Crossposted on Naked Capitalism



I just finished reading Nate Silver’s newish book, The Signal and the Noise: Why so many predictions fail – but some don’t.

The good news

First off, let me say this: I’m very happy that people are reading a book on modeling in such huge numbers – it’s currently eighth on the New York Times best seller list and it’s been on the list for nine weeks. This means people are starting to really care about modeling, both how it can help us remove biases to clarify reality and how it can institutionalize those same biases and go bad.

As a modeler myself, I am extremely concerned about how models affect the public, so the book’s success is wonderful news. The first step to get people to think critically about something is to get them to think about it at all.

Moreover, the book serves as a soft introduction to some of the issues surrounding modeling. Silver has a knack for explaining things in plain English. While he only goes so far, this is reasonable considering his audience. And he doesn’t dumb the math down.

In particular, Silver does a nice job of explaining Bayes’ Theorem. (If you don’t know what Bayes’ Theorem is, just focus on how Silver uses it in his version of Bayesian modeling: namely, as a way of adjusting your estimate of the probability of an event as you collect more information. You might think infidelity is rare, for example, but after a quick poll of your friends and a quick Google search you might have collected enough information to reexamine and revise your estimates.)

The bad news

Having said all that, I have major problems with this book and what it claims to explain. In fact, I’m angry.

It would be reasonable for Silver to tell us about his baseball models, which he does. It would be reasonable for him to tell us about political polling and how he uses weights on different polls to combine them to get a better overall poll. He does this as well. He also interviews a bunch of people who model in other fields, like meteorology and earthquake prediction, which is fine, albeit superficial.

What is not reasonable, however, is for Silver to claim to understand how the financial crisis was a result of a few inaccurate models, and how medical research need only switch from being frequentist to being Bayesian to become more accurate.

Let me give you some concrete examples from his book.

Easy first example: credit rating agencies

The ratings agencies, which famously put AAA ratings on terrible loans, and spoke among themselves as being willing to rate things that were structured by cows, did not accidentally have bad underlying models. The bankers packaging and selling these deals, which amongst themselves they called sacks of shit, did not blithely believe in their safety because of those ratings.

Rather, the entire industry crucially depended on the false models. Indeed they changed the data to conform with the models, which is to say it was an intentional combination of using flawed models and using irrelevant historical data (see points 64-69 here for more (Update: that link is now behind the paywall)).

In baseball, a team can’t create bad or misleading data to game the models of other teams in order to get an edge. But in the financial markets, parties to a model can and do.

In fact, every failed model is actually a success

Silver gives four examples what he considers to be failed models at the end of his first chapter, all related to economics and finance. But each example is actually a success (for the insiders) if you look at a slightly larger picture and understand the incentives inside the system. Here are the models:

The housing bubble. The credit rating agencies selling AAA ratings on mortgage securities. The financial melt-down caused by high leverage in the banking sector. The economists’ predictions after the financial crisis of a fast recovery.

Here’s how each of these models worked out rather well for those inside the system:

Everyone involved in the mortgage industry made a killing. Who’s going to stop the music and tell people to worry about home values? Homeowners and taxpayers made money (on paper at least) in the short term but lost in the long term, but the bankers took home bonuses that they still have. As we discussed, this was a system-wide tool for building a money machine. The financial melt-down was incidental, but the leverage was intentional. It bumped up the risk and thus, in good times, the bonuses. This is a great example of the modeling feedback loop: nobody cares about the wider consequences if they’re getting bonuses in the meantime. Economists are only putatively trying to predict the recovery. Actually they’re trying to affect the recovery. They get paid the big bucks, and they are granted authority and power in part to give consumers confidence, which they presumably hope will lead to a robust economy.

Cause and effect get confused

Silver confuses cause and effect. We didn’t have a financial crisis because of a bad model or a few bad models. We had bad models because of a corrupt and criminally fraudulent financial system.

That’s an important distinction, because we could fix a few bad models with a few good mathematicians, but we can’t fix the entire system so easily. There’s no math band-aid that will cure these boo-boos.

I can’t emphasize this too strongly: this is not just wrong, it’s maliciously wrong. If people believe in the math band-aid, then we won’t fix the problems in the system that so desperately need fixing.

Why does he make this mistake?

Silver has an unswerving assumption, which he repeats several times, that the only goal of a modeler is to produce an accurate model. (Actually, he made an exception for stock analysts.)

This assumption generally holds in his experience: poker, baseball, and polling are all arenas in which one’s incentive is to be as accurate as possible. But he falls prey to some of the very mistakes he warns about in his book, namely over-confidence and over-generalization. He assumes that, since he’s an expert in those arenas, he can generalize to the field of finance, where he is not an expert.

The logical result of this assumption is his definition of failure as something where the underlying mathematical model is inaccurate. But that’s not how most people would define failure, and it is dangerously naive.

Medical Research

Silver discusses both in the Introduction and in Chapter 8 to John Ioannadis’s work which reveals that most medical research is wrong. Silver explains his point of view in the following way:

I’m glad he mentions incentives here, but again he confuses cause and effect.

As I learned when I attended David Madigan’s lecture on Merck’s representation of Vioxx research to the FDA as well as his recent research on the methods in epidemiology research, the flaws in these medical models will be hard to combat, because they advance the interests of the insiders: competition among academic researchers to publish and get tenure is fierce, and there are enormous financial incentives for pharmaceutical companies.

Everyone in this system benefits from methods that allow one to claim statistically significant results, whether or not that’s valid science, and even though there are lives on the line.

In other words, it’s not that there are bad statistical approaches which lead to vastly over-reported statistically significant results and published papers (which could just as easily happen if the researchers were employing Bayesian techniques, by the way). It’s that there’s massive incentive to claim statistically significant findings, and not much push-back when that’s done erroneously, so the field never self-examines and improves their methodology. The bad models are a consequence of misaligned incentives.

I’m not accusing people in these fields of intentionally putting people’s lives on the line for the sake of their publication records. Most of the people in the field are honestly trying their best. But their intentions are kind of irrelevant.

Silver ignores politics and loves experts

Silver chooses to focus on individuals working in a tight competition and their motives and individual biases, which he understands and explains well. For him, modeling is a man versus wild type thing, working with your wits in a finite universe to win the chess game.

He spends very little time on the question of how people act inside larger systems, where a given modeler might be more interested in keeping their job or getting a big bonus than in making their model as accurate as possible.

In other words, Silver crafts an argument which ignores politics. This is Silver’s blind spot: in the real world politics often trump accuracy, and accurate mathematical models don’t matter as much as he hopes they would.

As an example of politics getting in the way, let’s go back to the culture of the credit rating agency Moody’s. William Harrington, an ex-Moody’s analyst, describes the politics of his work as follows:

In 2004 you could still talk back and stop a deal. That was gone by 2006. It became: work your tail off, and at some point management would say, ‘Time’s up, let’s convene in a committee and we’ll all vote “yes”‘.

To be fair, there have been moments in his past when Silver delves into politics directly, like this post from the beginning of Obama’s first administration, where he starts with this (emphasis mine):

To suggest that Obama or Geithner are tools of Wall Street and are looking out for something other than the country’s best interest is freaking asinine.

and he ends with:

This is neither the time nor the place for mass movements — this is the time for expert opinion. Once the experts (and I’m not one of them) have reached some kind of a consensus about what the best course of action is (and they haven’t yet), then figure out who is impeding that action for political or other disingenuous reasons and tackle them — do whatever you can to remove them from the playing field. But we’re not at that stage yet.

My conclusion: Nate Silver is a man who deeply believes in experts, even when the evidence is not good that they have aligned incentives with the public.

Distrust the experts

Call me “asinine,” but I have less faith in the experts than Nate Silver: I don’t want to trust the very people who got us into this mess, while benefitting from it, to also be in charge of cleaning it up. And, being part of the Occupy movement, I obviously think that this is the time for mass movements.

From my experience working first in finance at the hedge fund D.E. Shaw during the credit crisis and afterwards at the risk firm Riskmetrics, and my subsequent experience working in the internet advertising space (a wild west of unregulated personal information warehousing and sales) my conclusion is simple: Distrust the experts.

Why? Because you don’t know their incentives, and they can make the models (including Bayesian models) say whatever is politically useful to them. This is a manipulation of the public’s trust of mathematics, but it is the norm rather than the exception. And modelers rarely if ever consider the feedback loop and the ramifications of their predatory models on our culture.

Why do people like Nate Silver so much?

To be crystal clear: my big complaint about Silver is naivete, and to a lesser extent, authority-worship.

I’m not criticizing Silver for not understanding the financial system. Indeed one of the most crucial problems with the current system is its complexity, and as I’ve said before, most people inside finance don’t really understand it. But at the very least he should know that he is not an authority and should not act like one.

I’m also not accusing him of knowingly helping cover up the financial industry. But covering for the financial industry is an unfortunate side-effect of his naivete and presumed authority, and a very unwelcome source of noise at this moment when so much needs to be done.

I’m writing a book myself on modeling. When I began reading Silver’s book I was a bit worried that he’d already said everything I’d wanted to say. Instead, I feel like he’s written a book which has the potential to dangerously mislead people – if it hasn’t already – because of its lack of consideration of the surrounding political landscape.

Silver has gone to great lengths to make his message simple, and positive, and to make people feel smart and smug, especially Obama’s supporters.

He gets well-paid for his political consulting work and speaker appearances at hedge funds like D.E. Shaw and Jane Street, and, in order to maintain this income, it’s critical that he perfects a patina of modeling genius combined with an easily digested message for his financial and political clients.

Silver is selling a story we all want to hear, and a story we all want to be true. Unfortunately for us and for the world, it’s not.

How to push back against the celebrity-ization of data science

The truth is somewhat harder to understand, a lot less palatable, and much more important than Silver’s gloss. But when independent people like myself step up to denounce a given statement or theory, it’s not clear to the public who is the expert and who isn’t. From this vantage point, the happier, shorter message will win every time.

This raises a larger question: how can the public possibly sort through all the noise that celebrity-minded data people like Nate Silver hand to them on a silver platter? Whose job is it to push back against rubbish disguised as authoritative scientific theory?

It’s not a new question, since PR men disguising themselves as scientists have been around for decades. But I’d argue it’s a question that is increasingly urgent considering how much of our lives are becoming modeled. It would be great if substantive data scientists had a way of getting together to defend the subject against sensationalist celebrity-fueled noise.

One hope I nurture is that, with the opening of the various data science institutes such as the one at Columbia which was a announced a few months ago, there will be a way to form exactly such a committee. Can we get a little peer review here, people?

Conclusion

There’s an easy test here to determine whether to be worried. If you see someone using a model to make predictions that directly benefit them or lose them money – like a day trader, or a chess player, or someone who literally places a bet on an outcome (unless they place another hidden bet on the opposite outcome) – then you can be sure they are optimizing their model for accuracy as best they can. And in this case Silver’s advice on how to avoid one’s own biases are excellent and useful.

But if you are witnessing someone creating a model which predicts outcomes that are irrelevant to their immediate bottom-line, then you might want to look into the model yourself.