Invisible Asians

Asian-Americans are invisible. We're not a minority group. At least not to the politically correct intellectual contortionists at the NYTimes. Insidious racism, rather than meritocratic testing, must be responsible for the alarming decline in the representation of (certain) ethnic groups in NYC's top high schools.

See earlier posts on how affirmative action hurts Asian-Americans (Asians have to score 50 points higher, on average, on the SAT than whites to gain admission to the same elite universities), and how the silicon valley counterpart to this NYC magnet school story is told in a less politically correct way by the WSJ.

Via GNXP:

The New York Times headline is: Minority Students Decline in Top New York Schools. The graphic? Here:


Is something off with this graphic in relation to the headline, or what? Ah, click the link in the story, and you see this:

Universities as economic engines?

The Times article excerpted below quotes studies questioning the value of investing in universities as engines of economic growth. It seems obvious to me that proximity to universities with strong technical (science and engineering) departments is a necessary, but not sufficient, criteria for having a vibrant high tech economy. Certainly every high tech mecca (bay area, southern California, Boston, Seattle, Austin -- in rough order of VC investment per annum) is near one or more top universities.

But, there are many other factors that produce the silicon valley network effect, in particular access to venture capital, experienced financiers, entrepreneurs and engineers, and proximity to mature (public) technology companies. With so many factors at play, finding a statistical correlation with a single factor such as the quality of the local university is challenging.

NYTimes: ...If Stanford can hatch world-famous companies around Palo Alto, politicians assume, their colleges can, too. But with so many trying to spin universities away from their traditional academic focus into engines of economic development, it is worth considering whether investing in local universities can achieve that goal.

This strategy is based on the view that research done by professors can form the basis for local start-up companies and that the graduates of the university can supply the entrepreneurs and employees.

But advocates should remember an old maxim of economic development: Beware of investing in things that can move. As it turns out, graduates and research ideas both tend to move around a lot.

Subsidizing teaching is problematic as a development strategy because graduates frequently move out of state.

A study by the economists John Bound, Jeffrey Groen and Gabor Kezdi of the University of Michigan and Sarah Turner of the University of Virginia, “Trade in University Training: Cross-State Variation in the Production and Stock of College-Educated Labor,” (http://cat.inist.fr/?aModele=afficheN&cpsidt=15840383) found little evidence of people staying in places because they went to college there.

The more likely smart people are to leave, the more money their state is spending on helping another area’s economy develop. Marc Andreessen, for example, invented the Web browser while at the University of Illinois, but then founded Netscape in the actual Silicon Valley rather than starting a new one in Urbana.

Texas may subsidize science teaching at the University of Texas, El Paso, but the chance that its graduates will stay and transform the local area into Silicon Rio Grande is remote.

So if a state’s subsidies to graduates from a university will not create new Silicon Valleys, how about subsidizing the research? There is no question that academic research has hatched many of today’s booming technology industries. But scientific and engineering ideas also travel quickly.

Recall the 1980’s, when Japanese companies rose to prominence by producing things that had been invented in the United States, like photocopiers, computer memory chips and video recorders.

In a recent study of the determinants of the creation of high-tech firms in different locations, “Movement of Star Scientists and Engineers and High-Tech Firm Entry” (www.nber.org/papers/w12172), Professors Lynne G. Zucker and Michael R. Darby of the University of California, Los Angeles, examined the importance of what they called “disembodied discoveries.”

They looked at such factors as having successful patents at universities or where highly influential science articles had originated. They found little evidence that the ideas helped local businesses any more than businesses in other areas.

For every Stanford in Silicon Valley, there seem to be several Purdues in West Lafayette, Ind., or Cornells of Ithaca, N.Y. — places filled with path-breaking discoveries but with local economies that are seldom seen as the next Silicon Valley.

The one thing the study does find to be consistently associated with high-tech start-ups is the presence of star scientists — not the ideas, which can be copied, but the scientists themselves. This seems to be the one way in which a university can be used as an engine of business growth.
...

Portrait of a quant II

Those funsters at Businessweek have another goofy bit about quants in their latest issue. (See their previous article Math Rules! and here for previous quant-related posts.)

Well, they have the general idea right. If you can't read the text in the figure below, it says "...degree from MIT, Caltech or India in applied mathematics, physics, computer science or all of the above", "800 math SAT de rigueur" and "math tournament rock star" :-)

The Quintessential Quant: James H. Simons, the former math professor who founded the $12 billion quantitative shop Renaissance Technologies Corp., pocketed an estimated $1.5 billion last year. That was thanks to the 5% in fees and nearly 44% of profits that Renaissance docks its investors (vs. traditional hedge funds' typical "2 and 20"). Clients don't complain; Renaissance's leading fund has returned 35%, after fees, since 1989. And D.E. Shaw & Corp., the brainchild of ex-Columbia University computer science professor David E. Shaw, with $23 billion in capital, has netted investors 21% a year for 17 years, without a single losing 12-month stretch.

Landing a job at either of these shops can be insanely lucrative -- and even more insanely competitive. "Using a self-consciously obnoxious term, we're looking for superstars, the kinds of people who would be extraordinarily good at nearly anything," says Nicholas P. Gianakouros, head of global recruiting for New York-based D.E. Shaw.

He is being euphemistic. The handful of quant and programming geniuses who get into the toughest mathematics, physics, and computer science PhD programs on the planet are already best in class. So screening for the 5 or ten very best of that best means establishing a whole new set of prerequisites. "The quant shops are a different animal," says Alison Seanor, vice-president at Glocap Search, a Manhattan hedge-fund recruiter. What is the "it" factor that distinguishes the crème de la crème? All Seanor will say is, "I know it when I see it."

One obvious filter is that liberal arts students -- or even bankers and stock jockeys -- need not apply. What you will need is a nosebleed grade-point average in applied mathematics, physics, or computer science at an elite school like the Massachusetts Institute of Technology, California Institute of Technology, or Indian Institutes of Technology. Many of these students are published and have won high math honors such as the Putnam Fellowship. Often, their names are already so well known in the field that the quant funds make the first approach.

Another must-have: an 800 math SAT score (even if you sat for that exam in your awkward adolescence). Although the funds diplomatically claim the number is "just another data point," it's pretty well understood to be a critical credential.

The quant shops want malleable intellect untainted by Wall Street dogma -- i.e., not "buy, sell, or hold" types. "They're not really looking to make money on corporate events like takeovers," says Emanuel Derman, director of the financial engineering program at Columbia University and head of risk for quant house Prisma Capital Markets. "They're looking to make money on mathematical models." Top funds often advertise in esoteric scientific journals. "You'll not likely find our ads in a dentist's waiting room," says D.E. Shaw's Gianakouros.

If yours is one of the lucky 1% to 3% of résumés to survive an exhaustive initial culling, you can look forward to an hour-long phone interview peppered with thought problems and brain teasers. Pass that test and you will then be summoned as many as three times to undergo up to a dozen grueling interviews. "Every interviewer uses a different approach," says Gianakouros, citing programming problems and math proofs. Expect to be asked to build an intricate Excel model on the spot. Whatever the case, advises Derman, "don't say anything unless you're ready to be quizzed on it."

The firm will then solicit references for areas in which a candidate may appear weak. Ultimately, it takes a consensus among everyone who has met the candidate to extend a coveted offer. D.E. Shaw says that out of every 500 candidates who got the initial callback, only one makes the final cut. Many agree it's even harder to get into secretive Renaissance, which would not comment for this story.

A typical offer, say sources, starts with a base salary of around $250,000, plus a guaranteed annual bonus that could double that. The best can command a cut of a fund's upside -- beaucoup bucks when you consider the multibillion-dollar asset pots. All this, yet, says Seanor, "most of these guys have never even had a real job."

MIT vs Caltech: Nobel count

[ 2015: Updated information. ]

I learned from the Caltech News alumni magazine that 17 Caltech alumni have won the Nobel prize, versus 25 from MIT. You might think the advantage here goes to MIT, but their student body is 5 times larger! Most people are shocked to learn that Caltech's graduating class is only about 200 students. On a per-capita basis, I believe Caltech produces more science Nobel prizes than any other school. Keep in mind that about half of Caltech undergrads major in engineering or computer science, which are not Nobel-eligible disciplines.

When I was a student we used to joke that MIT stood for "Many Incompetent Technologists" (emphasis on Many) or "Made In Taiwan" :-)

On the other hand, Feynman went there, so it can't be all bad. Actually I have to admit it is probably more fun to be an undergrad at MIT than at Caltech (part of it is that the classes are so much easier :-). When I lived in Cambridge I could see there was a much more lively college scene in Boston than LA. MIT is bigger and has a better male-female ratio and more balanced social life than Caltech. Of course, the climate in Boston isn't as nice.

Before I had twins and startups I used to be involved in Caltech admissions and recruiting -- including calling up admitted students to answer questions and give advice. I was often speaking to students who had been admitted to both Caltech and MIT, and I was always scrupulously fair in describing the pros and cons of the two places.

More Caltech bragging: patents and PhDs.

Put/call ratio and stat arb

It looks like put/call volume ratios (suitably cleaned, subtracting market makers, etc.) have predictive value for the underlying stock. A model portfolio going short/long the 20% of names with the highest/lowest ratios would have made 60% annualized returns over the last dozen years, with no down years (trading costs might reduce this to 50%). A subscription to the CBOE data necessary for this analysis is now available for a $600/month fee. I wonder what the weekly or monthly Sharpe ratio is for this strategy?

More good evidence against the strong efficient market hypothesis :-)

NYTimes: A new study has found that a portfolio based on the preferences of options traders has consistently beaten the overall stock market. In reaching that conclusion, the study paves the way for what may be a very profitable stock-picking strategy.

The study, “The Information in Option Volume for Future Stock Prices,” appears in the fall 2006 issue of the Review of Financial Studies. Its authors are two associate professors of finance: Jun Pan of the Sloan School of Management at the Massachusetts Institute of Technology and Allen M. Poteshman of the University of Illinois at Urbana-Champaign.

...Until now, there has been no comprehensive study of option traders’ track records as stock pickers. That hasn’t been for want of trying: the requisite data simply was not available to researchers. The volume figures that options exchanges report publicly, for example, reflect a combination of several kinds of transactions, muddying the overall picture. The volume number reported for a given option, for example, may reflect new purchases by options traders, but it may also include the sale of positions previously acquired. That makes it hard to tell whether traders actually favored a stock.

Using a private database provided by the Chicago Board Options Exchange, the two professors were able to deconstruct an option’s total trading volume into various categories. They excluded trades by market makers, for example — dealers at the options exchange who buy and sell securities for the general purpose of maintaining liquidity. They narrowed the database further to focus on just that portion of an option’s daily trading volume that reflected new positions by other traders, on the assumption that these transactions offered a clearer signal of what traders actually thought of the underlying stock. The database covered the dozen years from the beginning of 1990 through the end of 2001.

For each option in this database, the professors calculated a daily volume ratio of newly acquired put options to newly acquired call options. A high ratio meant a strong consensus among options traders that the price of the option’s underlying stock would fall, while a low ratio showed a widely shared expectation that the stock would rise. The professors found that the stocks whose options had the lowest ratios consistently outperformed the stocks whose options had the highest ratios.

Consider this hypothetical portfolio constructed by the professors: it held the stocks of the 20 percent of options with the lowest put-call ratios, while selling short the stocks whose options had the highest such ratios. (The portfolio was readjusted weekly, adding and deleting stocks because of changes in the ratios.)

The professors reported that before transaction costs, this portfolio produced an annual average return of 62 percent over the dozen years covered in the study. This contrasts with an annualized total return of 12.3 percent for the general stock market over this period, as measured by the Dow Jones Wilshire 5000 index. Still more impressive was the fact that the portfolio earned double-digit returns each year, even when the overall market declined. Based on their data, however, the professors had no way to determine how options traders were able to achieve these results.

The portfolio required frequent transactions because the price moves correctly anticipated by options traders lasted for only a couple of weeks, on average. So transaction costs would have eaten up a big chunk of the return. But Professor Poteshman estimated that in the hands of an institutional investor, for whom such costs would typically be quite low, the portfolio’s return would still have been as much as 50 percent annually.

Even for individual investors, who would pay higher costs, Professor Poteshman estimated that the annualized return would have been well into double digits.

DESPITE the strong results of the strategy, it would have no more than academic interest if investors had no access to the private C.B.O.E. database that the professors studied. In July, however, the exchange began selling subscriptions to this database to the public.

A subscription isn’t cheap: $600 a month. That helps make the professors’ strategy impractical for small investors. Still, the study shows that potentially valuable information can be found in options traders’ behavior. And institutional investors, including hedge funds and mutual funds, can easily exploit it.

Theorist vies for poker title

Former(?) particle theorist Michael Binger (PhD Stanford, perturbative QCD) is among the finalists at the 37th World Series of Poker in Las Vegas. Last I checked, Binger had over $3 million in chips, but is only in 8th place.

Binger says he got "burned out" on physics around 2002 and has been earning his living at poker ever since. He "hopes to continue doing research in physics without having to run the rat-race of getting a job and impressing all the right people as he puts it." Sounds like a familiar dream :-)

Binger and other poker gods might someday go the way of Kasparov -- replaced by machines.

(Via Dave Bacon.)

Update: Binger took 3rd place and won $4M. The Times covered the wrong story -- they wrote about some hedge fund guy who placed 18th and donated his $650k to charity.

Economist on blogging professors

Buried in the article, which is mainly about blogging economists (and mentions a number our favorites, like the two Brads, but unfortunately not Mark Thoma of Economist's View), is the nugget below on how the gap between faculty quality (or at least productivity) at the very top schools and elsewhere has narrowed due to new technology.

Another factor, at least in theoretical physics, has been the terrible job market that persisted through the last 30 years of the 20th century (it seems to be better now, as sputnik era professors finally seem to be retiring). From around 1970-2000 there were only a handful of jobs per year in particle theory, and even average research universities were able to hire exceptional people. In many of those years, the new crop of PhDs from any one of the top programs could have filled every faculty opening in the country. A little arithmetic is enough to understand the consequent logjam, and why there are so many former theorists in finance, technology, even biology.

With professors spending so much time blogging for no payment, universities might wonder whether this detracts from their value. Although there is no evidence of a direct link between blogging and publishing productivity, a new study* by E. Han Kim and Adair Morse, of the University of Michigan, and Luigi Zingales, of the University of Chicago, shows that the internet's ability to spread knowledge beyond university classrooms has diminished the competitive edge that elite schools once held.

Top universities once benefited from having clusters of star professors. The study showed that during the 1970s, an economics professor from a random university, outside the top 25 programmes, would double his research productivity by moving to Harvard. The strong relationship between individual output and that of one's colleagues weakened in the 1980s, and vanished by the end of the 1990s.

The faster flow of information and the waning importance of location—which blogs exemplify—have made it easier for economists from any university to have access to the best brains in their field. That anyone with an internet connection can sit in on a virtual lecture from Mr DeLong means that his ideas move freely beyond the boundaries of Berkeley, creating a welfare gain for professors and the public.

Universities can also benefit in this part of the equation. Although communications technology may have made a dent in the productivity edge of elite schools, productivity is hardly the only measure of success for a university. Prominent professors with popular blogs are good publicity, and distance in academia is not dead: the best students will still seek proximity to the best minds. When a top university hires academics, it enhances the reputations of the professors, too. That is likely to make their blogs more popular.

A trillion dollar question

Brad Setser notes that China's dollar reserves will soon surpass the trillion mark, if they have not already. Where to invest all those dollars? OPEC nations, Russia and other Asian exporters (Korea, Taiwan, Japan) share the same problem.

Hedge fund quiz question #1: Is the yield curve flat because traders think the fed is likely to overtighten (leading to a recession in a year or two), or because of all those foreign central bank dollars looking for a parking spot?

By my count China already has over a trillion dollars in reserves and reserve-like assets. But I am counting the funds the PBoC shifted to the state banks. In a couple of months, though, China will formally announce that its reserves now top a trillion dollars. So it isn’t exactly a surprise that Chinese policy makers would be spending a bit of time thinking about how to use those funds.

The key fact for the global economy is not that China holds a trillion dollars in reserves. It is that those reserves are growing at a pace of around $20b a month/ $250b a year. This reserve increase has continued even as interest rate differentials have moved steadily in the dollar’s favor. China constantly struggles not just to invest its existing reserves productively, but to find new places to park its ever growing reserves.

Right now, there is no reason to think that China won’t have $1,500b in reserves in about two years time. Not unless Chinese policy makers show an ability to act far more decisively than they have so far.

$250b is a lot of money to invest every year. I suspect there are some constraints on how China can invest it. There aren’t many – strike that, there aren’t any – emerging markets that could absorb inflows on that scale. Modest sized industrial economies like Australia and the UK are also too small to absorb more than a small fraction of the total. Look at their respective current account deficits in dollar billions.

Japan’s government debt market is very, very big. But JGBs don’t pay much interest, and the PBoC likes a bit of carry. So China really is left looking at the US and the European market. I don’t really buy the notion that European debt markets are too small and illiquid for China. China likely has been placing funds in some smaller and less liquid debt markets in the US, not just the most liquid of instruments. But I do think that it would be hard for China to continue to peg to the dollar and dramatically increase its euro allocation.

Suppose China now invests 25% of its reserve growth in euros. That is $60b a year or so. Real money. Suppose it decided it wanted to invest 50% in euros. That is $125b a year. I suspect that a $60b increase in net flows to Europe would have an impact on the euro/ dollar exchange rate. And if it did, China’s peg implies that the RMB would depreciate along with the dollar. That would force China to buy more reserves.

Globalization: theory and an example

From Economist's View, excerpts from a recent paper by Harvard economist Richard Freeman on how globalization is affecting worldwide labor markets. Some key points: a doubling of the global labor pool in recent decades has changed the ratio of capital to labor significantly. The increase in available labor has a significant high-skill component, which will impact highly educated workers in the west, particularly in science and technology.

Following the Freeman excerpts, I quote from an article in today's WSJ, which discusses how rapidly China has moved up the manufacturing value chain. They will soon surpass Germany to become the number 3 producer of automobiles. Given that China produces several times more engineers per year than the US, working for a fraction of western salaries, it is no surprise to me (despite predictions from just a few years ago) that they can climb the value chain so quickly. The final claim, soon to be overturned, is that China can only copy, but not innovate.

Some earlier posts on China development here, here and here.

Freeman: At first, the advent of huge numbers of workers from India and China into the global capitalist system seemed to offer a boon to most workers in advanced countries. The labor force is less skilled in the global giants than in the advanced economies. According to the Heckscher-Ohlin model, skilled workers in the advanced countries would benefit from the new trading opportunities while only the relatively small number of unskilled workers would lose. If all workers in the North were sufficiently educated, they would avoid competing with low paid labor overseas and benefit from the low priced products produced there. Competition from low wage workers in China and India might create problems for apparel workers in Central and Latin America or for South Africa, but not for ... the advanced North. Similarly, the “North-South” trade model that analyzes how technology affects trade between advanced and developing countries implied that trade would benefit workers in the North, who had exclusive access to the most modern technology. More low wage workers in the developing world would lead to greater production of the goods in which the South specialized, driving down their prices.

Tell it to Lou Dobbs! The off shoring of computer jobs, the US’s trade deficits even in high technology sectors, and the global sourcing strategies of major firms have challenged this sanguine view. The advent of China, India, and the ex-Soviet Union shifted the global capital-labor ratio massively against workers. Expansion of higher education in developing countries has increased the supply of highly educated workers and allowed the emerging giants to compete with the advanced countries even in the leading edge sectors that the North-South model assigned to the North as its birthright.

...the global labor market changed greatly in the 1990s due to the advent of China, India, and the ex-Soviet bloc to the world economic system. During the Cold War era, these countries had trade barriers, self-contained capital markets, and little immigration to the advanced countries – all of which isolated their labor markets from those in the US and the rest of the capitalist global world. The collapse of Soviet communism, China’s decision to “marketize” its economy, and India’s rejection of autarky, greatly increased the supply of labor available to the global capitalist system. I estimate that if China, India, and the ex-Soviet bloc had remained outside of the global economy, there would be about 1.46 billion workers in the global economy in 2000 (figure 1). Because those countries joined the rest of the world, there were 2.93 billion workers in the global economy in 2000. Since twice 1.46 billion is 2.92 billion, I have called this “The Great Doubling”... The effect of this huge increase in the work force changed the balance between labor and capital in the global economy. ...

I estimate that as result of the doubling of the global work force the ratio of capital to labor in the world economy in 2000 fell to 61 percent of what it would have been in 2000 before China, India, and the ex-Soviet bloc joined the world economy. ... By giving firms a new supply of low wage labor, the doubling of the global work force has weakened the bargaining position of workers in the advanced countries and in many developing countries as well. Firms threaten to move facilities to lower wage settings or to import products made by low wage workers if their current work force does not accept lower wages or working conditions, to which there is no strong labor response. The result is a very different globalization than the IMF, World Bank, and other international trade and financial organizations envisaged two decades or so ago when they developed their policy recommendations for the world economy.

...Countries around the world, including the new giants, have invested heavily in higher education, so that the number of college and university students and graduates outside the US has grown rapidly relative to the number in the US. ...

But highly populous low wage countries have also invested heavily in higher education. Indonesia, Brazil, China, India – name the country – have more than doubled university student enrolments in the 1980s and 1990s (Freeman, 2006). China has made a particularly large investment in science and engineering, so that by 2010 it will graduate more PhDs in science and engineering than the US. While the quality of graduate training is higher in the US than in China, China will surely improve quality over time. India has produced many computer programmers and engineers. ...

Comparative advantage, comparative advantage, wherefore art thou, oh comparative advantage?

In the North-South model that trade economists use to analyze how technology affects trade between the advanced North and the developing South, the advanced countries monopolize cutting edge innovative sectors while developing countries end up producing traditional products. The greater the rate of technological advance and the slower the spread of the newest technology to low wage countries, the higher paid are workers in the North relative to workers in the South. The comparative advantage of advanced countries in high tech sectors is rooted in those countries having more scientists and engineers and other highly educated workers relative to the overall work force than developing countries.

In these sorts of analyses, the spread of higher education and modern technology to low wage countries can reduce advanced countries’ comparative advantage in high-tech sectors and adversely affect workers in the advanced countries as a result. Any country with a comparative advantage in a given sector can lose when another country can compete successfully in that sector. ... If a foreign competitor gains comparative advantage in industries that have particularly desirable attributes– that employ large numbers of highly educated workers and offer great opportunities for rapid technological advance – the country with the initial advantage has to shift resources to less desirable sectors – those with lower chance for productivity growth, with fewer good jobs, and so on. The usual assumption regarding high tech sectors is that only advanced countries have the educated work force necessary for competing in them. In the 1980s, Americans got worked up when Japan seemed to be producing better high tech products than the US.
In the 1990s the US worried about the competition between Airbus and Boeing in the manufacturing of aircraft. No one entertained the notion that China or India would become major players in high technology leading edge industries. ...

The advance of China and India into high tech has obsolesced these analyses. China has moved rapidly up the technological ladder; has greatly increased its high tech exports, and has achieved a significant position in research in what is purported to be the next big industrial technology – nanotechnology. Over 750 multinational firms have set up R&D facilities in China. China’s share of scientific research papers has risen greatly. While India has not invested as much in science and engineering as China, it has achieved a strong international position in information technology, also attracting major R&D investments, particularly in Bangalore. How can low income countries with few scientists and engineers relative to their work forces compete in high tech?

These countries have moved to the technological frontier because success in high tech depends on the absolute number of scientists and engineers rather than on the relative number of S&E workers to the work force. It isn’t how many engineers per person that produces a technological breakthrough as much as the total number of engineers working on the problem. ...

I have called the process of moving up the technological ladder by educating large numbers as “human resource leapfrogging” since it uses human resources to leapfrog comparative advantage from low tech to high tech sectors, contrary to the assumption of the North-South model. The low wages in these large populous countries, moreover, makes them formidable competitors for an advanced country because it gives them a potentially large cost advantage in attracting R&D. ...

In sum, the notion that US skilled workers need not worry about competition from equally skilled workers in low income countries because developing countries have fewer graduates per capita does not fit with reality. With an increased supply of highly educated persons from low wage developing countries, multinational firms can offshore high-skilled work and hire graduates from universities world wide; while large numbers of highly educated immigrants can come to the US to work.

WSJ: Raising the bar for competitors around the world, China is shifting its manufacturing resources to increasingly sophisticated goods, as shown by its rapid emergence as a global powerhouse in the auto-parts industry.

Just a few years ago, Chinese-made automotive components were plagued by a reputation for poor quality, and often cost more than U.S. or German parts. Detractors said the precision engineering required for the best parts was beyond the reach of inexperienced Chinese companies and their low-cost workers.

Last year, however, China for the first time exported more parts than its fast-growing auto industry purchased from abroad. Quality has improved so much that major Western auto makers like Volkswagen AG and DaimlerChrysler AG say they plan in coming years to buy billions of dollars of Chinese-made components -- such as brakes, fuel pumps, wheels and steering systems.

Those gains show how China continues to evolve as a manufacturer, posing new challenges for rivals in the U.S., Europe and Japan. After earning its stripes as a maker of simple consumer goods, such as furniture and textiles, China has branched out, quickly coming to dominate more labor-intensive parts of the consumer-electronics business, such as computer assembly, and moving into a broader range of industries.

The country's production of machinery and transportation equipment has surged, and export of those goods -- which range from auto parts to forklifts to vacuum cleaners -- totaled $352 billion last year, a fourfold increase from 2000.

Meanwhile, motor-vehicle production here has nearly tripled, and China is on pace to overtake Germany as the world's third-biggest auto maker. It has become the world's second-largest car market in terms of sales as millions of Chinese buy cars for the first time. Millions more are expected to do so as their incomes rise and car prices fall.

Now, "China competes in the entire range of products from telecom equipment to textiles," says Hafiz Pasha, director of the United Nations Development Program's Asia bureau.

The transition comes at a sensitive time for the U.S. and Europe, which have been finding it harder to hold on to high-paying manufacturing jobs. Employment in the U.S. auto-parts industry fell to about 644,000 in 2004 from about 721,000 in 2002, according to government figures.

More job losses could be on the way: Some major U.S. parts makers -- including Delphi Corp., which has plants in China -- have sought bankruptcy-court protection. And small and midsize suppliers, which often don't have the resources to set up lower-cost operations abroad, are facing growing pressure.

"In the past two years, Chinese bids for auto-parts orders have driven customer price targets to a level below our costs on some products," said Larry Denton, chairman and chief executive of Rochester Hills, Mich., parts maker Dura Automotive Systems Inc., at a recent government hearing in the U.S.

..."When we started exporting in 1997, people argued that you couldn't make" auto parts cheaper in China, says Jack Perkowski, chief executive of Beijing-based parts maker Asimco Technologies Ltd. "People also argued that China would never be a large car market."

Now, he says, "the conventional wisdom is that China can copy but not create. That's going to go too."

Bang for the buck

From Paul Kedrosky, the figure below was displayed by Microsoft's Craig Mundie at an analyst presentation. If the numbers are correct, Microsoft is now the leading technology company when it comes to R&D spending. Somehow I can't believe they're getting a decent return on their investment.

For comparison, the entire US high energy physics annual budget is under $1 billion, as is CERN's budget. Total annual US defense spending on R&D (this includes weapon systems, "missile defense", etc.) is about $60 billion.

Intellectual history

I want to recommend a book I've been reading recently, The Eighth Day of Creation by H.F. Judson. It's the most detailed intellectual history of molecular biology I've yet found, covering not just the science but the scientists as well. Someone described it as a New Yorker-style book covering the discovery of DNA, RNA and protein synthesis.

It may be chauvinistic, but I can't help noticing the prominent role played by physicists who crossed over into molecular biology: Bragg, Delbruck, Crick, Wilkins, Gamow, Szilard (yes, the Gamow and Szilard you know from big bang cosmology and the atomic bomb, respectively), Walter Gilbert, etc. The influence of Schrodinger's little book What is Life? is pervasive.

It's hard for me to think of many scientific histories as good as this one, in which the writer has a deep understanding of both the science and the personalities involved. Two examples are Subtle is the Lord (Abraham Pais on Einstein) and QED and the Men Who Made It (Sam Schweber on quantum electrodynamics), but these border on unreadable for the non-specialist. Perhaps Genius, Gleick's biography of Feynman, and The Enigma, Andrew Hodge's biography of Turing, also qualify. Can anyone suggest others?

Fast fox evolution

This Times article describes controlled (should I say directed?) breeding experiments on small mammals such as foxes and rats performed by a Siberian scientist Dmitri Belyaev. Belyaev has managed to produce nice (tame, domesticated) and nasty (aggressive) versions of each animal in a surprisingly short time by simply breeding according to exhibited trait. The tame foxes can even perform difficult cognitive tasks like reading humans well enough to determine what they are looking at -- dogs can do this, but generally smarter chimps cannot. It's a great demonstration of how fast evolution can proceed when selection pressure is strong enough. One of the fascinating side effects of selection based on behavior is that certain physical traits (in foxes: floppy ears, white patches of fur and differently shaped skulls) were also altered, so the tame foxes can be readily identified versus wild or aggressive ones. (I don't see why this would be a priori implausible: the genes for tameness might have superficial physical as well as behavioral effects.)

These results rule out any simple-minded conclusions about whether superficial physical traits might be correlated to cognitive or behavioral traits. Sub-populations that look alike might actually behave alike. Sometimes you can judge a book by its cover :-)

NYTimes: Belyaev chose to test his theory on the silver fox, a variant of the common red fox, because it is a social animal and is related to the dog. Though fur farmers had kept silver foxes for about 50 years, the foxes remained quite wild. Belyaev began his experiment in 1959 with 130 farm-bred silver foxes, using their tolerance of human contact as the sole criterion for choosing the parents of the next generation.

“The audacity of this experiment is difficult to overestimate,” Dr. Fitch has written. “The selection process on dogs, horses, cattle or other species had occurred, mostly unconsciously, over thousands of years, and the idea that Belyaev’s experiment might succeed in a human lifetime must have seemed bold indeed.”

In fact, after only eight generations, foxes that would tolerate human presence became common in Belyaev’s stock. Belyaev died in 1985, but his experiment was continued by his successor, Lyudmila N. Trut. The experiment did not become widely known outside Russia until 1999, when Dr. Trut published an article in American Scientist. She reported that after 40 years of the experiment, and the breeding of 45,000 foxes, a group of animals had emerged that were as tame and as eager to please as a dog.

As Belyaev had predicted, other changes appeared along with the tameness, even though they had not been selected for. The tame silver foxes had begun to show white patches on their fur, floppy ears, rolled tails and smaller skulls.

...There was far more to Belyaev’s experiment than the production of tame foxes. He developed a parallel colony of vicious foxes, and he started domesticating other animals, like river otters and mink. Realizing that genetics can be better studied in smaller animals, Belyaev also started a study of rats, beginning with wild rats caught locally. His rat experiment was continued after his death by Irina Plyusnina. Siberian gray rats caught in the wild, bred separately for tameness and for ferocity, have developed these entirely different behaviors in only 60 or so generations.

The collection of species bred by Belyaev and his successors form an unparalleled resource for studying the process and genetics of domestication. In a recent visit to Novosibirsk, Dr. Brian Hare of the Planck Institute used the silver foxes to probe the unusual ability of dogs to understand human gestures.

If a person hides food and then points to the location with a steady gaze, dogs will instantly pick up on the cue, while animals like chimpanzees, with considerably larger brains, will not. Dr. Hare wanted to know if dogs’ powerful rapport with humans was a quality that the original domesticators of the dog had selected for, or whether it had just come along with the tameness, as implied by Belyaev’s hypothesis.

He found that the fox kits from Belyaev’s domesticated stock did just as well as puppies in picking up cues from people about hidden food, even though they had almost no previous experience with humans. The tame kits performed much better at this task than the wild kits did. When dogs were developed from wolves, selection against fear and aggression “may have been sufficient to produce the unusual ability of dogs to use human communicative gestures,” Dr. Hare wrote last year in the journal Current Biology.

Reich on China growth

Via Economist's View. I think Robert Reich's view is a bit too rosy, although his points are all well taken. Regarding point (1), the top central planners in China are likely quite good, but the system is still such that tremendous resources are misallocated -- for example, into unnecessary property development projects. They are gaining very, very fast in productivity and mastery of new technologies, although the 11 percent number in (2) isn't to be trusted entirely. Regarding point (3), I just watched the movie Syriana and recommend it to anyone who isn't in the oil industry :-)

China Growth

I've been watching the statistics coming out of China about its economic growth. Here are three things you should know. (1) The people managing China's economy (I'm not talking about the politicians but about the financial and economic wizards who are actually making decisions about money supply, capital markets, and the like) are extremely good. They match the best economic minds anywhere in the world. In other words, they know what they're doing. (2) The latest data show China is now growing at a rate faster than 11 percent. That's extraordinary. It's faster than China has been growing for the last five years -- and that was faster than anyone had predicted. China's rate of economic growth is the biggest economic news in the world. (3) That growth is putting huge demands on world energy supplies, and raw materials. Oil prices will continue to rise, as will all other commodities. This is the most important economic fact in the world right now. It is also among the most important political facts in the world.

Intelligence, nature and nurture

Some new data on genetic vs environmental influences on IQ in this recent Times magazine article. Until recently, twins studies could only examine the effect of environmental variation within a limited range -- from working to upper class -- because very poor families are generally not allowed to adopt babies. The effect of family background has been found to recede to almost nothing by late adulthood in these twins studies, but the possibility that severe deprivation might have a stronger effect has not been ruled out. Recent investigations, as detailed below, have focused on very poor families and found a significant effect. We might characterize this as discovering the non-linear region of gene--family environment interaction :-)

NYTimes: A century’s worth of quantitative-genetics literature concludes that a person’s I.Q. is remarkably stable and that about three-quarters of I.Q. differences between individuals are attributable to heredity. This is how I.Q. is widely understood — as being mainly “in the genes” — and that understanding has been used as a rationale for doing nothing about seemingly intractable social problems like the black-white school-achievement gap and the widening income disparity. If nature disposes, the argument goes, there is little to be gained by intervening. In their 1994 best seller, “The Bell Curve,” Richard Herrnstein and Charles Murray relied on this research to argue that the United States is a genetic meritocracy and to urge an end to affirmative action. Since there is no way to significantly boost I.Q., prominent geneticists like Arthur Jensen of Berkeley have contended, compensatory education is a bad bet.

...When quantitative geneticists estimate the heritability of I.Q., they are generally relying on studies of twins. Identical twins are in effect clones who share all their genes; fraternal twins are siblings born together — just half of their genes are identical. If heredity explains most of the difference in intelligence, the logic goes, the I.Q. scores of identical twins will be far more similar than the I.Q.’s of fraternal twins. And this is what the research has typically shown. Only when children have spent their earliest years in the most wretched of circumstances, as in the infamous case of the Romanian orphans, treated like animals during the misrule of Nicolae Ceausescu, has it been thought that the environment makes a notable difference. Otherwise, genes rule.

Then along came Eric Turkheimer to shake things up. Turkheimer, a psychology professor at the University of Virginia, is the kind of irreverent academic who gives his papers user-friendly titles like “Spinach and Ice Cream” and “Mobiles.” He also has a reputation as a methodologist’s methodologist. In combing through the research, he noticed that the twins being studied had middle-class backgrounds. The explanation was simple — poor people don’t volunteer for research projects — but he wondered whether this omission mattered.

Together with several colleagues, Turkheimer searched for data on twins from a wider range of families. He found what he needed in a sample from the 1970’s of more than 50,000 American infants, many from poor families, who had taken I.Q. tests at age 7. In a widely-discussed 2003 article, he found that, as anticipated, virtually all the variation in I.Q. scores for twins in the sample with wealthy parents can be attributed to genetics. The big surprise is among the poorest families. Contrary to what you might expect, for those children, the I.Q.’s of identical twins vary just as much as the I.Q.’s of fraternal twins. The impact of growing up impoverished overwhelms these children’s genetic capacities. In other words, home life is the critical factor for youngsters at the bottom of the economic barrel. “If you have a chaotic environment, kids’ genetic potential doesn’t have a chance to be expressed,” Turkheimer explains. “Well-off families can provide the mental stimulation needed for genes to build the brain circuitry for intelligence.”

Plumbing for credit derivatives

This Economist article describes backend problems in the rapidly growing market for credit derivatives. The idea of keeping track of billion dollar trades on scraps of paper seems alarming to me. On the other hand I know how hard it is to build an IT infrastructure for a growing business.

I discussed credit derivatives previously here.

One of the world's fastest-growing markets calls in the plumbers

OVER a year ago, a whiff of something nasty filled the nostrils of the world's financial regulators. It came, appropriately, from the back end of the credit-derivatives market, an unregulated asset class that was growing so fast that banks and hedge funds that dabbled in it had lost track of their trades.

In other markets where trading is private (rather than on an exchange), the problem might have seemed minor, involving thankless back-office tasks with monotonous names like matching and confirmation. But this time regulators saw a threat to the stability of banks, because of the popularity of credit-default swaps (CDSs), instruments that disperse lending risk around the financial system.

From almost nothing in 2000, trading in CDSs has ballooned to a notional value of $17 trillion at the latest count. That still leaves plenty of room to grow—interest-rate swaps, for example, are a $160 trillion market. But the CDS market, which allows banks and other financial firms to buy and sell protection against the risk of default by a borrower, punches above its weight. According to the International Monetary Fund, it is far bigger than the world's corporate-bond markets, it helps set the cost of borrowing by companies, and it might even reduce swings in the credit cycle.

For all its virtues, however, its practitioners have been hopeless at keeping tabs on their own trades, especially in the secondary market into which hedge funds have stormed. So last year regulators pressed the industry's 14 top dealers, which they do supervise, to put their rubber gloves on and sort out the plumbing.

Since then, bankers in a group known, like a ruling clique, as the “14 families” have laboured to Dyno-Rod a backlog of unconfirmed swap trades. Prodded by regulators, traders have shut themselves in hotel rooms for one weekend after another to sort out discrepancies with their counterparties. Some banks have even temporarily halted trading to allow the back offices to catch up with the sales staff.

The efforts appear to have borne fruit. The main dealers agreed to a June 30th deadline to cut the backlog of unconfirmed trades by 70% from their levels when they were first summoned by the Federal Reserve Bank of New York last September. “Everyone's already achieved that, as far as I know,” said Mark Davies, head of global credit trading at Bear Stearns, one of the firms.

Yet the smell has not quite gone away. Last month Alan Greenspan, former chairman of the Federal Reserve, startled bond traders at a dinner in New York with both a friendly pat and a slap on the wrist. Credit derivatives, he gushed, were “becoming the most important instruments I've seen in decades.” But he then went on to say how appalled he was at the “19th-century technology” used to trade credit-default swaps, with deals done over the phone and on scraps of paper. In London the Financial Services Authority, which has warned that unconfirmed trades could cause liquidity problems and accelerate a financial crisis, is partially mollified. “We've gone from a red light to an amber,” an official says.

It goes without saying that an automated, transparent back-office system is a good way to bring new investors into the market and improve liquidity. There is also broad support for the way regulators have let banks find their own answers to the problems, rather than imposing rules.

But the supervisory oversight, as well as the solutions dreamt up by the big dealers, make some people nervous. They think there may be subtle changes in the $220 trillion market for over-the-counter derivatives, which is unregulated because it involves trades between private parties.

A good deal of the grumbling comes from hedge funds. Some of these, bankers say, have unsuccessfully resisted moves to automated trading, preferring to keep details of their trades to themselves and to play dealers off against each other.

There are other worries. At the centre of the complex trading infrastructure is a vast industry-owned utility called the Depository Trust & Clearing Corporation (DTCC), which the 14 dealers—ten of which have seats on its board—see as integral to automation (handily, it gave such users a rebate of $528m last year). The utility seeks to stitch together electronic platforms that stretch from traders' desks, through confirmation, to storing records of derivative contracts until they expire.

DTCC's prevalence has led to concerns that putting so much global information into storage in America may one day make the industry subject to American regulation, no matter where trades take place. The utility's Peter Axilrod believes this fear is unwarranted. He points to the light touch shown by supervisors so far.

Another fear is that DTCC might trample on private competitors as it moves into other areas of derivatives. When it began testing a system for recording initial agreements to trade last month, it described itself as a tap-dancing gorilla.

Such concerns are likely to loom larger now that the backlog of paperwork has been reduced. Of particular interest is whether DTCC will realise its ambitions to store hundreds of trillions of dollars' worth of credit, equity and interest-rate derivative contracts. This would be a hugely complex task: already it admits that its plans to warehouse CDS contracts are taking longer than expected. And the rest of the world might well worry that too much of the plumbing of a global market would be on American soil.

Let the machines do it

Via an intrepid correspondent... the Times covers numerous areas where human "experts" are outperformed by machine intelligence. Of course, we're not quite ready to let the machines take over just yet. I think IA = Intelligence Amplification is much more promising in the short term than AI = Artifical Intelligence.

NYTimes: Do you think your high-paid managers really know best? A Dutch sociology professor has doubts.

The professor, Chris Snijders of the Eindhoven University of Technology, has been studying the routine decisions that managers make, and is convinced that computer models, by and large, can do a better job of it. He even issued a challenge late last year to any company willing to pit its humans against his algorithms.

“As long as you have some history and some quantifiable data from past experiences,” Mr. Snijders claims, a simple formula will soon outperform a professional’s decision-making skills. “It’s not just pie in the sky,” he said. “I have the data to support this.”

Some of Mr. Snijders’s experiments from the last two years have looked at the results that purchasing managers at more than 300 organizations got when they placed orders for computer equipment and software. Computer models given the same tasks achieved better results in categories like timeliness of delivery, adherence to the budget and accuracy of specifications.

No company has directly taken Mr. Snijders up on his challenge. But a Dutch insurer, Interpolis, whose legal aid department has been expanding rapidly in recent years, called in Mr. Snijders to evaluate a computer model it had designed to automate the routing of new cases — a job previously handled manually by the department’s in-house legal staff.

The manager in charge of the project, Ludo Smulders, said the model was much faster and more accurate than the old system. “We’re very satisfied about the results it’s given our organization,” he said. “That doesn’t mean there are no daily problems, but the problems are much smaller than when the humans did it by hand. And it lets them concentrate more on giving legal advice, which is what their job is.”

Mr. Snijders’s work builds on something researchers have known for decades: that mathematical models generally make more accurate predictions than humans do. Studies have shown that models can better predict, for example, the success or failure of a business start-up, the likelihood of recidivism and parole violation, and future performance in graduate school.

They also trump humans at making various medical diagnoses, picking the winning dogs at the racetrack and competing in online auctions. Computer-based decision-making has also grown increasingly popular in credit scoring, the insurance industry and some corners of Wall Street.

The main reason for computers’ edge is their consistency — or rather humans’ inconsistency — in applying their knowledge.

“People have a misplaced faith in the power of judgment and expertise,” said Greg Forsythe, a senior vice president at Schwab Equity Ratings, which uses computer models to evaluate stocks.

The algorithms behind so-called quant funds, he said, act with “much greater depth of data than the human mind can. They can encapsulate experience that managers may not have.” And critically, models don’t get emotional. “Unemotional is very important in the financial world,” he said. “When money is involved, people get emotional.” Many putative managerial qualities, like experience and intuition, may in fact be largely illusory. In Mr. Snijders’s experiments, for example, not only do the machines generally do better than the managers, but some managers perform worse over time, as they develop bad habits that go uncorrected from lack of feedback.

Other cherished decision aids, like meeting in person and poring over dossiers, are of equally dubious value when it comes to making more accurate choices, some studies have found, with face-to-face interviews actually degrading the quality of an eventual decision.

“People’s overconfidence in their ability to read someone in a half-an-hour interview is quite astounding,” said Michael A. Bishop, an associate professor of philosophy at Northern Illinois University who studies the social implications of these models.

And the effects can be serious. “Models will do much better in predicting violence than will parole officers, and in that case, not using them leads to a more dangerous society,” he said. “But people really don’t believe that the models are as accurate as they are.”

Models have other advantages beyond their accuracy and consistency. They allow an organization to codify and centralize its hard-won knowledge in a concrete and easily transferable form, so it stays put when the experts move on. Models also can teach newcomers, in part by explaining the individual steps that lead to a given choice. They are also faster than people, are immune to fatigue and give the human experts more time to work on other tasks beyond the current scope of machines.

So if they’re so good, why aren’t they already used everywhere?

Not everyone is convinced that managers are incorrigibly myopic. “I’ve never seen any evidence that there is a pattern of decline at all, and it just doesn’t fit with the way management literature is going, which is all around the emotional intelligence angle,” said Laura Empson, the director of the Clifford Chance Center of the Said Business School at Oxford University.

“I think there are a lot of people who have a strong technological orientation who would agree life would be a lot simpler if it weren’t for the humans,” she said. “But the reality is, organizations do have a lot of very intense and complicated human issues within them.”

Max H. Bazerman, a professor at Harvard Business School, wonders how many managerial decisions can actually be modeled. “The vast majority of decisions that we make in professional life don’t have this quality,” he said.

He agrees that models can make better decisions about credit card applications and college admissions, he said, “but there are many decisions that are much more unique, where that database doesn’t exist. I’m as skeptical about human intuition as these folks, but it’s not only a computer model that we replace it with. Sometimes it’s thinking more clearly.”

Many in the field of computer-assisted decision-making still refer to the debacle of Long Term Capital Management, a highflying hedge fund that counted several Nobel laureates among its founders. Its algorithms initially mastered the obscure worlds of arbitrage and derivatives with remarkable skill, until the devaluation of the Russian ruble in 1998 sent the fund into a tailspin.

“As long as the underlying conditions were in order, the computer model was almost like a money machine,” said Roger A. Pielke Jr., a professor of environmental studies at the University of Colorado whose work focuses on the relation between science and decision-making. “But when the assumptions that went into the creation of those models were violated, it led to a huge loss of money, and the potential collapse of the global financial system.”

In such situations, “you can never hope to capture all of the contingencies or variables inside of a computer model,” he said. “Humans can make big mistakes also, but humans, unlike computer models, have the ability to recognize when something isn’t quite right.”

Another problem with the models is the issue of accountability. Mr. Forsythe of Schwab pointed out that “there’s no such thing as a 100 percent quantitative fund,” in part because someone has to be in charge if the unexpected happens. “If I’m making decisions,” he said, “I don’t want to give up control and say, ‘Sorry, the model told me.’ The client wants to know that somebody is behind the wheel.”

Still, some consider the continuing ascendance of models as inevitable, and recommend that people start figuring out the best way to adapt to the role reversal. Mark E. Nissen, a professor at the Naval Postgraduate School in Monterey, Calif., who has been studying computer-vs.-human procurement, sees a fundamental shift under way, with humans becoming increasingly peripheral in making routine decisions, concentrating instead on designing ever-better models.

“The newest space, and the one that’s most exciting, is where machines are actually in charge, but they have enough awareness to seek out people to help them when they get stuck,” he said — for example, when making “particularly complex, novel, or risky” decisions.

The ideal future, then, may lie in letting computers and people each do what they do best. One way to facilitate this development is to train people to identify the typical cognitive foibles that lead to bad choices. “I’ve now worked with these models for so long,” Mr. Snijders said, “that my instincts have changed along the way.”

As Mr. Bishop of Northern Illinois University puts it, by making smart use of computer models’ advantages, “you’ll become like the crafty A student who doesn’t work that hard but gets mostly right answers, rather than the really hard-working student who gets lots of wrong answers and as a result gets C’s.”

Mutants among us

Prosopagnosia, or face blindness, affects a surprisingly large fraction (few percent) of the population. Those who suffer from it have difficulty in distinguishing human faces, except by conscious effort (recalling particular features, or contextual clues). Preliminary evidence is that (a) we have a specialized module in our brains for face recognition and (b) there are one or more alleles (gene variants) which disable this function to various degrees.

How could these alleles survive selection? One would guess that face blindness is an evolutionary handicap, at least to some degree (although perhaps less so in small hunter gatherer groups, or in theoretical physics ;-). Is there a compensating advantage provided by the mutation?

It's fascinating to consider how many other strange cognitive mutations are present in our population at the percent (or fraction of percent level). Memory? Musical ability? Specialized mathematical ability (e.g., visualizing geometrical shapes, or a "feel" for magnitudes of quantities, or lightning calculation)?

I suspect we'll find more and more of these, and their associated alleles, as time goes by. See GNXP.com for more discussion and references.

It just occurred to me that there are likely dozens of readers of this blog who have prosopagnosia. Would anyone care to share their (anonymous) comments on how they adjust to the condition, and when they noticed having it?

NYTimes: Dr. Sellers, a professor of English at Hope College in Holland, Mich., has a disorder called prosopagnosia, or face blindness, and she has had it since birth. “I see faces that are human,” she said, “but they all look more or less the same. It’s like looking at a bunch of golden retrievers: some may seem a little older or smaller or bigger, but essentially they all look alike.”

Face blindness can be a rare result of a stroke or a brain injury, but a study published in the July issue of The American Journal of Medical Genetics Part A is the first report of the prevalence of a congenital or developmental form of the disorder.

The researchers say the phenomenon is much more common than previously believed: they found that 2.47 percent of 689 randomly selected students in Münster, Germany, had the disorder.

Dr. Thomas Grüter, a co-author of the paper, said there were reasons to believe that the condition was equally common in other populations. “First,” he said, “our population was not selected in terms of cognition deficits. And second, a study done by Harvard University with a different diagnostic approach yielded very similar figures.”

Dr. Grüter is himself prosopagnosic. His wife and co-author, Dr. Martina Grüter of the Institute for Human Genetics at the University of Münster, did not realize he was face blind until she had known him more than 20 years. The reason, she says, is he was so good at compensating for his deficits.

“How do you recognize a face?” she asked. “For most people, this is a silly question. You just do. But people who have prosopagnosia can tell you exactly why they recognize a person. Thomas consciously looks for the details that others notice unconsciously.”

Sun Valley: Predators' Ball 2.0

Herb Allen (of boutique investment bank Allen & Company) has traditionally run a summer retreat for the biggest movers and shakers in media and technology in Sun Valley, Idaho. This year it's reported that financiers (hedge fund and private equity guys) have invaded the party, perhaps signalling a shift in the balance of power.

I once hoped for an invite to this event a few years ago, as one of our startup's investors (a hedge fund) was a regular attendee and its founder an Allen & Co. alumnus. Alas, the invite never materialized, but I did have several meetings with Allen & Co. bankers (one of whom turned out to be the son of a famous former Treasury Secretary) in their luxurious Manhattan offices and our not so exciting Oakland digs. The Manhattan offices are lushly carpeted, wood paneled and sound proofed. A remote control device on the oak table let us order coffee and drinks, delivered by a uniformed servant, during the meeting.

NYTimes DealBook: The annual mogul-fest that Allen & Company holds here every year is best known for A-list attendees like Rupert Murdoch of the News Corporation, Richard D. Parsons of Time Warner, Howard Stringer of Sony and Sumner M. Redstone of Viacom.

It is a conference that has taken on an almost supernatural reputation for deal making amid barbecues, discussion panels and whitewater rafting. (The seeds of Walt Disney’s acquisition of ABC/Capital Cities in 1996 were sown here.) But this year, the ones to watch were deep-pocketed people you may have never heard of, from the world of private equity and hedge funds.

“I walked into dinner last night and didn’t recognize three-quarters of the people there,” said the chief executive of one of the world’s largest media companies, who refused to speak on the record for fear of upsetting Herbert A. Allen of Allen & Company, who frowns on attendees talking publicly about the invitation-only conference, which ended yesterday. “It was all these hedge fund and finance guys I had never met before. The balance of power is shifting.”

The guest list at Mr. Allen’s conference, which began in 1982, may be the ultimate barometer of where the center of influence lies in corporate America and on Wall Street. For most of the 1980’s and early 90’s, the power players were Hollywood studio moguls like Barry Diller, then of Paramount, and Michael D. Eisner, formerly of Disney. By the mid-1990’s, cable and telecommunications executives like John C. Malone of Liberty Media and Brian L. Roberts of Comcast became the belles of the ball. In the late 1990’s and early 2000’s, technology and Internet executives like Stephen M. Case, the founder of America Online, and Jerry Yang, co-founder of Yahoo, were drawing crowds at the hotel bar.

Now, flush with billions in cash and the ability to borrow heavily on top of that, the private equity bigwigs and hedge fund managers have become the stars. Call it Predators’ Ball 2.0 — a kind of outdoorsy reprise of Michael Milken’s famous gathering of leveraged-buyout mavens of the 1980’s.

“We used to come here every year to sniff each other,” said the chief executive of another media company, who also did not want his name used. “Now, all these finance people are sniffing us.” With media stocks down virtually across the board, some may smell opportunity.

Physical limits on information processing

http://arxiv.org/abs/hep-th/0607082

Physical limits on information processing

Authors: Stephen D.H. Hsu

We derive a fundamental upper bound on the rate at which a device can process information (i.e., the number of logical operations per unit time), arising from quantum mechanics and general relativity. In Planck units a device of volume V can execute no more than the cube root of V operations per unit time. We compare this to the rate of information processing performed by nature in the evolution of physical systems, and find a connection to black hole entropy and the holographic principle.

AdSense arbitrage

You may have noticed little Google ads on this blog, placed through their AdSense program. A snippet of JavaScript on this page is executed by your browser, which then loads the appropriate text ads from a Google server. The ads are targeted by key word and the placement of the ads is sold in a key word auction to advertisers.

I put the ads up mainly out of curiosity -- as you know, I am fascinated by all things Google :-)

What I've learned recently is that certain key words are very valuable. If you are reading this blog and see ads related to, e.g., hedge funds, derivatives, FX trading, volatility, etc., you can send me a dollar just by clicking! Please click early and often -- you'll be transferring funds from rapacious luxocrats to a humble physics professor ;-)

Bruce Schneier has a nice article on click fraud (Google's greatest weakness at the moment) in Wired News. Note I am not encouraging click fraud -- my readers really are interested in stochastic volatility and Black-Scholes :-)

Google's $6 billion-a-year advertising business is at risk because it can't be sure that anyone is looking at its ads. The problem is called click fraud, and it comes in two basic flavors...

But the overarching problem is both hard to solve and important: How do you tell if there's an actual person sitting in front of a computer screen? How do you tell that the person is paying attention, hasn't automated his responses, and isn't being assisted by friends? Authentication systems are big business, whether based on something you know (passwords), something you have (tokens) or something you are (biometrics). But none of those systems can secure you against someone who walks away and lets another person sit down at the keyboard, or a computer that's infected with a Trojan.

More income inequality

Latest on income inequality (via Economist's View). From 2003-2004 the top 1% gained 17%, while the other 99% of the population barely advanced. So, the fruits of economic growth went overwhelmingly to a small group. The top 1% of earners now account for about 20% of pre-tax earnings. IIRC, the threshold for the top 1% is about $275k per annum.

Economists Thomas Piketty and Emmanuel Saez have recently made available an updated version of their groundbreaking data series on U.S. income inequality.[1] The data are unique because of the detailed information they provide regarding income gains at the top of the income spectrum, and also because they extend back to 1913. By contrast, widely used Census data on income developments do not capture income trends among the top one percent of households and go back only to the end of World War II.

...The Piketty and Saez data offer the first real snapshot of income trends among those at the pinnacle of the income spectrum in 2004. The data show that income gains between 2003 and 2004 were particularly large for those at the very top of the income spectrum, resulting in a nearly unprecedented one-year increase in income concentration.[3] The Piketty and Saez data show:

1) From 2003 to 2004, the average incomes of the bottom 99 percent of households grew by less than 3 percent, after adjusting for inflation.

2) In contrast, the average incomes of the top one percent of households experienced a jump of almost 17 percent, after adjusting for inflation. (Census data show that real median income fell between 2003 and 2004. Average income is pulled up by gains at the top of the income spectrum; the 3 percent rise among the bottom 99 percent seems to largely reflect gains by households in the top quintile of the income spectrum. In contrast, trends in median income capture the experience of households in the middle of the income spectrum.)

3) The top one percent of households garnered 36 percent of the income gains in 2004.

Gdrive: codename Platypus

Gdrive, a Web-based hard drive application from Google, has been rumored for some time. Enterprising sleuths seem to have found some interesting tidbits -- looks like client software that syncs with a distributed storage network. I can't wait for mine! (Although, do you want all your files searchable by Google?)

This is what was on the page just some hours ago (the page isn’t active anymore, but Corsin made a backup):

Platypus (Gdrive)

A filer for the world. But better.

Storing your files in Platypus has a number of advantages over storing your files on either your C: drive or filer.

Backup. If you lose your computer, grab a new one and reinstall Platypus. Your files will be on your new machine in minutes.

Sync. Keep all your machines synchronized, even if they run different operating systems.

VPN-less access. Not at a Google computer? View your files on the web at http://troutboard.com/p.

Collaborate. Create shared spaces to which multiple Googlers can write.

Disconnected access. On the plane? VPN broken? All your files are still accessible.

The page also offers you to “find a new bug, get a free Platypus t-shirt!” and to browse a Platypus share with your username or group name. The Gdrive download is available for Windows, Mac and Linux. Within the page source, Google author Justin Rosenstein is listed (Justin was Product Manager for Google Page Creator). Also, a couple of feature listings are hidden as comments in the source:

Publish. All of the files you store on Platypus are automatically accessible from the (corporate) web.

Share. Other Googlers can mount your Platypus folders and open your files in read-only mode.

Collaborate. ... It also has advantages over storing your files in your filer or WWW directory

Local IO speeds. Open and save as quickly as you could if you were accessing them from your C: drive.

Can we say good-bye to traditional methods of saving files, accessing them, and creating backups?

Hedge fund mania

Mysterious hedge funds have reached the popular consciousness! Details magazine, in an article entitled The New American Class System, profiles the new money "luxocrats" that have risen to the top of our winner take all economy.

Don't miss the slideshow field guide to luxocrats -- the hedge fund guy, the Silicon Valley geek, the old money trust funder, the struggling guy trying to make it in NYC on $500k a year.

The article is, of course, more amusing than realistic, but see here and here for accurate numbers on the US wealth distribution. If you want something even meatier, read this Economist article on the growth of credit derivatives (hedge funds are the largest traders of credit derivatives). The CDS (Credit Default Swap) market has a notional value of $17 trillion now! If I recall correctly, this market barely existed five years ago.

Bonus! Here's a long profile of James Simons, one of the most successful hedge fund managers of all time (via the new blog angryphysics; where's the anger, though? :-) The article has some interesting details about a lawsuit between Renaissance and two former employees, physics guys trained at MIPT (one of Landau's institutes in Moscow) and MIT.

Details: You know this guy from college. He runs a hedge fund—let's call it Colossal Capital Strategies—and if you’re diligent or masochistic enough to do a little research, you’ll find out that he made about $100 million last year. He’s 36 years old and he has billions under management. The Wall Street Journal likes to refer to him as a “high-net-worth individual,” but that wording seems so odd and clunky—the sort of boardroom-pretzel terminology that some lawyer from Enron might use to explain away a phantom transaction.

No. Let’s call him a luxocrat.

A luxocrat is not merely rich, but rich in a way that you couldn’t have imagined back in college. (Otherwise you would have been nicer to the guy.) He’s rich enough to guarantee that every calorie that passes his lips has been fussed over by a master chef, rich enough to share his truffled foie gras with the treasury secretary on a Gulfstream headed for Barbados, rich enough to impulse-buy doodads from the Robb Report with a black American Express Centurion card (whose existence you weren’t even aware of), rich enough to make your proud and dutiful little 401(k) look, in comparison, like the mound of coins that an Appalachian beet farmer might stash away in a pickle jar. A luxocrat is enormo-rich, robber-baron rich, 21st-century rich, swelled up with a wealth of such magnitude that it suggests the American class system would have to undergo a wholesale restructuring in order to accommodate it.

Which is arguably what’s happening at this very moment.

Usually when you read about the widening gap between the rich and the poor, you think of the insane chasm that yawns between, say, Bill Gates and a laborer trying to subsist on a few cents a day on the Bangladeshi floodplain. But there is a new status gap opening up in the American consciousness, one in which a young guy can earn or inherit what used to be a very respectable sum (well into the six figures, for instance) and still feel as if he’s stuck on a treadmill of perpetual proletarian worry and strain. Meanwhile he can’t help but notice that he’s got ultra-wealthy contemporaries (hedge-funders, real-estate moguls, Google geeks, surfers who retired at 30 after hitting the Silicon Valley jackpot) whose daily activities seem to have been shorn of all that worry and strain. The end result is the emergence of a different social pecking order—one in which the new money dwarfs the old, in which the currency-market globalists and Palo Alto venture capitalists conquer the top while even the Hollywood producers and Merrill Lynch desk jockeys get shunted to the bottom. A world in which the old rituals and pathways to success have been vaporized.

Let’s begin with the luxocrat’s home. “There is so much more big money in the hands of younger people,” says Pam Liebman, the president and CEO of the Corcoran Group, a national real-estate powerhouse with almost $12 billion in annual sales. “And their way of thinking is much less traditional.” In Manhattan, for instance, every budding tycoon used to lust for the pedigreed grandeur of an apartment in one of the pre-war buildings on Fifth Avenue or Park Avenue; he could acquire status by living in proximity to old money. Now “old,” in any configuration, has lost its allure. The luxury tyro’s ideal habitat is a brand-new, Lysol-scrubbed steel-and-glass monument to personal service. (Consider the residential tower that Eric Packer, the cold-blooded 28-year-old billionaire in Don DeLillo’s 2003 novel Cosmopolis, likes to call home: “It had the kind of banality that reveals itself over time as being truly brutal. He liked it for this reason.”) Even if the building happens to be beautiful and designed by Richard Meier or Santiago Calatrava, what you see from the outside is less important than what the luxocrat has access to on the inside. The magic word is amenities: He wants a concierge, a pool, a gym, a spa, a playroom for the kids, valet parking, haute cuisine delivered to his door. He wants things taken care of. He wants, in effect, to live in a five-star hotel 24-7. You can’t really blame him. “People are busy, and they want to be pampered,” Liebman says. “They want things that make their lives easier, more pleasant.”

Children

Khalil Gibran (1883-1931), The Prophet.

Your children are not your children.

They are the sons and daughters of Life's longing for itself.

They come through you but not from you,

And though they are with you, yet they belong not to you.

You may give them your love but not your thoughts.

For they have their own thoughts.

You may house their bodies but not their souls,

For their souls dwell in the house of tomorrow, which you cannot visit, not even in your dreams.

You may strive to be like them, but seek not to make them like you.

For life goes not backward nor tarries with yesterday.

Hollywood genius

Physicist turned author and screenwriter Leonard Mlodinow has a nice article in the LA Times on the hit or miss nature of the movie industry. He recapitulates the myth of expertise as it applies to studio executives, whom he compares to dart throwing monkeys (a la fund managers in finance).

Mlodinow wrote a charming memoir about his time as a postdoc at Caltech in the early 1980s. Fresh from Berkeley, having written a PhD dissertation on the large-d expansion (d is the number of dimensions), he was in over his head at Caltech, but found a friend and mentor in the ailing Richard Feynman.

We all understand that genius doesn't guarantee success, but it's seductive to assume that success must come from genius. As a former Hollywood scriptwriter, I understand the comfort in hiring by track record. Yet as a scientist who has taught the mathematics of randomness at Caltech, I also am aware that track records can deceive.

That no one can know whether a film will hit or miss has been an uncomfortable suspicion in Hollywood at least since novelist and screenwriter William Goldman enunciated it in his classic 1983 book "Adventures in the Screen Trade." If Goldman is right and a future film's performance is unpredictable, then there is no way studio executives or producers, despite all their swagger, can have a better track record at choosing projects than an ape throwing darts at a dartboard.

That's a bold statement, but these days it is hardly conjecture: With each passing year the unpredictability of film revenue is supported by more and more academic research.

That's not to say that a jittery homemade horror video could just as easily become a hit as, say, "Exorcist: The Beginning," which cost an estimated $80 million, according to Box Office Mojo, the source for all estimated budget and revenue figures in this story. Well, actually, that is what happened with "The Blair Witch Project" (1999), which cost the filmmakers a mere $60,000 but brought in $140 million—more than three times the business of "Exorcist." (Revenue numbers reflect only domestic receipts.)

What the research shows is that even the most professionally made films are subject to many unpredictable factors that arise during production and marketing, not to mention the inscrutable taste of the audience. It is these unknowns that obliterate the ability to foretell the box-office future.

But if picking films is like randomly tossing darts, why do some people hit the bull's-eye more often than others? For the same reason that in a group of apes tossing darts, some apes will do better than others. The answer has nothing to do with skill. Even random events occur in clusters and streaks.

...If the mathematics is counterintuitive, reality is even worse, because a funny thing happens when a random process such as the coin-flipping experiment is actually carried out: The symmetry of fairness is broken and one of the films becomes the winner. Even in situations like this, in which we know there is no "reason" that the coin flips should favor one film over the other, psychologists have shown that the temptation to concoct imagined reasons to account for skewed data and other patterns is often overwhelming.

...Actors in Hollywood understand best that the industry runs on luck. As Bruce Willis once said, "If you can find out why this film or any other film does any good, I'll give you all the money I have." (For the record, the film to which he referred, 1993's "Striking Distance," didn't do any good.) Willis understands the unpredictability of the film business not simply because he's had box-office highs and lows. He knows that random events fueled his career from the beginning, and his story offers another case in point...