It was bound to happen. Someone was inevitably going to have to write this book, entitled Social Physics, and now someone has just up and done it. Namely, Alex “Sandy” Pentland, data scientist evangelist, director of MIT’s Human Dynamics Laboratory, and co-founder of the MIT Media Lab.
A review by Nicholas Carr
Pentland argues that our greatly expanded ability to gather behavioral data will allow scientists to develop “a causal theory of social structure” and ultimately establish “a mathematical explanation for why society reacts as it does” in all manner of circumstances. As the book’s title makes clear, Pentland thinks that the social world, no less than the material world, operates according to rules. There are “statistical regularities within human movement and communication,” he writes, and once we fully understand those regularities, we’ll discover “the basic mechanisms of social interactions.”
By collecting all the data – credit card, sensor, cell phones that can pick up your moods, etc. – Pentland seems to think we can put the science into social sciences. He thinks we can predict a person like we now predict planetary motion.
OK, let’s just take a pause here to say: eeeew. How invasive does that sound? And how insulting is its premise? But wait, it gets way worse.
Vomit. But also not the worst part.
Here’s the worst part about Pentland’s book, from the article:
Ultimately, Pentland argues, looking at people’s interactions through a mathematical lens will free us of time-worn notions about class and class struggle. Political and economic classes, he contends, are “oversimplified stereotypes of a fluid and overlapping matrix of peer groups.” Peer groups, unlike classes, are defined by “shared norms” rather than just “standard features such as income” or “their relationship to the means of production.” Armed with exhaustive information about individuals’ habits and associations, civic planners will be able to trace the full flow of influences that shape personal behavior. Abandoning general categories like “rich” and “poor” or “haves” and “have-nots,” we’ll be able to understand people as individuals—even if those individuals are no more than the sums of all the peer pressures and other social influences that affect them.
Kill. Me. Now.
The good news is that the author of the article, Nicholas Carr, doesn’t buy it, and makes all sorts of reasonable complaints about this theory, like privacy concerns, and structural sources of society’s ills. In fact Carr absolutely nails it (emphasis mine):
Pentland may be right that our behavior is determined largely by social norms and the influences of our peers, but what he fails to see is that those norms and influences are themselves shaped by history, politics, and economics, not to mention power and prejudice. People don’t have complete freedom in choosing their peer groups. Their choices are constrained by where they live, where they come from, how much money they have, and what they look like. A statistical model of society that ignores issues of class, that takes patterns of influence as givens rather than as historical contingencies, will tend to perpetuate existing social structures and dynamics. It will encourage us to optimize the status quo rather than challenge it.
How to see how dumb this is in two examples
This brings to mind examples of models that do or do not combat sexism.
First, the orchestra audition example: in order to avoid nepotism, they started making auditioners sit behind a sheet. The result has been way more women in orchestras.
This is a model, even if it’s not a big data model. It is the “orchestra audition” model, and the most important thing about this example is that they defined success very carefully and made it all about one thing: sound. They decided to define the requirements for the job to be “makes good sounding music” and they decided that other information, like how they look, would be by definition not used. It is explicitly non-discriminatory.
By contrast, let’s think about how most big data models work. They take historical information about successes and failures and automate them – rather than challenging their past definition of success, and making it deliberately fair, they are if anything codifying their discriminatory practices in code.
My standard made-up example of this is close to the kind of thing actually happening and being evangelized in big data. Namely, a resume sorting model that helps out HR. But, using historical training data, this model notices that women don’t fare so well historically at a the made-up company as computer programmers – they often leave after only 6 months and they never get promoted. A model will interpret that to mean they are bad employees and never look into structural causes. And moreover, as a result of this historical data, it will discard women’s resumes. Yay, big data!
I’m kind of glad Pentland has written such an awful book, because it gives me an enemy to rail against in this big data hype world. I don’t think most people are as far on the “big data will solve all our problems” spectrum as he is, but he and his book present a convenient target. And it honestly cannot surprise anyone that he is a successful white dude as well when he talks about how big data is going to optimize the status quo if we’d just all wear sensors to work and to bed.
Today’s guest post was written by Amie, who describes herself as a mom of a 9 and a 14-year-old, mathematician, and bigmouth.
Nota bene: this was originally posted on Facebook as a spontaneous rant. Please don’t miscontrue it as an academic argument.
Time for a rant. I’ll preface this by saying that while my kids are creative, beautiful souls, so are many (perhaps all) children I’ve met, and it would be the height of arrogance to take credit for that as a parent. But one thing my husband and I can take credit for are their good manners, because that took work to develop.
The first phrase I taught me daughter was “thank you,” and it’s been put to good use over the years. I’m also loathe to tell other parents what to do, but this is an exception: teach your fucking kids to say “please” and “thank you”. If you are fortunate to visit another country, teach them to say “please” and “thank you” in the native language.
After a week in paradise at a Club Med in Mexico, I’m at some kind of breaking point with rude rich people and their spoiled kids. And that includes the Europeans. Maybe especially the Europeans. What is it that when you’re in France everyone’s all “thank you and have a nice day” but when these petit bourgeois assholes come to Cancun they treat Mexicans like nonhumans? My son held the door for a face-lifted Russian lady today who didn’t even say thank you.
Anyway, back to kids: I’m not saying that you should suppress your kids’ nature joie de vivre and boisterous, rambunctious energy (though if that’s what they’re like, please keep them away from adults who are not in the mood for it). Just teach them to treat other people with basic respect and courtesy. That means prompting them to say “please,” “thank you,” and “nice to meet you” when they interact with other people.
Jordan Ellenberg just posted how a huge number of people accepted to the math Ph.D. program at the University of Wisconsin never wrote to tell him that they had accepted other offers. When other people are on a wait list!
Whose fault is this? THE PARENTS’ FAULT. Damn parents. Come on!!
P.S. Those of you who have put in the effort to raise polite kids: believe me, I’ve noticed. So has everyone else.
What is an experiment?
The gold standard in scientific fields is the randomized experiment. That’s when you have some “treatment” you want to impose on some population and you want to know if that treatment has positive or negative effects. In a randomized experiment, you randomly divide a population into a “treatment” group and a “control group” and give the treatment only to the first group. Sometimes you do nothing to the control group, sometimes you give them some other treatment or a placebo. Before you do the experiment, of course, you have to carefully define the population and the treatment, including how long it lasts and what you are looking out for.
Example in medicine
So for example, in medicine, you might take a bunch of people at risk of heart attacks and ask some of them – a randomized subpopulation – to take aspirin once a day. Note that doesn’t mean they all will take an aspirin every day, since plenty of people forget to do what they’re told to do, and even what they intend to do. And you might have people in the other group who happen to take aspirin every day even though they’re in the other group.
Also, part of the experiment has to be well-defined lengths and outcomes of the experiment: after, say, 10 years, you want to see how many people in each group have a) had heart attacks and b) died.
Now you’re starting to see that, in order for such an experiment to yield useful information, you’d better make sure the average age of each subpopulation is about the same, which should be true if they were truly randomized, and that there are plenty of people in each subpopulation, or else the results will be statistically useless.
One last thing. There are ethics in medicine, which make experiments like the one above fraught. Namely, if you have a really good reason to think one treatment (“take aspirin once a day”) is better than another (“nothing”), then you’re not allowed to do it. Instead you’d have to compare two treatments that are thought to be about equal. This of course means that, in general, you need even more people in the experiment, and it gets super expensive and long.
So, experiments are hard in medicine. But they don’t have to be hard outside of medicine! Why aren’t we doing more of them when we can?
Swedish work experiment
Let’s move on to the Swedes, who according to this article (h/t Suresh Naidu) are experimenting in their own government offices on whether working 6 hours a day instead of 8 hours a day is a good idea. They are using two different departments in their municipal council to act as their “treatment group” (6 hours a day for them) and their “control group” (the usual 8 hours a day for them).
And although you might think that the people in the control group would object to unethical treatment, it’s not the same thing: nobody thinks your life is at stake for working a regular number of hours.
The idea there is that people waste their last couple of hours at work and generally become inefficient, so maybe knowing you only have 6 hours of work a day will improve the overall office. Another possibility, of course, is that people will still waste their last couple of hours of work and get 4 hours instead of 6 hours of work done. That’s what the experiment hopes to measure, in addition to (hopefully!) whether people dig it and are healthier as a result.
Non-example in business: HR
Before I get too excited I want to mention the problems that arise with experiments that you cannot control, which is most of the time if you don’t plan ahead.
Some of you probably ran into an article from the Wall Street Journal, entitled Companies Say No to Having an HR Department. It’s about how some companies decided that HR is a huge waste of money and decided to get rid of everyone in that department, even big companies.
On the one hand, you’d think this is a perfect experiment: compare companies that have HR departments against companies that don’t. And you could do that, of course, but you wouldn’t be measuring the effect of an HR department. Instead, you’d be measuring the effect of a company culture that doesn’t value things like HR.
So, for example, I would never work in a company that doesn’t value HR, because, as a woman, I am very aware of the fact that women get sexually harassed by their bosses and have essentially nobody to complain to except HR. But if you read the article, it becomes clear that the companies that get rid of HR don’t think from the perspective of the harassed underling but instead from the perspective of the boss who needs help firing people. From the article:
When co-workers can’t stand each other or employees aren’t clicking with their managers, Mr. Segal expects them to work it out themselves. “We ask senior leaders to recognize any potential chemistry issues” early on, he said, and move people to different teams if those issues can’t be resolved quickly.
Former Klick employees applaud the creative thinking that drives its culture, but say they sometimes felt like they were on their own there. Neville Thomas, a program director at Klick until 2013, occasionally had to discipline or terminate his direct reports. Without an HR team, he said, he worried about liability.
“There’s no HR department to coach you,” he said. “When you have an HR person, you have a point of contact that’s confidential.”
Why does it matter that it’s not random?
Here’s the crucial difference between a randomized experiment and a non-randomized experiment. In a randomized experiment, you are setting up and testing a causal relationship, but in a non-randomized experiment like the HR companies versus the no-HR companies, you are simply observing cultural differences without getting at root causes.
So if I notice that, at the non-HR companies, they get sued for sexual harassment a lot – which was indeed mentioned in the article as happening at Outback Steakhouse, a non-HR company – is that because they don’t have an HR team or because they have a culture which doesn’t value HR? We can’t tell. We can only observe it.
Money in politics experiment
Here’s an awesome example of a randomized experiment to understand who gets access to policy makers. In an article entitled A new experiment shows how money buys access to Congress, an experiment was conducted by two political science graduate students, David Broockman and Josh Kalla, which they described as follows:
In the study, a political group attempting to build support for a bill before Congress tried to schedule meetings between local campaign contributors and Members of Congress in 191 congressional districts. However, the organization randomly assigned whether it informed legislators’ offices that individuals who would attend the meetings were “local campaign donors” or “local constituents.”
The letters were identical except for those two words, but the results were drastically different, as shown by the following graphic:
Conducting your own experiments with e.g. Mechanical Turk
You know how you can conduct experiments? Through an Amazon service called Mechanical Turk. It’s really not expensive and you can get a bunch of people to fill out surveys, or do tasks, or some combination, and you can design careful experiments and modify them and rerun them at your whim. You decide in advance how many people you want and how much to pay them.
So for example, that’s how then-Wall Street Journal journalist Julia Angwin, in 2012, investigated the weird appearance of Obama results interspersed between other search results, but not a similar appearance of Romney results, after users indicated party affiliation.
We already have a good idea of how to design and conduct useful and important experiments, and we already have good tools to do them. Other, even better tools are being developed right now to improve our abilities to conduct faster and more automated experiments.
If we think about what we can learn from these tools and some creative energy into design, we should all be incredibly impatient and excited. And we should also think of this as an argumentation technique: if we are arguing about whether a certain method or policy works versus another method or policy, can we set up a transparent and reproducible experiment to test it? Let’s start making science apply to our lives.
I’m not saying anything you don’t know already. I’m just stating the obvious: people who obsessively exercise are super boring. They talk all the time about their times, and their workout progress, and their aching muscles, and it’s like you don’t even have to be there, you could just replace yourself with a gadget that listens, nods, and then says encouraging things like, “Way to go!” at the very end. Excruciating.
Look, don’t get me wrong. I’ve gone through bouts of obsessive exercise myself, and those bouts sometimes were pretty lengthy. And no, it didn’t ever make me skinny, just incredibly fit. I remember I trained for a sprint triathlon once, and man was I fit by the time it finally happened in the spring on 2004.
But then, when I got to the starting line, and there I was wishing I could reorder the events so the the beginning swim would 5 kilometers and the run at the end were a quarter mile – I’ve never been much of a runner – and I just looked around at myself and everyone else there, and I wondered how I’d become so incredibly boring and self-obsessed that I had paid good money and driven miles and miles just to obsessively exercise in front of other people.
What was going on with me? I became increasingly disgusted by my own boringness throughout the race. I think the worst part was how many people said “You go, girl!” when I jogged by. They were trying to encourage the fat girl, I get it, but it made it even more obvious that I was doing something that I honestly didn’t need to be getting public response to.
Look, I’m not against exercise, and I love doing it, or at least I love having done it because it makes you feel good, and I encourage everyone to be fit and happy. But I’m serious when I say I will no longer tolerate hanging out with people who obsess over it and want to talk to me about their obsession. Too frigging boring, people!
So if someone mentions that they went biking over this gorgeous spring weekend, then awesome, I’ll be happy for them. But if they want to talk about which bike they used, and what their time around Central Park was, and how they’re training for this or that event, then no. I will tell them “sorry but can we talk about something not incredibly boring now?”
Why do I mention this today? Because I finally figured out what my hostility towards the Quantified Self crowd is, and it’s this same thing. All those gadgets and doodads are essentially props to pull out and use to have that same boring conversation that I’ve already refused to give into. So please, don’t show me your sleep tracker or your step monitor and expect me to care. I don’t care.
And don’t get me wrong – again – I know some people will benefit from that kind of thing. And some people actually have illnesses or physical therapy and exercise and particularly quantified exercise might particularly help them keep track of their health! I get it!
But let’s face it, most people are not doing this for health. They are doing it for some other weird, narcissistic and anxiety-shielding coping-mechanistic self-competitive (or outright competitive) reason. And again, I’m not hating on them exactly, because I get it, and I’ve been there. But I don’t want to talk about it with them.
It’s unusual that I find myself in the position of defending Wall Street activities, but here goes.
I just don’t think HFT is that big of a deal relative to other Wall Street evils. I have written a couple of times about HFT and I’m not a huge fan, and I don’t buy the “liquidity is good and more liquidity is better” argument: at some point enough is enough. I do think that day-to-day investors have largely benefitted from it but that people whose money is in massive funds which are regularly traded have seen their money get skimmed every month. Overall it’s a smallish negative tax on the average person, I’d expect.
Here’s why HFT deserves some of our hatred: there’s way too much human resources going into this stuff and it’s embarrassing, what with the laying of cables and blasting through mountains and such. And it’s a great sociological look into the absolutely greed-led mindset of the Wall Street trader, but honestly I think we already had that. It’s really business as usual at a microscopic scale, and nobody should really be surprised to learn that people will do anything to make money that’s technically possible and technically legal, and that they will brag about how they’re making the world a better place while they do it. Same old same old.
So I’m not saying HFT is awesome and we should encourage more of it. I’m all for thinking about how to slow down trading to once a second and make it “more fair” for more players (although that’s hard to do even as a thought experiment), or taxing transaction to make things slow down by themselves, which would be easy.
But here’s the thing, it’s not some huge awful thing we should focus on, even though Michael Lewis is a really good and engaging writer.
You wanna focus on something? Let’s talk about money laundering in HSBC and now Citi that is not under control. Let’s talk about ongoing mortgage fraud and robo-signing and the ongoing bailout/ taxpayer subsidy and people still losing their homes, and the poor still being the targets of illegal and predatory loans, and Too-Big-To-Fail getting worse, and the direct line between the bailout and the broken pension promises for civil servants and the overall price list for fraud that has been built.
Let’s talk about the people who created the underlying fraud still at work in places like Bank of America, and how few masterminds have gone to jail and how the SEC and the Obama administration has made that happen through inaction and passivity and how Congress is sitting on its hands because of the money coming in from lobbyists. Let’s talk about the increasing distance between the justice system for the poor and the justice system for the rich in this country.
Tell me what I missed.
The HFT noise is misplaced and a distraction from the ongoing real story.
Not enough time for a full post this morning, but I’d like people to read a New York Times article ironically entitled Moving Past Gender Barriers to Negotiate a Raise (hat tip Laura Strausfeld). It has amazing and awful tidbits like the following:
“It’s totally unfair because we don’t require the same thing of men. But if women want to be successful in this domain, they need to pay attention to this.”
If you read on you realize that what they mean by “pay attention to” is “roll over and conform to stereotypes”. Super gross, and fuck that.
I feel like this is a more subtle, New York Times version of Susan Patton’s terrible advice for young women in snaring husbands. What happened to the feminists?!!
Before I begin this morning’s rant, I need to mention that, as I’ve taken on a new job recently and I’m still trying to write a book, I’m expecting to not be able to blog as regularly as I have been. It pains me to say it but my posts will become more intermittent until this book is finished. I’ll miss you more than you’ll miss me!
On to today’s bullshit modeling idea, which was sent to me by both Linda Brown and Michael Crimmins. It’s a new model built in part by the former chief economist for the Commodity Futures Trading Commission (CFTC) Andrei Kirilenko, who is now a finance professor at Sloan. In case you don’t know, the CFTC is the regulator in charge of futures and swaps.
I’ll excerpt this New York Times article which describes the model:
The algorithm, he says, uncovers key word clusters to measure “regulatory sentiment” as pro-regulation, anti-regulation or neutral, on a scale from -1 to +1, with zero being neutral.
If the number assigned to a final rule is different from the proposed one and closer to the number assigned to all the public comments, then it can be inferred that the agency has taken the public’s views into account, he says.
- I know really smart people that use similar sentiment algorithms on word clusters. I have no beef with the underlying NLP algorithm.
- What I do have a problem with is the apparent assumption that the “the number assigned to all the public comments” makes any sense, and in particular whether it takes into account “the public’s view”.
- It sounds like the algorithm dumps all the public comment letters into a pot and mixes it together to get an overall score. The problem with this is that the industry insiders and their lobbyists overwhelm public commenting systems.
- For example, go take a look at the list of public letters for the Volcker Rule. It’s not unlike this graphic on the meetings of the regulators on the Volcker Rule:
- Besides dominating the sheer number of letters, I’ll bet the length of each letter is also much longer on average for such parties with very fancy lawyers.
- Now think about how the NLP algorithm will deal with this in a big pot: it will be dominated by the language of the pro-industry insiders.
- Moreover, if such a model were to be directly used, say to check that public commenting letters were written in a given case, lobbyists would have even more reason to overwhelm public commenting systems.
The take-away is that this is an amazing example of a so-called objective mathematical model set up to legitimize the watering down of financial regulation by lobbyists.
Update: I’m willing to admit I might have spoken too soon. I look forward to reading the paper on this algorithm and taking a deeper look instead of relying on a newspaper.