I'm not discounting her complaints, but the following is not a confusion of cause and effect:
"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."
A= Financial Crisis
B= Bad Models
C= Fraudulent System.
Nate said "A<-B"
Author says "A<-B<-C"
That is not a mix up of cause and effect.
Author's main complaint seems to be that Nate assumes bad models are an accident, and Author claims they were intentional.
Again, not a mixup of cause and effect. At worst it's a naive interpretation of the correct cause.
"Bad Model" in Silver's view is really "bad modelling" -- a problem with the statistical technique (frequentist vs. bayesian) used to model the data.
"Bad Model" in O'Neil's view is really "bad data" -- intentionally skewed numbers as a consequent of conscious or sub-conscious eliding or redactory effects on the data populating the model, for purposes other than accuracy: financial gain, for instance.
O'Neil's point is that even a frequentist model would have been effective had the data not been massaged. On this view O'Neil's view is correct: the cause was not "bad modelling" but "bad data." The "bad data" in the model was an "effect" of corruption, not the root "cause" of the financial meltdown.
"O'Neil's point is that even a frequentist model would have been effective had the data not been massaged."
This post really has nothing to do with techniques or "bad data" or a bayesian vs. frequentist debate.
Her point is that bad modeling was entirely incidental and inconsequential as a cause of the financial meltdown. To repeat an example I used previously, if I smashed your head in with a hammer, Silver would essentially be saying "a hammer caused davesims death."
The statement may be technically true, but it's a shallow analysis of cause and effect. It's unsatisfying because it gives too much significance to an incidental link in the complete chain of events.
> This post really has nothing to do with techniques or "bad data" or a bayesian vs. frequentist debate.
I disagree, but maybe I'm missing something. This early quote, regarding 'bayesian vs. frequentist' seems to sum up O'Neil's view of Silver fairly well to me:
"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."
And later on regarding 'bad data,' a point she reiterates several times:
"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 do think in the above quote O'Neil is equivocating between the idea of a 'bad model' and the skewed data or overreaching significance applied to the model.
Her fundamental point, which she could have done a better job communicating I think, is that it wasn't the models themselves that were bad, bayesian or otherwise, but the data in the models and at times the inordinate significance applied to those models that was the real cause of the meltdown.
These are particulars to the examples she provided. The unifying idea is that people often choose models or data based on self interest, even at the expense of accuracy. When discussing the cause of an event, it's not enough to say "a bad model is to blame" or "bad data is to blame." At this point, the model or data are instruments serving the self interest of the modeller, and they are incidental in the cause and effect relationship.
>On this view O'Neil's view is correct: the cause was not "bad modelling" but "bad data." The "bad data" in the model was an "effect" of corruption, not the root "cause" of the financial meltdown.
So it goes that corruption caused bad data, which caused a bad analysis by the model(she claims that both the data and model are bad), which caused the financial meltdown.
That does not invalidate Silver's point. It merely points out that Silver's analysis may be inadequate.
What I understand is that she means that corruption lead to the financial crisis. The bad models/data were just a smoke screen, or an instrument to get rich quicker.
The author isn't saying that A<-B is a confusion of cause and effect. She is saying that B is itself an effect of C, not a cause. (A better way to say it would be "B is not a root cause".)
The bad models were a consequence of a fraudulent system. So by definition, the models weren't the cause of the financial crisis. Therefore the bad models were an effect, in the same way the financial crisis was an effect.
If this is true, then the author's claim (mix-up of cause and effect) must be correct.
>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).
So C(corruption) directly leads to B(bad model). She does not argue that A(everything falling apart)<-C anywhere in the piece. Silver is claiming that A<-B. He is not commenting about C. So while Silver's claim may not identify the absolute root cause, the author does not actually prove that his cause and effect analysis is flawed. If anything, she has shown that it may be incomplete.
Silver isn't just claming that A<-B. He is claiming that fixing B will fix A, and that fixing B is straightforward: just use good models.
mathbabe is saying that fixing B is not straightforward, because B<-C, and fixing C is very, very difficult. Bad models exist because people have incentives to make them bad.
She's also saying, I think, that fixing B by itself won't fix A, because B is not the root cause; C is. Fixing B without fixing C just means that C will manifest itself somewhere else, and A will still happen. In other words, there are many causal routes from C to A, and fixing B by itself only blocks one of them. So A<-C is true regardless of the state of B.
So C(corruption) directly leads to B(bad model). She does not argue that A(everything falling apart)<-C anywhere in the piece.
The point of the piece is that the financial meltdown stemmed from corruption, not from models. If this wasn't her point, then why else would she have written this piece?
She wrote this piece because she thinks he's only citing the proximate cause and not the ultimate cause. At least that's how it reads to me.
She doesn't appear to be disputing the idea that following bad models caused the crisis — instead, she just seems to be saying Silver missed the fact that the models were deliberately bad rather than simply a poor application of math.
A few quotes from the author herself:
"the entire industry crucially depended on the false models."
"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."
She's not disputing that the models were false or even that acting in accordance with the models is what caused the crisis. She's disputing that this was accidental.
An argument doesn't have to contain a false statement to have no baring on the discussion.
And more generally it's quite possible for a thing to have multiple causes, and in this universe this is generally true for everything we witness. So if A causes C that doesn't mean that B can't also cause C "by definition". I'm not aware of any serious philosphical framework that says that events must have only a single cause, though many philosophers from Aristotle onwards[1] but great stock in the "final cause" of things, but that would be either "God" or "The Big Bang" depending on your religion or lack thereof and nobody was arguing for those.
And as an aside, the words "by definition" in an argument that isn't on it's surface about definitions is generally a bit of a red flag.[2]
She does not prove that claim, though. She argues that corruption causes bad models. For Silver's cause and effect analysis to be incorrect, she needed to prove that the inaccurate models had absolutely nothing to do with the financial meltdown.
The models can be part of the meltdown without really being a cause.
If I choose to shoot you with a gun, and have a variety of appropriate guns, it is not the fault of the specific gun chosen that it was used to kill you. I had motive, opportunity, and alternate means available.
"For Silver's cause and effect analysis to be incorrect, she needed to prove that the inaccurate models had absolutely nothing to do with the financial meltdown."
If I smashed your head in with a hammer, would people claim the hammer was the cause of your death? Is "hammer caused death" the end of the story, or even particularly important to the related series of causes and effects?
She's arguing that it's not, that Silver's cause and effect analysis is irrelevant because his purported cause is purely incidental. Intentionally inaccurate models were simply an instrument designed to further the self-interest of individuals.
(And further, she doesn't need to provide a "proof" to make this claim. But she does offer a compelling argument.)
The easiest way to understand this is that even if the models were accurate, the financial crisis would still have occurred. So blaming the models would be confusing cause and effect regardless of whether you think the models were wrong intentionally or unintentionally.
That said, I didn't read the book so I have nothing to say about whether or not Nate Silver actually does this.
The models certainly contributed. (Note: I have read this far in the book.)
The modeling error in question was independence; that is, if you have five mortgages, each with a 5% change of default, then these can be packaged up as an AAA security as follows: you only lose your money if all five default. A bit riskier package is that you lose if 4/5 default. And so on, each with different returns.
If they are independent, the p(default) = 1/20^5. If they are dependent, it is 1/20. Now multiple mortgage pool size by a 100 or 1,000 or 10,000 (?) and see how far off the estimated risk is. :)
Now combine this with a 30-to-1 leverage when buying these "AAA" securities.
(This was quite a good problem to work through with my daughter to see what that little "independence" assumption means. :)
His main point here was that modeling failures are typically due to out-of-sample conditions; when the housing bubble broke, the markets were might more tightly coupled across the country than the modelers assumed. While they could have seen this kind of dependence if they looked to Japan, there was no such precedence in the US in recent history.
This is a beginner mistake which I have a hard time believing the entire mortgage market failed to spot.
My personal belief is that they knew of the flaw of the models but did not care since their personal incentives were more profitable if the model was not fixed.
As Michael Lewis found in the Big Short. His Liar's Poker (from the 80's) explains the mentality. As he says in the former, he thought the latter would be the be-all end-all.
But the kids in Business School would constantly write him and ask him for new ways to rig the game.
You're imposing a cause/effect dichotomy that doesn't actually exist. Most causes are effects of something else. When we talk about mixing up cause and effect, we mean that A is said to have caused B when in actuality A was caused by B. Showing that A was caused by C does not mean that we've mixed up cause and effect, because nobody said A caused C.
"The bad models were a consequence of a fraudulent system. So by definition, the models weren't the cause of the financial crisis. Therefore the bad models were an effect, in the same way the financial crisis was an effect."
This is faulty reasoning. A pool ball may go into a pocket because it was hit by a cue ball. In this case the cue ball hitting the other ball is the cause of the other ball falling into the pocket. Yes, you can look back into the chain of causes to find one further back, e.g., the fact that a person used a pool cue to hit the cue ball into the other ball. This doesn't mean that the first cause you found was not a cause, and certainly doesn't mean that "by definition".
If a corrupt and fraudulent system could have brought about the financial crisis by some means other than causing bad models (which I assume is true), then you can say that the bad models were not a necessary part of the causal chain. But you can't say that they weren't part of the causal chain at all. As a matter of fact (at least according to assumptions everyone is making in this topic's thread) they were.
I'm not sure you can get to the financial crisis without faulty models. Risk models, especially on packaged securities, are crucial to setting ratings as a proxy for risk. Those ratings are key to determining pricing, maximum leverage, and which portfolios can buy them.
If the models had been more accurate - for example, if they had more properly reflected the way that the value of a bundle of mortgages will suddenly lose value as housing prices decline - banks would not have been able to pile up such huge risks and high leverage.
You can only justify things like 30X leverage if you believe the securities in question have essentially no risk - because at that leverage, a 3% loss in value means you're wiped out.
Okay, but all routes to that gaming - at least at the institutional level - ran through being able to do it without it impacting credit ratings. Credit ratings and security pricing are very dependent on statistical models for risk, in particular correlation and downside outcomes.
I don't think the author is proposing an "A<-B<-C" system. She is stating that the models are really blameless in the situation when the information provided to the models are wrong. The cause and correlation issue arises there.
Silver assumes that the systems fail because the models are bad. O'Neil is instead claiming those are just correlations and not a cause and effect relationship. Basically the models are bad and the systems failed because the people providing the data were corrupt. Using your example: "A<-C" and "B<-C"
It's known as proximate and ultimate causation. [0] An Aristotelian analysis would say the fraudulent system or its corruption was the final cause, the models were the formal cause.
Having just read this book, I believe that this commentary is wrong on nearly all points. Specifically:
- The ratings agencies...did not accidentally have bad underlying models.
He first talks about how the models were defective, and then goes on at length to describe the perverse incentives for developing and keeping these models, and holds the responsible parties to the fire for willful ignorance and unabated greed. He could have made some of these points more strongly, but I don't think that he skirted over the real issues.
- the only goal of a modeler is to produce an accurate model.
Actually he speaks quite a bit about the reasons that various forecasters generate models in the first place, and their motivations and responsibilities. For example, the Weather Channel's skewed "wet" forecasts. They will, for example, predict a 20% chance of rain if their models show only a 5% chance. On the one hand it is a bit dishonest, but on the other hand there is a net utility in doing so. Part of the problem is that the general public doesn't understand what the percentages really signify, so it may be of some benefit to emphasize that it really could rain, it's just not very likely, so it behooves people to at least have some backup plan for rain. The other side of this is that people will not remember the good forecasts as much as the bad, and the Weather Channel has business considerations in mind.
- 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.
How much of the book do you suggest he devote to this? He addressed the issue directly, as the next couple paragraphs state. He also talks to quite a few institutional players at large organizations, both public and private.
- Having said all that, I have major problems with this book and what it claims to explain. In fact, I’m angry.
Really, stop with the faux outrage, because you don't seem all that angry throughout the post, you more seem to be disappointed he did not devote the book to your own pet topics. I find this kind of criticism to be mostly without merit. For me, there were three overriding themes in "The Signal and the Noise": Bayesian thinking (this being the primary one), the goals and motivations of forecasters, and examples of what forecasters have done right and what they have done wrong. I think it covered the bases pretty well, and it was an entertaining and lively read on the whole.
You seem to miss the point of Silver's conflict of interest. He is a Modeler, and has a financial gain in promoting certain views. Among these are (1) the notions that modelers are in general "experts"; and (2) that the intentions of people using the "product" of (1) are benign, or at any rate are not Opportunist. His PR agent would never let him say otherwise, it would be bad for business.
The financial crisis, at its heart was not an issue of "mistaken models". It was driven by (1) bad legislation from washington; and (2) unethical opportunism exploiting #1.
As a general rule, an Analyst at a Credit rating agency is a lowly position in wall street. Most important people (people acting of their own free will) disregard Rating Agency "analysis" out of hand. They do their own work. And people don't get promoted into Goldman Sachs, for example, from S&P et al very often. There is almosts a social stigma attached to the job, that would need to be cleansed with an MBA or some other "success" to pave the way. The people that cannot disregard rating agency work (due to law), of course are a built in market. And in general they are not on an even playing field with Wall Street with respect to their talent pool and access to information. So, bad analysis merely fills a vaccuum.
Using this as one example, think of the consequences. First, the model/analysis of the rating agency is a "product" looking to be sold (like a used car). There are no buyers for even the best analysis (wall st does its own research). So the "buyers" are not wall street but people who are forced to rely on credit ratings by law (eg, pension funds or some other public actors). But this is a captive market. The goal of the rating agency then becomes to game the system by extending the market. That is, creat new "protected classes" or create new "Asset types" that are salable to protected classes of investor. Structurally, now, this is the "game". The models, data-sets, and actual implementaions are really beside the point, provided there is a lack of transparency ("black box", proprietary, just complicated, etc). The models will be reverse engineered to "work", they only need to be "plasuble" (as in plausible deniability). Remember, the rating agencies are <<not legally experts>>. If they were, they could be sued, They are just expressing their 1st amendment rights <<to have an opinion>>.
None of this you seem to address in your commentary.
Nate Silver is just one in a long line of "experts" looking for ways to "sell their services". The first step in doing this is to never undermine the notion of "experts", or the idea that the system is driven by "expert knowledge". As the financial crisis has shown, those people who know what they are doing in the world are operating well beyond the scope and bounds of information that is available to people who publicly claim these hats. There is a reason hedge funds are famously secretive.
This is a really long and angry way of complaining that Silver assumes good faith on the part of those tasked with creating models and policy.
The underlying issue is my biggest peeve with both the buisness and political world. There is a popular viewpoint spread by many defacto authority figures that one should presume good faith from all groups involved, even though everything I can see tells me the opposite.
Did Silver decide to perpetuate that by being afraid to address the topic of malice in his book or did he fall victim to cultural attitudes himself? Either way this rant is all over the place, it complains about Silver mixing cause and effect while itself attacking a symptom, not the problem.
Edit: This post is written temporarily presupposing that the author is correct in her take on Silver's book, the comment by flatline in this thread hits some good points on why she may be a bit off. It was so long ago that I read the book I don't particularly remember how many inches Silver dedicated to incentives let alone care to debate if that was enough given the goals of his book.
> The underlying issue is my biggest peeve with both the buisness and political world. There is a popular viewpoint spread by many defacto authority figures that one should presume good faith from all groups involved, even though everything I can see tells me the opposite.
This is a pretty big deal.
Assuming good faith by authority figures is a popular and common fallacy from people who sit in the "Lawful" quadrant. N.b., I see this a lot in academic circles. Arguing from authority is the M.O. there, and assuming from authority is the consequence.
There's also assuming bad faith, a equally fallacious and (at least equally) popular idea from people who sit in the "Chaotic" quadrant.
We have to remember to have nuance in our opinions and dealings with others.
I agree with you that the heart of the objection is in Silver's presumption of good faith.
That being said, the author has previous experience working on Wall Street as a quant, and is involved with the OWS Alternative Banking Group. She certainly comes with a strong perspective on the matter, but neither is she arguing out of ignorance of the system.
Is it possible that Silver doesn't actually presume good faith, but merely says in his book "okay, if you claim that we should presume good faith, here's what you would have to do to fix the problems"? Maybe it's Silver's backhanded way to express that presuming good faith is a lie perpetuated by authority for their own benefit?
[[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.]]
Ahhh, so she was part of the Occupy movement and comes from the world-view that the financial system and government is corrupt. She should have said that up front. Makes what she is saying make more sense.
Nate Silver doesn't believe those things, and so that largely explains why they come to different conclusions.
She has a more reasonable, and more specific, point than that.
The naivete of which she accuses Silver is that Silver assumes that the modelers are striving for accuracy in their models (and that they failed to achieve accuracy). With some linked evidence, she asserts that the modelers in the financial industry knew that their models were inaccurate, but that those models supported the corrupt narrative that enriched them, and so perpetuated them. In other words, in the finance world (she says, with some apparent understanding and evidence) the wilfully inaccurate models were a means to a corrupt end.
I disagree that she had reasonable points. I don't see how malice played a significant role at all in the meltdown. Carelessness, bad incentives, and etc. - sure. Maybe a bit of malice. But most of the misallocation of capital was done by firms doing things like trusting AAA rated securities when everybody else was doing the same and it was in their financial interest to do so. Actions like this were not wise, but it's hard to see malice if you're not a card-carrying Marxist.
She doesn't attribute malice to anyone. She describes the belief that the issue with models in the meltdown was inaccuracy as "maliciously" wrong, which I take to be hyperbole on her part to emphasize the category flaw she sees in this (namely, that if it's just inaccurate models, then better mathematicians are what's needed; in her view, the financial system at the heart of the meltdown was "corrupt and criminally fraudulent", so it's not mathematicians that are needed, and concentrating on them allows criminals to go free).
The fundamental problem she sees is that incentives in the financial industry are not aligned with accurate models, that inaccurate models were deliberately used to further short-term performance at the expense of further inflating the bubble.
"most of the misallocation of capital was done by firms doing things like trusting AAA rated securities"
On this, she links to specific evidence that the "trust" they were exercising was knowingly misplaced because the incentive was always to get the next big bonus.
Until any contrary evidence, all Government is corrupt by definition (and yes, I call lobby-led politics "corruption"). In a true and free market "finance" is not corrupt, if you can't pay your debts you're out of it, this has been true since this industry was first invented by the Italians around the 1300-1400s.
In this latest crisis the problem was that the financial market was mostly led by Government-mandated decisions, starting with the Bear Stearns rescue in early 2008.
Where do I start? Hobbes or Machiavelli? More to the point, I do not believe that people/humans are "good by definition", there needs to be a higher thing that regulates our behavior so that we will not do harm to each other. Hobbes believed in the "Leviathan", there are other people that believe that the magical "assembly" of some of their representatives (a "central" Government, if you will) would make the latter more responsible and less eager to look for their own good and prosperity, and then there are people like me who believe that the free market is one of the best tools in solving the inherent conflicts between humans.
Like they say, money has no smell. Anyway, it's a very long discussion :)
Really? No higher anything tells me to be a good person, I do it because it's the right thing to do and I've seen pain and wouldn't want people to experience it for no reason. Maybe you need someone/something to tell you what's right and wrong, but not everyone does.
In replying to @jeremyarussell bellow, because somehow my comment is being down-voted to oblivion and I cannot reply directly:
What can I say? To each his own. I did not say that you, jeremyarussell, or any specific person taken "per se" needs a higher thingie to stop him/her from committing any crimes, it's just that for example the 20th century has been marked by some of the most heinous crimes in our existence as a species while in the same time only a small percentage of our fellow humans are psychopathic or sociopaths or however else you want to call those who commit crimes for pleasure only. Somehow, to make up for the difference (in the "people killed vs. natural-born-killers" ratio), I believe that we have somehow to look deeper into ourselves. Also pls. see this: https://en.wikipedia.org/wiki/Banality_of_evil.
And now that I'm reminded of Hannah Arrendt and the crimes of the 20th century, and for once to make a documented reference to Godwin's law on the internets (and to give people real reasons for down-votes), there's this little photo right here (http://imgur.com/PfM0e) of the Tiraspol (https://en.wikipedia.org/wiki/Tiraspol) train-station taken during WW2 by a German soldier. I paid around 0.50 euro-cents for the photo in an antiques shop (I live in Eastern Europe), around with other photos taken by the same soldier on the top of Eiffel Tower in occupied Paris or on the shores of the Aegean in Thessaloniki.
Now, those train tracks that you see in the photo at first don't signify anything by themselves, what with the peasant women waiting patiently for the next train or what-have-you. But if you happen to know (like for example I do) that tens of thousands (if not hundreds of thousands) of Romanian Jews (Romania being the country where I grew up and currently live in) were carried in trains to their final destination to close-by Transnistria (http://isurvived.org/Transnistria.html) then you begin to see things differently.
It took my country's Government close to 60 years to finally starting to acknowledge that we actually did exterminate people in Transnistria. Up until not that long ago there still was a statue of Ion Antonescu (https://en.wikipedia.org/wiki/Ion_Antonescu), the guy who ruled this country when all these, let's say really bad things happened, in a church's front-yard a couple of metro stations from where I'm now writing this comment. In fact, if I were to take the tramway to work tomorrow instead of the metro I would pass right by the said church (no statue of him now), which Antonescu actually helped build (the church, that is). Do you honestly think that church-building Ion Antonescu thought of himself as being a bad person?
And this is one of the many reasons why I think Governments do not deserve our trust.
I keep thinking of the crusades as a prime example of higher power gone wrong(and just how bad humans can get, and also that happened way back when and all). It's just interesting to me though that you say we need a higher power, and that higher power is often the government that is corrupt.
No worries about rants, I like reading them and trying to get myself to not just see another's point of view, but to own it, try and make it mine and get an even deeper sense of what's going on behind the words. And sleep well.
Having spent part of my career in finance, the part she saying about modeling in that industry is true for some situations but not others. (Unsurprisingly.) An insurance company is generally going to try to get its own premium pricing models right, for example. But if the point of the model is to sell someone on something, than look out. I remember a senior banker saying to me "model this merger, and make sure it comes out to be accretive by X cents per share." It was irrelevant to him if the model was accurate - he just wanted ammunition to convince his client to do the acquisition.
The villains in this story (ratings agencies) had lots of models, bad and good. They chose to publicly report the ones that suited their interest, rather than the most accurate ones.
The way to improve the situation is to educate the consumers of those models (securities purchasers) about what models are best, and which are misleading.
It's more productive to say, "Buyer beware, sellers are misleading you in the following ways..." than, "Shame on corrupt sellers!". Silver's book is doing the first, which does not constitute defense of corruption.
Her criticism boils down to, "but there are agent-principal problems!"
But I think that's a bit of a sideswipe at Nate, who is dealing with a different domain of problem: modeling inaccuracy when the incentives ARE aligned in favor of optimizing predictive accuracy.
But then the financial crisis is not within the domain of problem Nate is dealing with, because, as mathbabe says, the incentives were not aligned in favor of optimizing predictive accuracy. So Nate should not have talked about the financial crisis at all, yet he still did.
Bayesian methods are the best for people who want to turn de-biasing into re-biasing, particularly when you're dealing with lots of output variables. (Generally when the variables are few, as they are in the things this document talks about, a screwy prior sticks out like a sore thumb.)
Sometimes the distribution that you ~can~ sample isn't really the distribution that you wish you could sample, and sometimes changing the prior in such a model is a way to make it behave as if it was sampled correctly to begin with.
Silver attempts a dispassionate analysis. Her post simply asserts what she believes to be the problem with the housing bubble and ensuing meltdown, without much evidence or analysis.
She may be right. But Silver's larger point is evidence-based analysis. Where's the evidence to support her position? Why is her assertion any different than the political pundits' assertions? Is an email exchange between a couple of traders enough to prove global complicit awareness? There may well HAVE been global complicit awareness. But without enough data to statistically support her position, this response seems to prove Silver's point that we're better off looking at the data than we are working off what we 'know' is right.
This is anecdotal, but I'll posit it anyway. I did network administration for a number of title companies, mortgage lenders, real-estate agents, and other assorted firms involved in the selling and buying of houses between 2004 and 2011. Most of these people were what you would consider hard working honest employees, but due to perverse incentives helped in their part of making all of this worse. I saw plenty of lenders have the client flat out lie about their income. It didn't seem to matter, the banks rarely rejected the loans if the paper looked good. The better you lied, the more sales you got. If you were totally honest, people heard that it was hard to get a loan or closure from your firm and would head to others. At the time there was seemly no risk, it wasn't till after the financial crash that I heard of any arrests over paper manipulation. The problem is the people that have the data now are going to be very shy about releasing it. It will show fraud by the buyers, possible fraud by the lenders, poor research by the big banks. By the time the crash came it was a game. Interest only loans? You've got to be kidding me.
At a murder scene, evidence is how one determines the cause and the killer, but if your killer has means he can manipulate that evidence. The issue we have now is that the murder (banks) are the group holding all the evidence. They don't want it looked in to, it would show they were an accomplice.
Amazing how willing the poster is to criticize Nate Silvers based on assumed conflicts of interest and naivety about his own incentives mislead him, while remaining silent on her own.
> "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."
And of course, she derives also derives her income from speaking, consulting, and is writing her own book. She certainly benefits from positioning herself as a more expert Nate Silvers. "This best seller is wrong, buy my book to find out the details why" is pretty effective marketing.
By her own logic we should criticize her just as strongly.
Hmm, i should read this book, I probably have a different perspective. I worked in the banking group at one of the 2 major rating agencies, then structured some large ABS transactions at Merrill Lynch and at one of the largest issuers, but haven't worked in finance for a while
Analysis and modeling should always be free of dogma. Otherwise you will (as the other of this post demonstrates) start using your analysis and modeling to justify and prove your dogma (rather than to understand). The fact that Silver's analysis and modeling did not prove her point does not invalidate it. In fact, you could argue that the root of her "anger" is that his book proves her beliefs to be at least unproven if not entirely false.
From someone involved in real estate, real estate finance, stocks, venture capital, financial modeling, and behavioral finance (since before that phrase was even coined), who sold his home and three other properties from 2004 to summer 2008 and rented so as to exit before the crash, all these bubbles and crashes are, IHMO, not about modeling, they are about greed, fear, conflict between self interest on collective interest, and perverse incentive and compensation systems (in many areas, from politics to lobbying to regulatory to finance to sales to brokerage to legal to money management to many (all?) of the other important things for finance-related models). None of those things have changed materially. Nate can build the fanciest financial asset/credit/price model from his wettest dream and it will fail, probably at the worst possible time, such as once you go all in on it. Many reasons for that. But I bet he could get a job on Wall Street pumping out models that back up what will make the firm the most amount of money in the short term, damed the best interests of pretty much anyone, the firm included, in the long term. See Goldman Sachs :)
> 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.
Those who are attracted to financial careers often have a deep and abiding love for money, much like wolves have a deep and abiding love for sheep. Sheep farmers are smart enough to not to hire wolves as shepherds; but in the financial industry, the wolves are already in charge.
The whole "Nate Silver makes pundits look bad so pundits will all grasp at straws and twist his words to try to make him look wrong so they can feel relevant again" act is getting a tad old.
I agree that Nate Silver's book is short on many subjects (such as politics) but the goal of his book is not to explain "Why did X happen" but simply to discuss the challenges of prediction within different domains. It's a book that helps people learn the art/science/challenges of prediction.
Saying that his book lacks detailed discussions of incentives (while true) misses the point of his book.
It's not surprising that the banks that "won" the financial crisis should have had an incentive to use bad models. But there were more losers than winners, and for the losers this was a straightforward matter of their models being wrong when they didn't want them to.
"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."
A= Financial Crisis B= Bad Models C= Fraudulent System.
Nate said "A<-B" Author says "A<-B<-C"
That is not a mix up of cause and effect.
Author's main complaint seems to be that Nate assumes bad models are an accident, and Author claims they were intentional.
Again, not a mixup of cause and effect. At worst it's a naive interpretation of the correct cause.