and specifically to the “superorganism hypothesis” of myrmecologist E. O. Wilson (BTW, who here doesn’t love the word “myrmecologist”?). Her description:
Whether or not this is an accurate representation of their inner state, ants have long been described as having an automaton’s machine-like nature, one in which individual identity is subsumed under the totalitarian will of the collective in Borg-like, Communist wetdream fashion.
That’s how I feel when I’m lost in the labyrinthine bowels of automated customer service hell. I’m part of a network that works profitably at the superorganism level, but doesn’t serve the interests of the individual in the slightest, nor cares to nor purports to, driven as it is by the spare logic of collective efficiency.
Question: what is less human than the rigid caste societies of Army ants marching hollowly and inexorably on their prey, driven by the dictates of their genes?
Answer: only the hollowed-out computer-generated voice of the quasi-British phone operator who demands that you enter your social security number over and over again as an exercise in surrendering your will to a corporation whose power role in the financial arrangement is made ever more apparent to both parties by the dawning impossibility of ever speaking to a human at the end of the interminable and ultimately futile phone call.
Powerful analogy; I’ve tended to use the herded cows analogy myself. To entertain myself in the painful waits, I often emit audible “moos” to emphasize the forced passivity I object to. It sometimes backfires and interprets my sounds as a menu choice, though, so I’m thinking of going with the ants, who I don’t think make much noise.
A few thoughts:
- If you know you need to talk to a person eventually and that there’s no point going through all the stages, sometimes just dialing “0 0 0 0 0 0 0 0” a bunch of times will put you straight through. I usually try this straight away the first time I call. Sometimes it works, sometimes it totally fails and I have to call back. Worth a try.
- I wonder how efficient these call centers really are. I have a theory that people simply give up and pay (or default on) their incorrect bills rather than having to deal with this irredeemably opaque system.
- I also wonder what the built-up learned passivity does to us as a society. Having worked as a customer support person myself, I know that there are probably nice people at the other end of the system, and if I could only get through to them, which is a big if, they’d be super informed and helpful. But most people probably don’t think of it that way.
Citigroup’s plutonomy memos
Maybe I’m the last person who’s hearing about the Citigroup “plutonomy memos”, but they’re blowning me away.
Wait, now that I look around, I see that Yves Smith at Naked Capitalism posted about this on October 15, 2009, almost three years ago, and called for people to protest the annual meetings of the American Bankers Association. Man, that’s awesome.
So yeah, I’m a bit late.
But just in case you didn’t hear about the plutonomy memos (h/t Nicholas Levis), which were featured on Michael Moore’s “Capitalism: a Love Story” as well, then you’ll have to read this post immediately and watch Bill Moyer’s clip at the end as well.
The basic story, if you’re still here, is that certain “global strategists” inside Citigroup drafted some advice about investing based on their observation that rich people have all the money and power. They even invented a new word for this, namely “plutonomy.” This excerpt from one of the three memos kind of sums it up:
We project that the plutonomies (the U.S., UK, and Canada) will likely see even more income inequality, disproportionately feeding off a further rise in the profit share in their economies, capitalist-friendly governments, more technology-driven productivity, and globalization… Since we think the plutonomy is here, is going to get stronger… It is a good time to switch out of stocks that sell to the masses and back to the plutonomy basket.
The lawyers for Citigroup keep trying to make people take down the memos, but they’re easy to find once you know to look for them. Just google it.
Nothing that surprising, economically speaking, except for maybe the fact that their reaction, far from being outrage, is something bordering on gleeful. But they aren’t totally complacent:
Low-end developed market labor might not have much economic power, but it does have equal voting power with the rich.
This equal voting power seems to be a pretty serious concern for their plans. They go on to say:
A third threat comes from the potential social backlash. To use Rawls-ian analysis, the invisible hand stops working. Perhaps one reason that societies allow plutonomy, is because enough of the electorate believe they have a chance of becoming a Pluto-participant. Why kill it off, if you can join it? In a sense this is the embodiment of the “American dream”. But if voters feel they cannot participate, they are more likely to divide up the wealth pie, rather than aspire to being truly rich.
Could the plutonomies die because the dream is dead, because enough of society does not believe they can participate? The answer is of course yes. But we suspect this is a threat more clearly felt during recessions, and periods of falling wealth, than when average citizens feel that they are better off. There are signs around the world that society is unhappy with plutonomy – judging by how tight electoral races are.
But as yet, there seems little political fight being born out on this battleground.
This explains to me why Occupy was treated the way it was by Bloomberg’s cops and the entrenched media like the New York Times (and nationally) – the idea that people are opting out and no longer believe they have a chance of being a Pluto-participant is essentially the most threatening thing they can think of. Interestingly, they also say this:
A related threat comes from the backlash to “Robber-barron” economies. The
population at large might still endorse the concept of plutonomy but feel they have lost out to unfair rules. In a sense, this backlash has been epitomized by the media coverage and actual prosecution of high-profile ex-CEOs who presided over financial misappropriation. This “backlash” seems to be something that comes with bull markets and their subsequent collapse. To this end, the cleaning up of business practice, by high-profile champions of fair play, might actually prolong plutonomy.
This is what Dodd-Frank has done, to some extent: a law that makes things seem like they’re getting better, or at least confuses people long enough so they lose their fighting spirit.
Finally, from the third memo:
➤ What could go wrong?
Beyond war, inflation, the end of the technology/productivity wave, and financial collapse, we think the most potent and short-term threat would be societies demanding a more ‘equitable’ share of wealth.
Note the perspective: what could go wrong. Lest we wonder who inititated class warfare.
School starts next week
I know I’m not alone when I say, thank god school starts next week. These kids need to be back in school.
Not that I don’t adore the little lovemuffins, or that I don’t enjoy spending time with them, or that I enjoy hearing them whine about homework. It’s been great, and we’ve watched quite a few good movies in the past few days (for some reason they didn’t enjoy “12 Angry Men” or “Contact” as much as they should have, though).
Don’t get me wrong, I am happy for them to have summer vacation. I just wish we could all take a pill about a week before school actually starts that puts us in a coma for exactly one week. Is that too much to ask?
It wouldn’t help to make summer one week shorter, either. That would just move up the insanity one week sooner. No good. We need that pill.
I’m not employed right now, and I’m trying to find time to write and to plan my future. But it’s kind of hard to do that when my three sons are actively coming up with ways to simultaneously talk louder than anyone knew was humanly possible and to fight ferociously about such things like who gets to play with the cardboard boxes from the last Fresh Direct delivery.
I’m not gonna lie, I’ll be glad when they’re gone. I’m counting the hours. T minus 166.
The country is going to hell, whaddya gonna do.
Yesterday I finished reading Chris Hayes’s book “Twilight of the Elites,” and although I enjoyed it, I have to say it was more about the elites than about their twilight.
He focused on the enormous distance between people in society, how the myth of meritocracy is widening that gap (with healthy references to Karen Ho’s book Liquidated, which I blogged about here), and how, as the entrenched elite get more and more entrenched, they get less and less competent.
But Hayes didn’t really paint a picture of how things would end, although he mentioned the Tea Party and Occupy as possible important sources of resistance, not unlike Barofsky’s recent book Bailout (which I blogged about here), in which Barofsky appealed to the righteous anger of the people to whom government is no longer accountable.
Well, I guess Hayes did add one wrinkle which surprised me. He said it would be the upper middle class, educated class that actually foments the coming revolution. Oh, and the bloggers (because the mainstream media is so captured they’re useless). So me and my friends.
His argument is that we are the ones sufficiently educated and sufficiently insiderish that we will be at the window, with our faces pressed against the glass, looking in at the true insider elites, and seeing how stupid and incompetent those guys are, and how they are rigging the system against the rest of us, and we’ll eventually explode with disgust and righteous anger and that will signal the end.
Kind of feels like that’s already happened, but maybe I’m being impatient.
Two things I really enjoyed about his book:
First, the fact that practically everyone thinks they’re an underdog and has fought tooth and nail to succeed in this world. Absolutely true, including the guys I worked with in finance. I think the phrase he used is “people born on third base think they hit a triple”.
Second, he does a really good job describing the never-can-be-too-rich culture of our country; his example of going to Davos is an excellent one and brings that concept to life perfectly.
It’s enough to get you kind of depressed overall, though. If we are to believe this book’s thesis, our entrenched elite and dysfunctional political structure and economic system are doomed to fail at some future moment, and the best we can hope for is a moment where the hypocrisy collapses in on itself. What is there to look forward to exactly?
I asked that of a friend of mine, and how it was getting me down. His advice to me was to own it more. To make the coming apocalypse an event, kind of like the 4th of July or a vacation, that you plan for and enjoy thinking about.
He said plenty of people do this, it’s in fact a huge industry of doom and gloom. The country is going to hell, whaddya gonna do, he said, might as well have some fun with it.
What? Who are these doom and gloom people? Start here, where Dmitry Orlov compares the preparedness of the US to the former USSR for the coming inevitable apocalypse. He calls this the “Collapse Gap”.
It’s got some great points (although he can’t both say that lawlessness ensues and people take what they want, and also say that people behind in their mortgages will be homeless) and it’s really funny as well, in a completely cynical, Russian way of course. My favorite lines:
One area in which I cannot discern any Collapse Gap is national politics. The ideologies may be different, but the blind adherence to them couldn’t be more similar.
It is certainly more fun to watch two Capitalist parties go at each other than just having the one Communist party to vote for. The things they fight over in public are generally symbolic little tokens of social policy, chosen for ease of public posturing. The Communist party offered just one bitter pill. The two Capitalist parties offer a choice of two placebos. The latest innovation is the photo finish election, where each party buys 50% of the vote, and the result is pulled out of statistical noise, like a rabbit out of a hat.
What makes us fat
I recently finished a book that made rethink being fat, and the cause of the worldwide “obesity epidemic”. Rethink in a good way.
Namely, it suggested the following possibility. What if, rather than getting fat because we are overeating, we overeat because we are getting fat? Another way of thinking about this is that there’s something going on that makes us both store fat away and overeat – that they are both symptomatic of some other problem.
In particular, this would imply that the fact of being fat is not a moral weakness, not a mere lack of willpower. Since I long ago dismissed the willpower hypothesis myself (I don’t seem to have trouble with other aspects of my life which require planning and willpower, why do I have so much trouble with this even though I’ve seriously tried?), this idea comes as something of a “duh” moment, but a welcome one.
To get in the appropriate mindset for this idea, think for a moment about all of the studies you hear about feeding animals such as rats, rabbits, monkeys, pigs, etc. different diets, and noting that sometimes the diet makes them super fat, and sometimes it doesn’t. Sometimes the animals are bred to have a genetic defect, or a pituitary or other gland is removed, and that has an effect on their fatness as well. In other words, there’s some kind of internal chemical thing going on with these animals which causes this condition.
Bottomline: we never accuse the fat mice of lacking will power.
So what is this thing that causes overeating and fat accumulation? The theory given in the book is as follows.
Fat cells are active little chemical warehouses which accept fat molecules and allow fat molecules to leave in two separate (but not unrelated) processes. Rather than thinking of fat as being stored there until the moment it is needed, instead think of the flow of fat molecules both into and out of each fat cell as two constant processes, so it’s actually better to consider the rate of those flows, the inward rate and the outward rate.
Suppose the outward rate of the fat molecules is somehow suppressed compared to the inward rate. So the fat molecules are being allowed into the fat cells just fine but they aren’t leaving the fat cells easily. What would happen?
In the short term, this would happen: lacking the appropriate amount of energy, the overall system would feel internally starved and get super hungry and quickly cause the animal to overeat to compensate for the lack of available energy.
In the longer term, the number of fat cells (or maybe the size of the average fat cell) would increase until the energy flow is sufficient to satisfy the internal needs of the system. In other words, the animal would gain a certain amount of weight (in the form of fat) and stay there, once the internal equilibrium is reached. This jives with the fact that people seem to have a certain “set point” of weight, including overweight. Indeed the amount of fat an animal has in equilibrium allows us to estimate how suppressed the outward flow of energy is.
What causes this suppressed outward rate? The book suggests that it’s elevated insulin. And what causes chronic elevated insulin? The book suggests that the main culprit is refined carbohydrates.
In particular, the author, Gary Taubes, suggests that by avoiding refined carbohydrates such as flour, sugar, and corn syrup, we can bring our insulin levels down to reasonable levels and the outward rate of fat from fat cells will no longer be suppressed.
Not everyone reacts in exactly the same way to refined carbs (i.e. not all insulin responses are identical) and scaled definitely matters, so eating 180 pounds of sugar a year is worse than 90 pounds a year, according to the theory. Moreover, things get progressively worse over time and it takes about 20 years of carb overloading to have such effects.
It’s easier said than done to avoid such foods as an individual living in our culture (nothing at Starbucks, nothing at a newsstand, almost nothing at a bodega), but one thing I like about this theory is that it actually explains the obesity epidemic pretty well: as the author points out, massively scaled refined carbohydrates have only been consumed at such rates for a short while, and the correlations with weight gain are pretty high.
Moreover, and I know this from personally avoiding most carbs for the past 6 months (which I started doing for another, related reason – I hadn’t read the book yet!). I’ve lost weight easily, and I haven’t ever been hungry, even compared to what I used to experience when I wasn’t dieting at all. According to the theory, my fat cells are releasing fat easily because my insulin levels are low, which means I don’t have internal starvation, which in turn explains my complete lack of hunger.
Also in the book: he claims we don’t actually know eating saturated fat raises cholesterol, nor that high cholesterol causes heart disease except when it’s super high, but then again it also seems to be bad to have super low cholesterol. I gotta hand it to this guy, he’s not afraid of going against conventional wisdom, at the risk of being ridiculed, which he most definitely has been.
But that doesn’t make me dismiss his theories, because I’m pretty sure he’s right when he says epidemiology is fraught with politics and bad selection bias.
It’s certainly an interesting book, and who knows, he may be right on some or all scores. On the other hand, maybe it doesn’t matter that much – not many people want to or are willing to avoid carbs, and maybe it’s not environmentally sustainable, although I don’t eat more meat than I used to, just more salad.
We are now ruling out the idea that people don’t exercise enough as the cause for being fat, and as we’ve attempted to follow the advice of the so-called experts, everyone seems to just get fatter all the time. As far as I’m concerned, all conventional bets are off.
#OWS update
I’m happy to show you that Alternative Banking now has a working blog, thanks to a newer member Nicholas Levis. He blogged recently about a Reality Sandwich event I went to last Wednesday, where David Graeber, author of Debt: the first 5000 years was speaking. Interesting and stimulating.
We also have a playing card project called “52 Shades of Greed” which is coming out soon. Check out some of the amazing art here.
Finally, we are about to launch a Kickstarter campaign for our “move your money” app, as soon as I figure out how to accept the money without doing something illegal. Please tell me if you have experience with such things!
More exciting things in the works which I can’t talk about yet. I’ll keep you updated.
NSA mathematicians
When I was a promising young mathematician in college, I met someone from the NSA who tried to recruit me to work for the spooks in the summer. Actually, “met someone” is misleading- he located me after I had won a prize.
I didn’t know what to think, so I accepted his invitation to visit the institute, which was in La Jolla, in Southern California (I went to UC Berkeley so it wasn’t a big trip).
When I got to the building, since I didn’t have clearance, everybody had to stop working the whole time I was there. It wasn’t enough to clean their whiteboards, one of them explained, they had to wash them down with that whiteboard spray stuff, because if you look at a just-erased whiteboard in a certain way you can decipher what had been written on it.
I met a bunch of people, maybe 6 or 7. They all told me how nice it was to work there, how the weather was beautiful, how the math problems were interesting. It was strangely consistent, but who knows, perhaps also true.
One thing I’d already learned before coming is that there are many layers of work that happen before the math people in La Jolla are given problems to do. First, the actual problem is chosen, then the “math” of the problem is extracted from the problem, and third it’s cleansed so that nobody can tell what the original application is.
Knowing this (and I was never contradicted when I explained that process), I asked each of them the same question: how do you feel about the fact that you don’t know what problem you’re actually solving?
Out of the 6 or 7 people I met, everyone but one person responded along the lines, “I believe everything the United States Government does is good.” The last guy said, “yeah, that bothers me. I am honestly seriously considering leaving.”
Needless to say, I didn’t take the job. I wasn’t yet a major league skeptic, but I was skeptical enough to realize I could not survive in such an environment, with colleagues that oblivious. They also mentioned that I’d have to stop dating my Czech boyfriend and that I’d need to submit information about all my roommates for the past 10 years, which was uber creepy.
Nowadays I hear estimates that 600 mathematicians work at the NSA, and of course many more stream through during the summer when school’s not in session, both at La Jolla and Princeton. Somehow they don’t mind not knowing how their work actually gets used. I’m not sure how that’s possible but it clearly is.
This mindset came back to me, and not in a good way, when I read this opinion piece and watched this video in the New York Times a couple of days ago.
William Binney, a mathematician, was working on Soviet Union spying software that got converted to domestic spying after 9/11. In other words, they used his foreign spying algorithm on a new data source, namely American citizen’s raw data. He objected to that, so strongly that he’s come out against it publicly.
The big surprise is how come they let him know what they were actually up to. My guess is he was high enough up the chain that they thought he’d be okay with it – he’d been there 32 years, and I guess he was considered an insider.
In any case, watch the video: this is a courageous man. The FBI came into his house with guns drawn to intimidate him against his whistleblowing activities and yet he hasn’t been cowed. Indeed, after getting dressed (he was coming out of the shower when they exploded into his house), he explained to them the crimes of George Bush and Dick Cheney on his back porch.
As he explains, “the purpose is to monitor what people are doing”. He explains how people’s social media data and other kinds of data are linked over domains and over time to build profiles of Americans over time: “you have 10 years of their life that you can lay out in a timeline, that involves anybody in the country”.
Describing the dangers of this program, Binney was extremely articulate:
- “The danger here is that we could fall into a totalitarian state like East Germany”
- “We can’t have secret interpretations of laws and run them in secret and not tell anybody. We can’t make up kill lists and not tell anybody what the criterion is for being on the kill list”
- “Just because we call ourselves a democracy doesn’t mean we will stay that way.”
There you have it. The good news is that that guy is no longer helping the NSA do their thing.
But the bad news is, plenty of mathematicians still are. And if you want to find a community more trusting and loyal than mathematicians, I think you’d have to go to a kindergarten somewhere. Not to mention the fact that, as I described above, the problems are intentionally cleaned to look innocuous.
Another example, possibly the most important one of all, of mathematics being manipulated to potentially evil ends. We will have trouble proving actual evil consequences, of course, since there’s no transparency. The only update we will get is via the next whistleblower who can handle guns pointed at him as he leaves his shower.
Explain your revenue model to me so I’ll know how I’m paying for this free service
When you find a website that claims to be free for users, we should know to be automatically suspicious. What is sustaining this service? How could you possibly have 35 people working at the underlying company without a revenue source?
We’ve been trained to not think about this, as web surfers, because everything seems, on its face, to be free, until it isn’t, which seems outright objectionable (as I wrote about here). Or is it? Maybe it’s just more honest.
When I go to the newest free online learning site, I’d like to know how they plan to eventually make money. If I’m registering on the site, do I need to worry that they will turn around and sell my data? Is it just advertising? Are they going to keep the good stuff away from me unless I pay?
And it’s not enough to tell me it’s making no revenue yet, that it’s being funded somehow for now without revenue. Because wherever there is funding, there are strings attached.
If the NSF has given a grant for this project, then you can bet the project never involves attacking the NSF for incompetence and politics. If it’s a VC firm, then you’d better believe they are actively figuring out how to make a major return on their investment. So even if they’re not selling your registration and click data now, they have plans for it.
So in other words, I want to know how you’re being funded, who’s giving you the money, and what your revenue model is. Unless you are independently wealthy and want to give back to the community by slaving away on a project, or you’re doing it in your spare time, then I know I’m somehow paying for this.
Just in the spirit of disclosure and transparency, I have no income and I pay a bit for my WordPress site.
When to quit your nerd job
I get lots of emails nowadays from quantitative people who are unhappy in academics, or in finance, or in tech, and want to know what they should do next, and specifically if they should quit their job. Most of them have Ph.D.’s or are even professors or well-established in their profession. They’re interested in switching fields, or at least jobs, and they want advice.
Maybe I get so many emails like this because they’ve read my advice post and realize I’m all about these three rules:
- Go for it! (this usually is all most people need, especially when talking about the crush type of advice)
- Do what you’d do if you weren’t at all insecure (great for people trying to quit a bad job or deciding between job offers)
- Do what a man would do (I usually reserve this advice for women)
I’m going to concentrate mostly on rule #2 today in giving job advice.
Most of the time, the people who ask me are in pretty darn shitty situations and really want to quit, and really want to be able to say to themselves that they deserve better, but are kept from doing so from some kind of fear that there are no better jobs out there or that they deserve to be treated badly. It’s really surprising and annoying that they are so afraid to ask for and demand more. Why are nerds always underselling themselves?
Here are things I hear people complain about that make me want to punch them (or really, give them encouraging hugs and then kicks in the pants):
- My brain is rotting. Why on earth would you stay in a job where your brain rots? Don’t you realize that, as Ph.D.’s in math or stats or physics or whatever, our brains are our main tools? That’s why we get paid, that’s why we will always be able to get a job, but only if we don’t let them rot. It’s kind of like an athlete saying, yeah I’m on this professional team but I spend all day lying around watching TV so my muscles have completely atrophies. Guess what, athlete, that’s no good!
- I’m isolated and nobody ever talks to me. If teamwork is important to you, this is a dealbreaker. Get your ass up and look around. Are there other people in your field/ department/ group who have similar skills as you but who are working with other people? How did they get that set up? Can you get that set up? If you have a boss, can you tell your boss you need to work with other people?
- I’m being used by my company – they pay me well but they don’t give me real work to do. I’m mainly here for them to show clients they have a Ph.D. working in the back. This is pretty common and really terrible, because it leads to brainrot as well as isolation, and moreover your name is attached to what is probably a shitty business model and product. I say demand to get in on the business for real or leave. Simple as that, you don’t want to collude in fraudulent business offerings.
- The pace and politics of academics drive me nuts. I totally get this, personally, because these are also things that led me to leave academics. On the one hand, I’m super glad I left because those things really did drive me nuts. On the other hand, let me just say, you never get rid of your problems, you just get new ones. You have to be prepared for fast-paced but still political problems outside of academics.
My theory is that people are way too slow to quit a job, or at least to agitate for a better position within their workplace. And keep in mind I’m saying this to you as an unemployed person, so you know I know how to quit a job – I’m a pro! The truth is, though, that I quit each job knowing there are lots of juicy jobs out there for people with quantitative skills.
My advice:
First find out what you’re worth on the open market. Look at job listings, talk to people, mention that you’re open to talking to people, find out what else is out there. You may realize you have it pretty good after all, or that it’s worth talking to people inside your company or department about changing your position slightly that would help out your mental state a lot.
Second, you could do the above and then end up saying to yourself, “What the fuck! They’re either promoting me/ moving me or else I’m quitting!”. This is a perfect moment to make demands you wouldn’t normally have the balls to make, and they often work. In fact you should keep in mind that it’s most companies’ policy to generally underpay and underappreciate their employees until they demand better, and then to give in to those demands. True fact.
Next, if you do decide to leave, do a budget on your finances and figure out how many months you can afford to be unemployed. It turns out that people are always very conservative about this (understandably) and it takes them quite a bit of emotional turmoil to even make that calculation with hard numbers. But it’s a good idea, because you’ll often find that you actually have enough money to quit your job and spend a few months learning skills and networking to get a job that you actually think might be a better fit for you.
You can also try to get another job while you’re working, but it’s really hard to be sure you’re not just embarking on a rebound relationship. I prefer being unemployed for a while myself, but it’s all about personality.
Good luck, and remember rule #2!
Someone didn’t get the memo about regulatory capture
So there’s this guy named Benjamin Lawsky, and he’s the New York State Superintendent of Financial Services. Last week he blew open a case against a British bank named Standard Chartered for money laundering and doing business with Iran.
The other regulators don’t like his style one bit, even though he managed to force Standard Chartered to pay $340 million for their misdeeds, as well as look like bad guys. I’ll get back to why the other regulators are pissed but first a bit more on the settlement.
What’s not cool about a fine is that nobody goes to jail and they continue business as usual, hopefully without the money laundering (their stock has mostly recovered as well).
What is cool about the $340 million fine is that it took almost no time compared to other settlements with banks (a nine month investigation before the blowup last week) and that it’s actually pretty big – bigger, for example, then the proposed settlement SEC is making with Citigroup which judge Rackoff blocked for shorting their clients in 2008 and not admitting wrongdoing.
In this case of Standard Chartered, they may not be admitting wrongdoing but we’ve all already read the evidence, as well as the smoking gun email:
The business chugged along even after the banking unit’s chief executive in the Americas warned in a 2006 memo that the company and its management might be vulnerable to “catastrophic reputational damage” and “serious criminal liability.”
According to the regulatory order, a bank official in London replied: “You f- Americans. Who are you to tell us, the rest of the world, that we’re not going to deal with Iranians.”
[Aside: do you think, being a polite Brit, that this guy actually wrote “f-” in his email?]
Back to the other regulators. They are so used to working for the banks, it is inconceivable to them to publicize damning evidence before giving the heads up to the bank in question looking for a quiet settlement. That’s the way they do things. And then they never get much money, and nobody ever goes to jail. Oh, and it takes forever.
They argue that this is because they don’t have enough resources to go the distance with lawyers, but it’s also because their approach is so weak.
So naturally they’ve been pretty upset that Lawsky has balls when they don’t, especially since he doesn’t have nearly the resources that the SEC has.
My favorite ridiculous argument against Lawsky and his approach came from this article I read yesterday on Reuters. It stipulates that Lawsky is creating an environment where there’s a possibility of regulatory arbitrage. From the article:
But a central lesson of the financial crisis was the need for regulators to better cooperate and share information. Working at cross purposes creates opportunities for what’s known as “regulatory arbitrage,” whereby banks circumvent regulations by exploiting rivalries among their various overseers.
Um, what? That whole mindset is clearly off.
The goal would be the regulators get to decide who’s the bad guy, not the banks. And don’t tell me loopholes in the regulatory structure are introduced by having a regulator willing to do his job without sucking everybody’s dick first. Please.
And if I’m a regulator, and if it would work better to share my information with Lawsky to do my job as a regulator, you better believe I’m willing to share it with him if I can get credit alongside him for exposing illegal activities. That is, if I really want to expose illegal activities.
The U.S. Treasury is a bad baby daddy
It occurs to me, when reading Treasury’s latest excuse for the unbelievably shitty performance of HAMP, that Treasury has been a really crappy baby daddy. From a recent New York Times article (also see this):
Mr. Summers declined to comment on the record, but other current and former officials echoed Mr. Geithner’s view that the administration had done well under the circumstances. Some said they underestimated the complexity of helping millions of people. Some said they tried too hard at first to protect taxpayers from unnecessary losses. But they agreed that the most important problem was beyond their control: the mortgage industry was set up either to collect payments or to foreclose, and it was not ready to help people.
“They were bad at their jobs to start with, and they had just gone through this process where they fired lots of people,” said Michael S. Barr, a former assistant Treasury secretary who served as Mr. Geithner’s chief housing aide in 2009 and 2010. “The only surprise was that they were even more screwed up than the high level of screwiness that we expected.”
I mean, let’s say I have a whining teenager who I’ve just realized has stolen my money, signed my name to various notes to the principal, and has been playing hooky for months or even years. I might not ask that same kid to help his friends with their college applications unsupervised.
I might think he needs to be watched, and that I’d keep in mind the selfishness and immaturity that he’s already exposed as I watch him, to make sure he doesn’t end up plagiarizing his best friends’ college essay, or steal the application fees, or something else I hadn’t even thought of.
What I wouldn’t worry about is the possibility that he’s not smart enough to help his friends – he’s already shown me how manipulative and clever he can be when it benefits him.
Moreover, if I didn’t supervise that kid, then after none of his friends get into college I’d blame myself, and not the kid, for my failing. Because he’s only a kid, and I’m supposed to be the grownup. I’d be a bad baby daddy.
That’s what Treasury is doing. Those guys knew better than to trust the banks with something like HAMP, which was essentially unsupervised and had too many conflicting incentives for the banks to ever be expected to actually help people in trouble with their mortgage. They set it up terribly, looked the other way when the banks did nothing (and as Barofsky explained to us, this was intentional – they were foaming the runway for the banks to recover), and now they’re trying to say it’s because the banks were screwed up.
Not good enough, Treasury.
Another death spiral of modeling: e-scores
Yesterday my friend and fellow Occupier Suresh sent me this article from the New York Times.
It’s something I knew was already happening somewhere, but I didn’t know the perpetrators would be quite so proud of themselves as they are; on the other hand I’m also not surprised, because people making good money on mathematical models rarely take the time to consider the ramifications of those models. At least that’s been my experience.
So what have these guys created? It’s basically a modern internet version of a credit score, without all the burdensome regulation that comes with it. Namely, they collect all kinds of information about people on the web, anything they can get their hands on, which includes personal information like physical and web addresses, phone number, google searches, purchases, and clicks of each person, and from that they create a so-called “e-score” which evaluates how much you are worth to a given advertiser or credit card company or mortgage company or insurance company.
Some important issues I want to bring to your attention:
- Credit scores are regulated, and in particular the disallow the use of racial information, whereas these e-scores are completely unregulated and can use whatever information they can gather (which is a lot). Not that credit score models are open source: they aren’t, so we don’t know if they are using variables correlated to race (like zip code). But still, there is some effort to protect people from outrageous and unfair profiling. I never though I’d be thinking of credit scoring companies as the good guys, but it is what it is.
- These e-scores are only going for max pay-out, not default risk. So, for the sake of a credit card company, the ideal customer is someone who pays the minimum balance month after month, never finishing off the balance. That person would have a higher e-score than someone who pays off their balance every month, although presumably that person would have a lower credit score, since they are living more on the edge of insolvency.
- Not that I need to mention this, but this is the ultimate in predatory modeling: every person is scored based on their ability to make money for the advertiser/ insurance company in question, based on any kind of ferreted-out information available. It’s really time for everyone to have two accounts, one for normal use, including filling out applications for mortgages and credit cards and buying things, and the second for sensitive google searches on medical problems and such.
- Finally, and I’m happy to see that the New York Times article noticed this and called it out, this is the perfect setup for the death spiral of modeling that I’ve mentioned before: people considered low value will be funneled away from good deals, which will give them bad deals, which will put them into an even tighter pinch with money because they’re being nickeled and timed and paying high interest rates, which will make them even lower value.
- A model like this is hugely scalable and valuable for a given advertiser.
- Therefore, this model can seriously contribute to our problem of increasing inequality.
- How can we resist this? It’s time for some rules on who owns personal information.
mathbabe 