Bad models or bad modellers

by John Q on November 9, 2008

The idea that bad mathematical models used to evaluate investments are at least partially to blame for the financial crisis has plenty of appeal, and perhaps some validity, but it doesn’t justify a lot of the anti-intellectual responses we are seeing. That includes this NY Times headline In Modeling Risk, the Human Factor Was Left Out . What becomes clear from the story is that a model that left human factors out would have worked quite well. The elements of the required model are
(i) in the long run, house prices move in line with employment, incomes and migration patterns
(ii) if prices move more than 20 per cent out of line with long run value they will in due course fall at least 20 per cent
(iii) when this happens, large classes of financial assets will go into default either directly or because they are derived from assets that can’t pay out if house prices fall

It was not the disregard of human factors but the attempt to second-guess human behavioral responses to a period of rising prices, so as to reproduce the behavior of housing markets in the bubble period, that led many to disaster. A more naive version of the same error is to assume that particular observed behavior (say, not defaulting on home loans) will be sustained even when the conditions that made that behavior sensible no longer apply.

But at least this is criticism of specific models. What is really silly, on a par with saying “evolution is just a theory” is the currently popular talking point “this shows you shouldn’t trust models, so I can consult my own prejudices on topic X (most commonly, climate change)“. Any attempt to predict the future behavior of a system requires a model of that system, whether it’s explicit or implicit, complex or simple, solved with a computer or by assertion.

In the case of the bubble, the crucial determinant of model failure was not complexity or simplicity. It was the presence (or, for those who predicted a that the bubble would burst, absence) of the assumption “house prices always go up”. Of course, this assumption was much easier to detect from talking to an amateur speculator than in analyzing a synthetic CDO, but it had the same effect in either case.

More generally, in most cases, the headline result from a large and complex model can usually be reproduced with a much simpler model embodying the same key assumptions. If those assumptions are right (wrong) the model results will be the same. The extra detail usually serves to produce more detailed results rather than to produce significant changes in the headline results.



a 11.09.08 at 9:28 pm

“It was the presence (or, for those who predicted a that the bubble would burst, absence) of the assumption “house prices always go up”. ” I’m skeptical of this claim. IBs test their positions with all kinds of scenarios : surely they had at least several scenarios where housing prices go down. I imagine the problem is that they thought that, once the prices of their products started to go down, they could still unload all of them at the then-current price; instead, the market turned illiquid (the spreads widened and the bid prices were just too low or non-existent, as everyone was running for the exit at the same time).

In any case, I think it’s a fine line between (1) saying the models were wrong; and (2) saying the models were right but wrong assumptions were made about one of the parameters, housing prices. In any model involving many parameters – which includes pretty much any model of complex behaviour – some simplifying assumptions need to be made about the parameters, or else the number of possible scenarios become intractable. Is that the fault of the model? the parameters? Does it matter, when the end result is that complex processes are modeled incorrectly?

One should remain skeptical of models and the assumptions of a model, until they tested in both good conditions and bad. Holds for finance, holds for economics, holds for climatology. Be skeptical.


magistra 11.09.08 at 9:37 pm

But wasn’t there a more basic problem with many of the risk models, that they assumed that the risk of each transaction was independent of each other? You can’t possibly model the correlations between 1 million mortgages or other financial transactions, so you have to assume they’re independent of each other, even though they’re not. People going bust or defaulting on their mortgages mean more people are likely to, because there’s a knock-on effect. It’s like assuming that because the probability of any individual apple being rotten is one-tenth, the probability is that one-tenth of the apples in a barrel are rotten, and forgetting that rotten apples in a barrel cause others to rot.


Barbar 11.09.08 at 10:18 pm

You can’t possibly model the correlations between 1 million mortgages or other financial transactions, so you have to assume they’re independent of each other, even though they’re not.

It’s not easy to model the correlations well, but that doesn’t mean you’re required to assume independence, and I’m not sure that that was really the problem here. Whether or not a model should reflect the correlations depends on what the model is used for. If you’re trying to come up with a fair price for a certain package of mortgage securities, the correlation between the individual mortgages may not be especially critical; if you managing the downside risk of your investments, then it’s absolutely essential. Risk managers have been aware of correlation for a very long time.


ab 11.09.08 at 10:41 pm

All the models have built in correlations—using some common factor capturing a ‘house price index’ that drives many mortgages. So there is a correlation. The the distribution of that common factor in the models had too small a tails relative to the truth. But you cannot measure tails with any accuracy anyway.

The models did not assume independence. And some people who were modeling the products were thinking about a bubble-though no one thought that there was going to be such a big crash. But there was no historical experience with such a big crash, and the models were typically based on ‘curve fitting’ rather than thinking through the basic supply and demand relationships in the markets (which apparently had broken down over the last few years, anyway).

I wonder too if the decision makers were too removed from the modelers. And, I bet that if you asked the to executives of the investment banks the question: How sensitive is the total value of the firm to the Case and Shiller house price index, they couldn’t tell you in real time. Model based or not.

I heard rumors that one big bank did not know their own overall exposure to real estate, themselves. That bank is no longer in business.


Adam 11.09.08 at 11:20 pm


Wasn’t that presumed independence exactly the point? Mortgages payments were pooled together and then the rights to the first x dollars from the pool were sold as high level securities and the rights to the last y dollars from the pool were sold as junk.

That model only works if the mortgage payments have a payout probability independent of each other. If not – if something causes all those mortgages to default at once – you’re screwed (as was the case).


Greg Ransom 11.09.08 at 11:33 pm

Let’s be frank about where the anti-intellectuals begins and ends here.

It begins and ends with the deeply mistaken pseudo-scientific picture of “science” that lies behind the explanatory strategy of the “rocket scientists” in economics, a false picture falsely applied to economic phenomena.

Hayek invented the word “scientism” exactly for purpose of speaking about economists who pursue knowledge premised on this false picture of “science”.

If economists had a sound sense of the limitations of these “models” — and thereby a sound understanding of what these models can and can’t due, I’d have some confidence that this sort of calamity wouldn’t happen again.

But I have no such confidence.

The public is right not to trust economic understanding — and the economy — to fake scientists pursuing a pseudo-scientific model of “science”.


Bunbury 11.10.08 at 12:10 am

IBs model lots of things and interpret those models lots of different ways for lots of different audiences. Many are produced as sales aids. The model that prevails is the model that gets deals done. It is unlikely to be the one that tells everyone to go home. Investment banks were also new to lending — even a decade ago they didn’t really have full access to wholesale money markets and their balance sheets ballooned in the last few years before they burst. The right choice of model would not have been obvious and would have had to overcome models bringing in a lot of business.

Magistra, Adam — What Barbar said. Correlation has been a routine part of C(D/M/L)O pricing for some time. There are potential flaws with, for example, using Gaussian copulae and the models might not have fully reflected the risk of house price movement so correlation estimates may be flawed but these are much more subtle issues than assuming idependence and it is not sure that these problems are more serious than “garbage in garbage out” and inability to interpret the results.

Human factors have been included for ages. Pricing mortgages has involved making assumptions about the extent to which investors will not take full financial advantage of the options included in most US mortgages and refinancing in general. Indeed it is unlikely that an investor assuming full financial rationality on the part of borrowers would ever find themselves investing. Any appeal to behavioural factors will have to be more specific and also do more than calim to have said down when something else was saying up.


bab 11.10.08 at 12:31 am

While it is undoubtedly true that media coverage on the subject is suspect (quelle surprise), I remain stupefied by the decisions made by these institutions.

These models are supposed to give you some sense of the amount of risk that is associated with the assets your institution is holding/trading, right? Did everybody’s model suck at this? Or is it that the IBs (and others), to one extent or another, made very bad bets given what the models were telling them?

Moreover, I realize that we’re at historically very high levels of mortgage defaults, but isn’t the percentage still fairly low compared to all outstanding loans? (The figure for “troubled” loans is somewhere in the teens, isn’t it?) So what kind of model bankrupts an institution based on a default rate differential of 10 or so percentage points from normal?


PHB 11.10.08 at 1:44 am

You don’t need to model the interactions between the mortgages, you need to model the system that the mortgages are a part of.

Insurers consider the probability of claims being dependent on each other all the time – Lloyds remained solvent after 9/11 because they had always considered two jet airliners colliding over NYC as their liquidity event they had to be able to meet.

In this case the insurers models demonstrated that the issuers of the ‘collateral default swaps’ were insufficiently capitalized from the start. That is why they are called default swaps, not insurance.

My business involves being an insurer, we have a pretty unique risk in that we have issued over a million policies with a maximum loss of a million dollars each and never seen a single claim in thirteen years. But we know that if we do see claims they are going to be highly correlated.


J Thomas 11.10.08 at 1:46 am

Compare this to our experience in vietnam. It was pretty clear that we weren’t heading for a good result. The north vietnamese were pretty dedicated to their government and their war for whatever reason, while the south vietnamese weren’t — particularly after the second coup. But we hung on because nobody who could say it was time to cut and run was willing to make that choice.

Now imagine that we were making great profits in the short run…. How could they have persuaded themselves to quit while it was still profitable?

I remember listening to some people involved in a university trust fund. They were supposed to do socially responsible investment during vietnam, and a manager had bought Dow Chemicals. This was wrong because Dow was making stuff for the war. And one of them was saying, “…and not only that, but they bought it while it was going down.”. After months of wrangling they agreed that they had to sell it. And that same guy complained, “….but they sold it while it was going up.”.

Compare with the history of insurance companies selling hurricane insurance.

I think maybe given the way our institutions work, risk analysis works better for deciding whether to *stay out* of a venture than for deciding *when* to get out of one you’re already in.


virgil xenophon 11.10.08 at 2:12 am

Once again J Thomas zeros in on the institutional psychology of the decision-makers
to good effect. Witness GM going bankrupt because no one is buying their cars because they cannot get financing (among other reasons) even as the credit arm of the company, (GM Acceptance) driven by attorney’s worried more about regulators than the health of the company that employs them, tightens credit requirements (credit score of 700+) to effectively prohibit anyone from buying their products except those capable of buying a Rolls-Royce–in which case they would not be in the market for GM products anyway.


david 11.10.08 at 2:51 am

as far as I know, nobody modeled the incentives tied to mathematically pretty fee machines. The bubble blew and popped on fee machines, and a false hope in risk management. Maybe you have to model things, but power’s one of those things, and so is greed. We’ve stopped talking so much about the fee machines, but that’s where the tale of the bonuses that screwed us lies.


Barry 11.10.08 at 3:00 am

Personally, I think that the problem all came down to the guys up at the top reaping 2-digit ($ millions) in annual bonuses for their ‘profitability’, and telling the middle and lowers what to assume. Those bonuses wouldn’t (and won’t) be clawed back in later years, if those ‘profits’ were all moonshine.

Follow the incentives.


Matthew Kuzma 11.10.08 at 3:23 am

I don’t know what this is supposed to say, but I can’t parse it.

It was not the disregard of human factors the attempt to second-guess human behavioral responses to a period of rising prices

D’oh! A missing “but”! Fixed now, I hope- JQ


Cycledoc 11.10.08 at 3:40 am

Models, correlation, probability, behavioral responses– all this seems to be very sophisticated B.S.

If someone had simply looked at the loans–no down payments, interest only, no qualifications, etc. Simply applying occam’s razor (All other things being equal, the simplest solution is the best) would have predicted the current catastrophe. We were all in collusion and denial.


J Thomas 11.10.08 at 4:49 am

Cycledoc, anybody could see that the thing was not sustainable. But hardly anything is. Once you’re in, the question isn’t whether to get out, it’s when to get out. Unless you have an alternative that’s almost as profitable, you can lose heavily by getting out too soon.

So they stayed in because nobody had a good rationale to say precisely when to pawn it off onto somebody else. (Or maybe some smart guys in some companies did just that. They got out in time, they got all their bad debt safely sold, and perhaps they even weathered the generalised problems. Maybe one of them will write a book.)

The obviously-bad mortgages at the end should have told people that they were down to the dregs, the keg was almost empty. But also by that time it was all so big — who could the big players have sold to except each other? And if one of them did try to unload much, that might be what made it all collapse.

I’ve seen the claim that the chinese, japanese, korean etc banks who have so many US dollars are in that same bind. If they admit their losses they lose big right then. If they try to unload dollars they get a crash and have to admit it. So they keep buying in the hope that some miracle will happen. I don’t know that it’s true, but it’s the same story in a different context.

This sort of thing happens all over. In a casino it’s easier not to make the first bet than it is to stop. Far easier not to take an addictive drug in the first place than to decide that it’s time to go through withdrawal. Etc.

And of course it isn’t enough to model your own business. You have to model the whole industry, and you don’t know in detail what everybody else is doing.


Michael Turner 11.10.08 at 4:56 am

Could it be we owe much of our current predicament to the following, uh, “algorithm”?

(1) When market prices soar above model predictions, use mark-t0-market.
(2) When model prices soar above market prices, use mark-to-model.

(Substitute “sink below” for “soar above”, depending on the kind of financial instrument and how it yields. I’d like to say I’m sort of joking here, but with an automated feed for market prices, it would probably be hardly more than a half-page of code to automate this “algorithm” as a wrapper around existing modeling code, so that the trader at his workstation doesn’t even see a difference on the screen.)

Maybe this is how they did it at Fannie Mae? See WaPo story excerpted below. Of course, Fannie and Freddie have been fingered by conservative ideologues trying to establish some sweeping soshulist-gummint-did-it rationale for this crisis. How convenient. Perhaps a better case can be made for the proposition that we can learn something from the record of Fannie Mae (more regulated and thus more transparent) as it tried to play catch-up-game in the casino. One thing we might learn is that many of the big financial corporations were probably optimistically second-guessing their own models when those models indicated more prudent trades.

Susan Woodward, in “Rescued by Fannie Mae?” (WaPo Oct 14, 2008), suggested that we should actually use Fannie Mae’s existing models as a way to get home pricing on a better footing.

Fannie has an underwriting and valuation shop with models for valuing mortgages that are up and running. Key inputs to these models are Fannie’s own indexes of property values at the Zip code level — others make do with prices in entire urban areas. The data needed are not difficult to assemble: current loan balances, the date loans were originated, original property value (or appraisal for refinances), loan type (fixed, adjustable rate, ARM features, loan length), borrower’s credit score, Zip code and current loan status. These models project payments and proceeds from foreclosures and calculate the property’s present value. Similar but less detailed models in recent academic work show that they do quite a good job of projecting defaults for prime and subprime loans, given changes in property values.

Some argue that Fannie is discredited for this work because it, too, has losses on riskier mortgages. But Fannie’s losses arose from a failure to reserve adequately for losses that were anticipated by its models. Fannie’s business people overrode the risk managers when making the decision to keep reserves too low. The models were right.

It’s disturbing that modeling (and complex financial engineering in general) is being targeted so indiscriminately by the punditocracy. No doubt there were some bad models and some financial instruments whose complexities obscured their risks and flaws, but there’s still a lot of baby that would go out with the bath.

I’ve even read about how the problem is “derivatives”. Not even “complex derivatives.” Just “derivatives.” Jeez. So we should get rid of commodity futures markets, too, eh? Compared to this, Ned Ludd was positively nuanced.


notsneaky 11.10.08 at 5:34 am

John’s exactly right here.

He is right when he says

“The idea that bad mathematical models used to evaluate investments are at least partially to blame for the financial crisis has plenty of appeal, and perhaps some validity, ”

and even more right

” it doesn’t justify a lot of the anti-intellectual responses we are seeing.”

and in terms of human psychology, exactly right when he says

“What is really silly, on a par with saying “evolution is just a theory” is the currently popular talking point “this shows you shouldn’t trust models, so I can consult my own prejudices on topic X”

which Greg Ransom seems to be very happy to illustrate perfectly.

That’s the thing about outlier, unusual and unanticipated events (and I mean, specifically unanticipated, not the general “make doom-and-gloom predictions every year for 40 years (whether it’s “capitalism is inherently unstable” or “Gov regulation will cause civilization to collapse”)) and then when something bad happens after 40 years jump up and down and pee yourself screaming “I told you so, I told you so”). Everybody can claim that these events debunk whatever it is they would like to be dubunked because it’s such a rare opportunity which applies to everything and nothing in particular.

That doesn’t change the fact that any sensible model still needs something like i) through iii).


nnyhav 11.10.08 at 6:05 am

The models used for valuation and risk (and ratings) were parameterised against market inputs (other prices) not by economic scenarios. The latter would determine how parameters would be perturbed, but could not calibrate the extent of the perturbation, which went well out of range of where the models had been (or could have been) tested.

The lack of due diligence on underlying loans and the feedback bubbling of housing prices, attributable to way easy credit and strong demand for securitisation, were exogenous to models whether for securities or for economies. But plenty of human economists picked up on it. (Some argued the circle was virtuous, others that viciousness was just around the next turn.)


Cranky Observer 11.10.08 at 1:20 pm

> That’s the thing about outlier, unusual and unanticipated events

I can’t remember off the top the name of the bridge failure which caused Eads to change his models of iron bridge stress (and the Roeblings to be far more conservative in their design of the Brooklyn Bridge), but the defenses here of the financial models used from 2000-2008 strike me as if the Eads, Roeblings, etc had ignored every physical (and lifetaking) failure before them and barged ahead with their preferred models regardless of the actual physical world. That didn’t happen; Eads in particularly was known to have absorbed and learned extensively from previous failures.

Of course he was dealing with the immediate physical world, a concept which seems to have disappeared from Wall Street about 1990. “Outlier event”? I drove through the Inland Empire from 2002-2006 scratching my head and wondering exactly how the number of 1200 sq ft townhouses built on desert scrubland and priced at $450,000 could continue growing at 5 times the rate of Los Angeles’ population and job growth. But I guess no one who worked on these “models” bothered to check little things like your objective reality.

I put “models” in scare quotes because it is quite clear (and was clear at the time) that these “models” were just cream-skimming fraud justification machines. Which in all honesty did their job quite nicely: the Wall Street princes who caused this mess are safely off in the Connecticut mansions with $100 million in Swiss accounts. Too bad about us working stiffs.



Alex 11.10.08 at 5:38 pm

Quite a lot of them certainly did use an assumption of independence; the whole point about overcollateralisation is that if the definition of a credit rating of X is a probability of default of Y per cent, and the average default probability of the loans in your bucket is Z (where Z<Y), to achieve a rating of X you toss in (Y-Z) per cent more loans above the face value of the security.

This has an implied assumption of independence whatever you do, because it’s only in the scenario where each default event is independent of the others that putting more of the same collateral in the pot will fix it. Otherwise, the extra loans (Y-Z) will just default at the same higher rate as the main pot, leaving you – or more importantly the buyer – screwed.


Alex 11.10.08 at 5:39 pm

where Z>Y, obviously.


Alex 11.10.08 at 5:42 pm

Actually, CT mangled that post pretty badly. Try again.

The whole point about overcollateralisation is that if the definition of a credit rating of X is a probability of default of Y per cent, and the average default probability of the loans in your bucket is Z (where Z < Y), you add (Y-Z) per cent more loans to the pool over and above the face value of the security.


mpowell 11.10.08 at 8:04 pm

I don’t see much disagreement between J Thomas and Cranky’s answers here. The behaviour of the relevant players made sense given the conditions they were operating in. The best way to prevent this kind of crash is better regulation of the market.


Bunbury 11.10.08 at 8:11 pm

Overcollateralisation is just having more nominal collateral than you need to meet your obligations. Overcollateralisation is, as suggested, used to boost the credit rating of portions of CDOs but even the rating agencies would use more elaborate maths than suggested above. The typical approach would be to come up loss distributions for each asset and then glue them together with a copula to give a combined loss distribution, that is explicitly not assuming independence. There are many inadequacies in this approach, some similar to the shortcomings of assuming independence, but it is not assuming independence which would be something quite specific. This approach is almost as well established as Black-Scholes and its limitations, smiles and what have you as well known. Nevertheless you might do quite a lot of hard work and still not have a firm grip on the role of house prices in your risk.

Also models tend to be tuned to particular risks. When the market is terribly competitive and things are good, focussing on tail risk might well lose you money in the short term. In the past that would have been fine for investment banks because they would have been long gone by the time there was trouble but that changed in the last few years and it is possible that the situation changed faster than the use of models. Pricing and risk management aren’t exactly the same.


J Thomas 11.10.08 at 10:05 pm

I don’t see much disagreement between J Thomas and Cranky’s answers here. The behaviour of the relevant players made sense given the conditions they were operating in.

I agree. Part of the problem is that the market gives no hint how much the future should be discounted.

The best way to prevent this kind of crash is better regulation of the market.

Then we depend on good regulation. I’d like to see a clear basic explanation about how to do good regulation and how to avoid bad regulation. We need an excellent philosophy of regulation or else we’ll keep doing it badly.

Here’s a very quick sketch of a start at that. Economies and markets are games, played by rules. The legal system does not regulate such things well at all — taking someone to court is like an act of war, you can’t much hope to come out ahead, the best you can hope for is to hurt the other guy worse than you get hurt yourself. Libertarians who want to allow people to do pretty much what they want subject to lawsuit in courts similar to US courts, are being disingenuous.

One good way to regulate things would involve setting up clear rules for the game. The rules should be simple enough that people understand them easily. They should be easy to enforce. Players of the game should have an incentive to turn in cheaters, while at the same time they should have little incentive to falsely turn in innocents as cheaters.

The game and all its parts should be designed to promote the values we want, which include among others productivity, fairness, the continued existence and prosperity of the game, and an open invitation to those who are not playing or who are not playing hard to get more involved.

I would suggest as one minor detail that fractional-reserve banking should be illegal when performed by private entities rather than the government. When a US state charters a bank they give that bank a license to steal. As another minor detail, there should be careful study when it is better to have private monopolies that manage markets, versus government agencies whose job is to manage those markets. What are the important criteria to decide this question?


Chris 11.11.08 at 12:46 am

Fannie’s business people overrode the risk managers when making the decision to keep reserves too low. The models were right.

This sounds oddly reminiscent of the Challenger (yes, the space shuttle). The engineers were concerned about cold-weather performance of the solid-rocket-booster seals, but were overruled by the managers, who decided to launch anyway. Boom (literally). And the whole thing (well, not the disaster itself, but the decisionmaking that led up to it) might have been covered up if not for one oddball physicist.

Imagine you are working for an investment bank and you put together a new model in, say, 2005, predicting that within a few years, declining real estate prices will prevent the continuous refinancing of these complex loan instruments, leading to higher default rates and an industry-wide death spiral of securities devaluation. Your bank is currently exposed enough to become insolvent if this happens. Do you take that model to your boss? How do you expect he would react if you did? Fire you and look for someone with a positive attitude who works well with others, would be my guess.

Maybe we need to give a little more respect to negativity.

P.S. HTML blockquote doesn’t show up in the instant preview, so I added the quote tag too. We’ll see how it comes out when posted.


christian h. 11.11.08 at 3:43 am

The problem with models is that they aren’t perfect. They work fine when employed, for example, to actually hedge a risk. But when they are used to make high returns on leveraged investments, their non-perfection will sooner or later lead to trouble.

I suppose when someone builds an airplane, they rather err on the side of caution. The same did (and does) not apply to financial models – as someone wrote up-thread, the model wouldn’t sell if it is too cautious (leverage makes slight differences in risk assessment into potentially large differences in returns).

By the way, saying “the models didn’t take the human factor into account” is of course absurd on its face – the models are produced, and employed, by humans in the first place, after all.


Zamfir 11.11.08 at 9:51 am

Christian, funny that you mention aircraft design. Long-range flight, and space flight even more, does in fact have a significant multiplier effect: if you diminish weight (or something that can be trade-off against weight) by a kilo, you need less fuel, which in turn leads to a lighter structure, etc.

The result is that aircraft design indeed has much smaller safety margins than most engineering disciplines, and a much higher reliance on modelling to get every bit of performance out of a design. Still, aircraft have become extremely safe, while the financial world has completely lost control. It might be interesting to think why this difference exists.

My suspicion is the lack of (non-oligopolic) competition in aircraft industry. If there were a thousand aircraft manufacturers, including “hedge manufacturers” that rebuild existing planes to other specs, some more safe than others, a race to the safety bottom would be much more likely. As it is, companies gladly oblige the strongest safety regulations, because they know their competitors and know they adhere to the same rules, so they can pass on the cost to customers. The downside is massive overspending on safety, on the order of dozens of millions of dollars per life saved, much more than in most sectors.

Another aspect is PR: the aircraft industry knows that if flying was as safe as driving a car,
there would be massive crashes every few days, hurting the image of flying beyond repair. The financial world seems to fear such bad PR a lot less, perhaps because there are no human lives directly at stake, or perhaps it is less clear where to put the blame for financial crises.


virgil xenophon 11.11.08 at 9:52 am

Chris is right, the boss’s attitude is typically to hope that he will be retired , living in Sarasota, and checking out the performance of the stocks in his 401k in the WSJ at McDonald’s over morning coffee before hitting the links–all well before the shite hits the proverbial fan–if it ever does…….(thinks the boss)


Michael Turner 11.11.08 at 11:07 am

Chris@27: One of the better analyses of the Challenger disaster was Diane Vaughan‘s, in her The Challenger Launch Decision: Risky Technology, Culture and Deviance at NASA. She described the basic social mechanism as “normalization of deviance” — the tendency, as time goes on without a disaster, to ignore warning signs that might have been initially troubling, even alarming.

Normalization of deviance is a real problem with anything bubble-related, because, even though it’s really not that hard to tell when you’re in a bubble if you’re sensitized to the signs, it’s virtually impossible to predict when bubbles will end. It’s almost a disadvantage to be sharply attuned to bubble behavior. You’re just a Chicken Little act to everyone around you, after a while. You’re not wrong. Catastrophe is coming. But you’re just a drag, and everybody comes to hate seeing your sorry depressive ass looming in the hallway.

Interestingly, Vaughan’s earlier work on how couples break up (Decoupling) provided her an invaluable clue for understanding the Challenger launch decision (and the Columbia disaster later — it was the same damned social process, just eating away at a different part of the vehicle hardware.) Couple breakups can take a long time, but isn’t it funny how you can sometimes tell with a particular couple that it’s happening, even if it seems they don’t? It’s because you’re getting a snapshot of what, for them, is a gradual process of “normalizing” increasing differences in their relationship.

I live in Japan, visiting the U.S. now and again. I would come back one year and see, “Wow, 0% financing for cars, something’s wrong here.” Another year, another visit, “Wow, 0% financing for mortgages, this is nuts!”

I had two advantages, however: (1) I’d seen a couple of housing bubbles already, and I’d had friends and family lose their shirts in them, and (2) I was getting time-lapsed snapshots. Without the “benefit” (?) of both, it might be arrogant of me to assume I could have seen what was happening.


ab 11.11.08 at 3:32 pm

Alex’s point seems sensible about over-collateralization, the models use conditional independence to use the law of large numbers. But if you say that in the model the overall housing price index drives the conditional default probability of the mortgages, then you are using conditional independence, but unconditionally the default rate will depend on the housing price index, and so unconditionally defaults depend on the house price path.


Dion 11.11.08 at 9:39 pm

We would do well to remember the wealth of economic interactions pursued involving alternative medical products, that clearly have no demonstrated benefit, and, to the contrary, may often have an elevated risks due to interactions with prescribed medications etc. Americans still spend billions a year on this quackery, and it is marketed to them in the guise of “freedom of choice” rather than “Studies demonstrating efficacy.” (When studies are provided, they generally don’t involve The New England Journal of Medicine)

In other words, they may be pursuing this care for reasons other than health maintenance, and if the claims are great enough, lack of evidence is minimalized. Also, a thick envelope of “alternative” spirituality usually accompanies the product; its therapeutic value cannot be determined by mere rationality.

Uninsured credit-default swaps, how can this work? (not to mention the side bets placed on them) Who cares, when the promises are nearly infinite; don’t be so negative and bourgoise in your demands for evidence…


Mark Anderson 11.12.08 at 2:51 am

Re: Zamfir’s aircraft analogy.

I don’t think the analogy holds. With aircraft, yes, everything is modeled. But the corner points of the model get tested in real physical systems. They fly the AC into stall, to make sure that those numerical aerodynamic models work. They break a wing to check that the structure models work. In the end, they can do experiments to make sure they match the physical world. This blocks the race to the bottom; fancy justifications don’t fly :-) when the wing breaks at less than its design point. This is enforced both by FAA regulation and by the threat of liability. The organization doing the rating is exposed if something goes wrong, and has an incentive to play it straight.

Economic models don’t generally have this ability. Bloomberg had a two part article “Bringing Down Wall Street as Ratings Let Loose Subprime Scourge” discussing how the ratings agencies altered their models to boost ratings. There was definitely some relaxation of the risk correlation factor and requirements for diversity in the models. This relaxation was driven by the desire to give high ratings and please the customers paying to have their products rated. So there was definitely a race to the bottom, as the customers could go elsewhere. And the rating agency and to some extent the bank packaging the deal weren’t planning to keep any of the assets, so weren’t really exposed to the downside.

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