Tragedy beyond words

Terrible news:

Dad : Primrose Road
Early yesterday morning, my father died of a massive heart attack. He was healthy and had no pre-existing medical problems.
There is no rhyme or reason or sense to anything in this world.

He taught me how to dance the Lindy, play poker, engage almost anyone from anywhere in the world in conversation, and tell stories. The world is a very different place now.

fl has been, and is, a very good friend, for several years.

This is terrible.

Boyd and the Quantitative Revolution

First impressions of the new book, The John Boyd Roundtable: Debating Science, Strategy, and War are popping up all over the blogosphere. On the second day of its general availability, both Mike Tanji add their thoughts. My chapter in the Roundtable, the History of the OODA loop, was based on an earlier post on my blog.

As was this piece, which criticized the usefulness of the OODA loop:

While I’ll always be a fan of the OODA loop, a great conceptual model of human cognition, it does not help me in predicting outcomes. That’s why I generalized Horn et al to create a domain-knowledge/general-ability/motivation/behavior model of performance.

The OODA loop is certainly a “true” model of two-system processing, where a good Orientation can allow you by bypass conscious Decision making. However, it does not have a good way of telling reasonable applications from just-so stories.

Boyd’s OODA loop was a product of the Cognitive Revolution, that burst through psychology discovering internal mental processes that mediated behavior. However, the OODA loop may become a victim of the Quantitative Revolution, that is currently overthrowing much of the academy and the public schools, and is needed for any form of quality control. As OODA is described as a reaction to the Zero-Defect mentality, an early attempt to bring the Quantitative Revolution to military affaris, this would be an ironic fate.

What does China think about the bailout?

All the talk these days is of the government bail-out of the foolish (high-risk when already ahead) banks who sold mortgages to people who could not pay them back. Some of the banks then turned around and sold these bad mortgages to even more foolish banks, who are in even worse trouble. This has already destroyed the large investment banks of the United States, with every one of them now bankrupt, bought, or transformed. But the liquidity trap (see this excellent column by Paul Krugman explaining the problem, courtesy of Marginal Revolution). It’s a complex issue, and I do not understand it. Too many smart people are saying too many different things, many of them unexpected

So instead, I’ll trust China. China has nearly as much of a stake in America’s economic status as we do, and China (unlike America) has a ruling class made of engineers, instead of lawyers.

But what does China think about the bailout?

China Expat says that China is belittling the idea:

As China Finance, China News, and Chaobao Financial News stated, these actions by the Federal Reserve is only “creating money that does not exist which leads to the inflation of liquidity,” and that by showering the bailout on just a handful of stupid financial companies (my take), the Federal Reserve is “only protecting and encouraging large companies’ wrong doing.”

Yet McClatchy newspapers says that China cheers the news:

China voiced reassurance that the U.S. plan would contain the crisis, while South Korea and Japan pondered whether it would provide protection for banks that are holding distressed American mortgage debt.

As stock markets rallied around East Asia, analysts worry that Washington will print more money to finance the bailout, an action that could weaken the U.S. dollar, lift commodity prices and fan inflationary pressures.

President Bush called Chinese leader Hu Jintao early Monday local time to explain the $700 billion rescue plan of the U.S. financial sector, which is shaping up as possibly the largest bailout of private industry in American history. The state news agency, Xinhua, said Bush told Hu that “his government was well aware of the scope of the problem, and had taken and would continue to take necessary measures to stabilize the domestic and world financial markets.”

So what does China think of the bailout?

Obama captiulates on foreigner-bashing!

Good news! Both Slashdot and Weekly Standard note the news, but both miss the story. Obama has always been a flake, a candidate of the establishment who can be trusted to do what he is told because he does not know any better. Today, we see more evidence of that. Given the choice between bashing China or following the Democratic Party elites, Obama flops. Good.

Versionista has the comparison. Obama’s old webpage pushed the view that H1Bs were in general not particularly qualified, and we could get away with less immigration if our schools were better. But that nonsense is now gone! The old page was full of language like this:

Most H-1B new arrivals, for example, have earned a bachelor’s degree or its equivalent abroad (42.5%). They are not all PhDs. We can and should produce more Americans with bachelor’s degrees that lead to jobs in technology. A report of the National Science Foundation (NSF) reveals that blacks, Hispanics, and Native Americans as a whole comprise more that 25% of the population but earn, as a whole, 16% of the bachelor degrees, 11% of the master’s degrees, and 5% of the doctorate degrees in science and engineering. We can do better than that and go a long way toward meeting industry’s need for skilled workers with Americans.

The implication of lines like this is that if we spend more money on “blacks, Hispanics, and Native Americans,” we would need less Asians. Thankfully, the establishment got to Obama, and this 19th century racial-politics/bundle-of-work nonsense is gone.

I previously criticized Obama for hinting that he would kick the forigners out of the country. I am glad he came around.

Here’s hoping Obama’s next move to betray his original supporters and befriend the establishment is changing his tune on Colombia.

Debating Science, Strategy, and War

The news is on HG’s world, Wizards of Oz, zenpundit’s blog, and it’s great: Nimble Books has now released The John Boyd Roundtable:Debating Science, Strategy, and War. Nimble previously published Revolutionary Strategies in Early Christianity, which was based on my series Jesusism-Paulism: The Revolution of Early Christianity.

The John Boyd Roundtable would not have been possible without Mark Safranski as the editor, or Chet Richard’s organization of last year’s conference on John Boyd in Quantico, Virginia. Likewise, the new book is in debt to Frans Osinga, who not only wrote a chapter of this next but also previously published a book-length text, Science, Strategy, and War: The Strategic Theory of John Boyd. And of course, W.F. “Fred” Zimmerman of Nimble Books.

I am also proud that my chapter, “A History of the OODA Loop,” made the cut, and begins on Page 1.

The Unfairness of Working Memory

Several interrelated posts this morning, including “Intelience and the President of the United States, “Capturing my Thoughts: How could Demographic Warfare me used with 5GW?,” “Fixing Milwaukee Notes: Milwaukee School District Governance,” and “U.S. college panel calls for less focus on SATs.”

The topics all revolve around Working Memory, the capacity of the adult to keep 7 (ish) things in mind at the same time. Some people have more, some have less. Working memory is heritable and impacts life outcomes. Working memory is not “fair.” It is predicted by your class origin, your socio-economic status, your race, and so on while its variance is predicted by your sex. (Being male is risky business.)

Many social problems will be eleviated when we can use retroviruses or stem cell therapy to increase the working memory of the underclass. At the same time, any individual with low working memory can more than compensate by building up his long-term memory (his knowledge and experience), his self-efficacy (how he responds to failure), and his behavior.

Regulation of Emitted Behavior via Conditioning

Russia is an organism capable of learning that emits behavior. Our goal then becomes to control the emission of that behavior so that it is favorable to us.

We do this through conditioning. When Russia does something good, we reward that behavior by either giving it something it wants, or taking away something Russia does not want. When Russia does something bad, we don’t reward it. When Russia does something radically different — such as the Olympic War against Georgia — we change the conditions under which Russia can earn rewards.

There is good news that we are not rewarding Russian behavior.
There is good news that are are changing the condition.

There is no new Cold War against Russia. Russia is to weak for that to happen. There is merely the training of a Gap state to act in a war that does not disrupt global economic growth. And that is a good thing.

Maturation and Regulation

I received an email from a tdaxp reader who noted with alarm the shake-outs in the financial industry the consolidation, and the coming regulation. The reader emphasized to me the productive value of the financial industry to the country, and expressed concern that the financial industry that comes out of this crisis will be less innovative than the financial industry was before.

This cycle happens over and over again. The growth days of an industry fade, the industry fills the ecological-economic niche available too it, and as it can no longer grow as a share of the national economy the number of firms in the industry is sharply reduced. Likewise, as the now too-big industry threatens the national economy with the right-sizing it needs, the government steps in to make sure the industry’s transition from growth to maturity happens in a way that does not threaten everyone else.

It is likely that the financial industry is no longer a “growth” sector, at least not to the extent it once was. It is once again mature. It will become less risky, more regulated, more cautious, and less innovative.

The same thing happened to the automotive industry, the railroads, and even steel. Now finance feels the burdens of growing up.

The American author H.P. Lovecraft once wrote, “adulthood is hell.” Or in context:

Then I perceived with horror that I was growing too old for pleasure. Ruthless Time had set its fell claw upon me, and I was 17. Big boys do not play in toy houses and mock gardens, so I was obliged to turn over my world in sorrow to another and younger boy who dwelt across the lot from me. And since that time I have not delved in the earth or laid out paths and roads. There is too much wistful memory in such procedure, for the fleeting joy of childhood may never be recaptured. Adulthood is hell.”

Growing up is hard to do. Real people on Wall Street have lost their jobs, real speculators have lost fortunes.

It’s happened before, and it will happen again.

I am glad I live in a country where, even in such a crisis, creative destruction is still possible. A now mature industry shakes out surplus players, destroys shareholder value in those firms, and even syncs up our financial sector with the financial sector of others.

The R Statistics Language

R (also called GNU R, or even GNU S) is the open-source version of the S Programming Language, a language which fulfills the same statistical needs as SAS and SPSS. While SAS is a macro language designed for statistics, and SPSS is a macro language designed for statistics with a very nice graphical front-end, R looks like dialects ot C, acts like a dialect of LISP, and function as nifty alternative to SPSS and SAS. As I come from a programming background, R is beautiful in concept.

R’s learning curve is steep. If perl tries to make ‘impossible things hard and hard things easy,’ then R’s philosophy seems to be ‘make hard things easy and easy things hard.’ Some procedures that are complex and tedious in SPSS and R, such as taking the inverse of a matrix by the loadings of its correlation matrix as determined bya one-factor Principal Component Analysis, or PCA (in that case, it would be solve(ad.data.cor) %*% as.matrix(principal(ad.data.cor,nfactor=1)$loadings). Other tasks are requirer a deepper understanding of the material, however. For example, in SPSS creating a ‘Component Score Coefficient Matrix’ after a PCA is as simple as ticking a check box, or adding a simple request in the macro code. In R, you need to realize that the Component Score Coefficient Matrix is actually just the inverse of a matrix multiplied by the loadings of the matrix after running it through PCA: so you’d enter the line solve(ad.data.cor) %*% as.matrix(principal(ad.data.cor,nfactor=1)$loadings).

By far the coolest part of R and PCA is learning what unknown unknowns you forgot to solve for. For instance, a bundle of seemingly meaningless data can be examined through a ‘scree plot,’ to see which things you forgot to measure for (‘latent variables’) mattered, and which did not.

Unknown Unknowns? That’s the R