I had no idea Inv banks like BS and UBS warehoused so much asset backed securities, which doesn’t make much sense, but that error is from bad transfer pricing, because it should have never made economic sense for a bank to borrow at the AA rate to buy AA or AAA Mortgage backed securities. A good transfer pricing system would have stopped that. If you read UBS’s report to shareholders, they go over the errors pretty well (which were similar to Bear’s), you can google it, released around April of this year. That’s a boring reason, but gets at their mistake pretty well. More fundamentally, the failure of mortgages was a function of relaxing underwriting standards over the past 15 years, and I think best laid out it Stan Liebowitz’s piece, which is a fascinating tale, but again, the Black Swan is pretty irrelevant to that line of analysis.

Now, to say they repeatedly make the mistake of going bankrupt, anthropomorphizes the market in a silly way. Some people make mistakes every 5 to 10 years in a systematic way, but they are different people, in different fields, making mistakes about different financial structures. But the annual default rate for nonfinancial companies, historically, is around 1%, so they usually do not make mistakes, though it clusters over time (eg, 2001, 1990, 1981, 1970). Every panic, or crisis, is different, because people aren’t so dumb as to make the exact same mistake twice. I don’t see the Black Swan as a fruitful way to group these mistakes, because ‘overconfidence’ is merely evidenced by failure in these contexts, like saying don’t buy assets that will decline a lot in value.

Combinatorial prediction markets allow one to ask billions of questions at once, which will cover a lot more black swans.

How does that work?

Joe, what is happening is a mixture of your scenario 2 and scenario 3.

Or perhaps the bidders factor that in when making their bids, so that the prediction doesn’t actually give expected value. (I may think there is a 10% chance that the US will have a revolution and the dollar become worthless; but I don’t factor that into my business operations, because my business will probably go under if that happens, and because I just don’t want to think about that possibility.) In that model, we expect prediction markets not to predict major financial collapses, because planners are only planning for scenarios in which that doesn’t happen.

Simulation.

I remember reading in his book that it might be a black swan if it was unexpected from your viewpoint eventhough it was expected from someone else’s. Eg, the killing of turkey was BS for it but not for the butcher. So if the information about the probabiliy of such an occurrence is limited, then it can qualify as a BS. This along with the dramatic impact of the event.

OK, maybe. And as a popular author he suffers the unfortunate need to be fantastic in his storytelling as well as rational in his choice of supporting evidence. And I appreciate it’s not easy to do that. But I still feel like he’s playing on the lack of sophistication in the broader population to recognize and refute cognitive bias in order to sell books.

Second, if he has been talking about flaws in the financial model – then how is that a black swan? As others are pointing out – he obviously had relevant priors he was using to make that statement.

“For everyone who “knew” when this bubble was going to burst and made money, there could ten “corpses” in the financial graveyard of people who thought the same thing but their timing was off. I think it’s just as important to look at the losers who “knew” that is was a bubble as the winners.”

Right! Which seems to be evidence that the world is working in a perfectly normal manner here. It’s not like the “Innovator, Imitator, & Idiot” distribution isn’t known. You can’t expect everyone to be winners. And, back to the point of “white swans painted black” – if there are winners or people like Taleb himself who can anticipate the tendency towards a tail event – how is the occurrence of that event a “black swan”?

It’s trivial to say, if the driving factor has a fat-tailed distribution with few observations to calibrate to, and the function based off that factor is very complicated (his fourth quadrant), chances are forecasts will have high standard errors. But often one is forced to make an estimate, implicitly, and so one is left with identifying the state space and applying probabilities. You can just say, “I have no idea”, but usually one has some idea, and being absolutely certain of the inaccuracy of any estimate, is a strange bias of its own.

He has been slamming Value at Risk since 1996, but value at risk has only grown in popularity as a tool since then (and he de-linked those criticisms from his website, because they were clearly overstated). VaR is imperfect, but one has to see what it replaced, which was a hodge-podge of idiosyncratic reports that did not scale at all. Science is about compression, explaining more with less, and that’s what VaR does. It is imperfect, but people have been talking about jump diffusion models in option and volatility estimates for a long time, and they are not super popular because they add more problems than they remove (adding parameters is always a trade-off). As per extreme events influencing pricing, that goes back to Reitz and his Peso-problem explanation of the equity risk premium in 1988, which has a considerable literature, and is never adressed by Taleb.

I think he continually demolishes straw man arguments, ignoring a vast amount of literature that is aware that estimates–even estimates of variance–have standard errors, and are not normally distributed. Stable paretian distributions, and fractals, are not popular because they are inferior to modern kluges found in volatility smiles, variations or Garch models, not because no one seriously tried them (Mandelbrot introduced them in 1962, Chaos was a best seller in 1990).