It was a truly awesome machine. When I left college (which had a B6700) and went to work on Honeywell and IBM machines it was like taking a long sad walk back to the dark ages.
One small example: the job control language (WFL) was an Algol variant which was great to program and Turing complete. Compare to IBM's appalling "JCL".
Array bounds checking with useful source level error messages (versus S0C4 abends and a core dump on IBM), writing multi-processor apps in Algol or COBOL was easy (versus close to rocket scientist level on an IBM requring for example that you code in assembler). Flags to distinguish code from data so data would not get executed etc etc.
Everyone has known for decades that these models (Black Scholes / Gaussian Copula) are wildly inaccurate in the tails. where the real risks live. They are roughly accurate on quiet days. Mandelbrot has been publishing on this since the 1960s! Anyone who pretends to believe these models is running a scam of some kind.
It is true that the GC played a minor role in the recent crisis but Black Scholes did not. See below for details.
The factors in the 2007-2012 crisis:
1. Fraudulent lending practices and falsified loan applications.
2. Excessive borrowings by households fueled by the Fed keeping interest rates too low for too long.
3. Belief that present trends would continue forever and house prices would continue to the moon.
4. Greed blinded people to the risks they were taking.
5. Lax to nonexistent regulation which allowed banks and related organizations to leverage to insane levels. It also allowed companies like AIG to sell insurance that they could not pay off on.
6. Risks were ignored due to perceived government guarantees (Fannie Mae and her ilk).
7. Fraudulently selling subprime toxic garbage as AAA securities. This is where the Gaussian Copula came in. Given known wrong and bogus assumptions (eg that house prices would never fall across the whole USA), it allowed the investment banks to pretend that the top tiers of the subprime securities were AAA ie secure. Internal emails showed they knew they were not really AAA. However the GC was only the vehicle; the underlying problem was fraudulent and criminal intent.
8. Rating agencies were paid large sums of money to rate the toxic waste as AAA. They either knew or did not care that the securities were toxic waste as long as they got the cash.
9. Pension funds and other naive investors believed that the rating agencies and investment banks were not lying when they said the AAA-rated securities were OK.
10. More recently we have seen the crisis in Europe which is the result of the failure to rein in housing bubbles caused by too-loose credit, and by governments which borrowed more than they could afford to pay back, and which made commitments that they could never fulfill (eg excessive pensions).
BS played a role in the near meltdown in 1998 when LTCM went down, and also in the 1987 stock market crash.
In both cases idiots pushed the models outside their sphere of validity. LTCM was leveraged to the hilt and assumed that short term historical correlations would continue to prevail. A cursory examination of history would show this is not the case.
In 1987 a technique called "portfolio insurance" was invented which supposedly allowed the user to simulate a protective "put option" at no cost. Portfolio insurance required selling stocks when the market fell. The BS model assumes infinite liquidity and no price jumps and if these are true PI should work. Again these are not valid assumptions and when the technique was implemented on the overvalued October 1987 market it accelerated and intensified the crash.
Even in physics most models are inaccurate outside a certain range of validity. It requires a degree of honesty and intellectual integrity to refrain from using them when they are not valid. Eg you cannot use Newtonian mechanics at 99.999% of the speed of light.
TL;DR this was not a mathematical mistake - it was fraud.
> Can you tell me how you reduced your fructose consumption? What foods did you eliminate and what did you replace them with?
Basically I stopped adding honey to my coffee (sad - it's delicious) and cut my fruit intake from 10-15 pieces to 2 per day. Also I eliminated all other sources of sugar such as cake, though these had been pretty minor in my case anyway.
I agree with Lustig that fruit is less toxic than concentrated sucrose or HFCS, but I am living proof that in sufficient quantities it is still bad news.
I added some extra fats (nuts, flax oil) and some extra protein (beans, peas, fish, red meat, chicken).
The best thing about the weight loss is that I lost weight in the bad places (ie my stomach) which previously had been impossible to move.
> should be relatively easy to test, even by individuals.
I did this. I cut my fructose consumption to the equivalent of two pieces of fruit a day and easily lost weight after years of struggling. My cholesterol fell from 255 to 160 mg/dl with an inprovement in the good/bad cholesterol ratios and a fall in triglycerides and uric acid. Also my inflammatory markers fell dramatically in some cases to unmeasurably low levels.
Lustig is spot on. A lot of people are heavily invested in the old orthodoxy and react accordingly. Fructose is a carbohydrate, but in any but small quantities it is metabolically a fat.
Having said that, the state of Prostate Cancer treatment and the lack of research into Prostate Cancer is a disgrace.
Prostate cancer kills similar numbers to breast cancer yet gets half the research funding. Thank goodness we live in a patriarchal society or the ratio would be even more in women's favor.
I have watched two male relatives die of Prostate Cancer and it is not a good way to go.
Actually there was a reduction in all-cause mortality. It's just that the study's design was such that the reduction could possibly have been due to chance. This is not at all the same thing as "finding no ... benefit".
This lack of statistical significance is probably mainly due to the small size of the study (160 men).
Note that the reduction in death rates from prostate cancer was large in practical terms and (statistically) significant.
Prostate cancer kills a minority of men. Take this fact, add in the fact the study was small, and then throw in all other causes of death (which vary randomly) then it is not at all surprising that the result was not statistically significant. This is mainly due to a small study and noise from other causes of death.
Actually not even that. The study found a difference but the effect was not large enough or the study was too small to prove that the effect could not possibly have been due to chance.
Time and time again we get people who should know better equating "no significant difference found" with "found that there was no difference".
This is so frustrating. In medical studies the word "significant" does not mean what it means in normal English. It refers to the possibility that the effect was due to chance, not to how large, important, or clinically significant the effect is.
The study found no significant difference. This does NOT means the study found there was no difference. What it means is that the study was not large enough or the effect was not large enough to prove that the reduction was real at 95% significance.
In fact the screened men died at a LOWER rate. It was just that the effect could possibly have been due to chance.
So realistically the report should say "PSA screening probably saves lives but more study is needed to be sure and to determine how large the effect is".
Her complaint here, one of many many complaints of sexism, seems to be that her manager is unhappy that she loads him down with complaints and too much detail that he doesn't want to know.
She puts this down to sexism. It's not clear why this is sexist. She is wasting his time. If he is like most managers he is very pressed for time and he doesn't need his time wasted.
In this case, sexism is a redundant explanation because other, better, simpler explanations are available. in Bayesian theory this is technically called "explaining away" and it is a good thing. Fact A is not evidence for claim B if explanation C is better.
I read her blog because it has some interesting ideas. However it reminds me of PZ Myers' blog in some ways. There is the same repetitive theme of how stupid all these people are compared to moi.
"Within six months, they had an actual user-affecting outage directly attributed to things on that team. We hadn't had any of them during my tenure running that service."
"As I've said before, enjoy your pages, guys. "
Maybe this is just typical sysadmin arrogance / all users are ID10Ts.
I also see signs of a paranoid attitude:
"I knew it was going to be a disaster and they'd use it against me".
Playing games of passive-aggressives:
"I was going to be cold and dry and only answer exactly what was asked of me."
Really if you drop of load on a co-worker in a team meeting you are going to get some blowback.
Her blog is also full of complaints of women not being treated fairly eg:
This is a classic Dunning-Kruger situation. Penrose is out of domain of expertise here. He has not bothered to study the theory of mind (eg see the lack of relevant references in his book).
His arguments do not stack up, as extensively documented elsewhere.
This is not the first time that a famous physicist/mathematician has got it drastically. Neils Bohr was an animist - he believed along with many people at the time that there was some magic hidden essence to life that went beyond material things. When told about the discovery of DNA he said "Yes, but where is the life?".
Trading is very hard. Markets are efficient at a first approximation and where they are not efficient, simple arbitrages do not exist. You are up against smart, well capitalized people. Once you find an edge your irrational brain cuts in.
Books: "Investing by the numbers" by Jarrod Wilcox. "Trend Following" by Covel. "The only three questions that count" by Fisher. And of course the "[Stock]Market Wizards" books. Typically the top traders seem to have read a couple of hundred books on the topic and have done thousands of hours of research.
Your assumption that you will lose at first it probably right. But that's better than winning at first, which is likely to falsely convince you that you're a genius.
Beware the following which can look like skill for a while.
1. Randomness (temporary luck)
2. Excessive Leverage (-> gambler's ruin)
3. Selling insurance without realizing it eg selling out of the money options.
One small example: the job control language (WFL) was an Algol variant which was great to program and Turing complete. Compare to IBM's appalling "JCL".
http://en.wikipedia.org/wiki/Job_Control_Language
Array bounds checking with useful source level error messages (versus S0C4 abends and a core dump on IBM), writing multi-processor apps in Algol or COBOL was easy (versus close to rocket scientist level on an IBM requring for example that you code in assembler). Flags to distinguish code from data so data would not get executed etc etc.