The price of eggs

Eggs purchased from female donors at elite universities are several times more expensive.



From Aaron D. Levine, "Self-Regulation, Compensation, and the Ethical Recruitment of Oocyte Donors," Hastings Center Report 40, no 2 (2010): 25-36. More.

Crete talk: black hole information and decoherence

I finally posted my contributions to the proceedings of the meeting I attended last summer in Crete (see here for talk slides). I waited until now because the contributions were refereed and only recently went to the publisher.

A reaction I sometimes get to this work is that: "Maldacena showed in AdS that measuring a certain correlator can determine whether black hole evolution is unitary (i.e., whether information is destroyed)". That is correct, but the measurement precision needs to be of order exp(-S), where S is the entropy of the hole. It turns out that is the same level of precision one needs to detect decohered Everett branches of a system with S degrees of freedom.

http://arxiv.org/abs/1003.5382

The black hole information paradox and macroscopic superpositions

We investigate the experimental capabilities required to test whether black holes destroy information. We show that an experiment capable of illuminating the information puzzle must necessarily be able to detect or manipulate macroscopic superpositions (i.e., Everett branches). Hence, it could also address the fundamental question of decoherence versus wavefunction collapse.

Comments: 10 pages, 4 figures, contribution to proceedings of the 1st Mediterranean Conference on Classical and Quantum Gravity.

SES and IQ

It is sometimes claimed that IQ is just a proxy for SES (Socioeconomic Status): high IQ kids are merely the beneficiaries of better home environments, and correlations between IQ and life outcomes are merely a proxy for correlations between childhood environment and outcomes. Of course, this claim does not address the significant variations in IQ within families. Does IQ have predictive power once SES is controlled for? The answer is obvious from anecdotal experience: we all know siblings who, by definition, shared the same family SES, but with different IQs and life outcomes.

Vanderbilt psychologist David Lubinski sent me a recent paper addressing this issue -- Cognitive Epidemiology: With emphasis on untangling cognitive ability and socioeconomic status, Intelligence 37 (2009) 625-633. Among the figures is the following one, based on NLSY longitudinal data (the analysis was originally done by Charles Murray). It compares life outcomes of paired siblings (one of each pair required to be in the "normal" reference range of IQ), thereby controlling for SES. As you can see, IQ has a strong impact on education, earnings and social status even after family SES is controlled for. (Click for larger version of figure.)



Similar analysis shows that the correlation between SAT scores and college grades only decreases slightly when SES is controlled for. See also related post on non-shared environmental effects.

Below is a useful SES-SAT (or SES-IQ) syllogism, well supported by the studies at the two links above.

SES does not cause SAT (weakly at most). ***
SES does not predict college success, SAT does.

*** Someone recently asked me about the non-genetic or environmental variation in adult IQ. If we use a heritability range of .7-.9 (as deduced in twin studies; applicable for IQ in late adulthood and over a large range of childhood environments), then the resulting distribution of IQ differences for monozygotic twins would have SD = 5-8 points. That is, clones raised in different families would typically have IQ differences of around 5-8 points. Note, though, that very little of this difference is accounted for by SES -- most of it is due to non-shared environmental effects. For a lower heritability value such as .5 (perhaps more appropriate for adolescents), the expected variation between clones would be about 10 points.

Photos and long term memory

Why do I take photos of my kids or while traveling? The answer is so obvious it's a cliche: for the memories.

Remarkably, long term human memory capacity is probably only on the order of a gigabyte. Ask yourself what specific things you remember from the year 2000. How about your first day of class in college?

While the brain processes huge amounts of data (dominated by visual input), the amount of information retained is quite small -- no more than a few bits per second. When I ask you what your date's prom dress looked like, you are probably reconstructing it from conceptual primitives like "blue", "prom dress", or "velvet" :-) Do you think you could picture all of the girls you ever dated if you hadn't looked at their photographs over the years?

Looking at old photographs allows me to recapture and enhance otherwise elusive memories. Also, there's some selection bias in what one chooses to photograph: the memories triggered tend to be happy ones. Viva Espana! :-)

Human Memory: ... This makes the work of Thomas K. Landauer very interesting, for he has entirely avoided this hardware guessing game by measuring the actual functional capacity of human memory directly (See "How Much Do People Remember? Some Estimates of the Quantity of Learned Information in Long-term Memory", in Cognitive Science 10, 477-493, 1986).

Landauer works at Bell Communications Research--closely affiliated with Bell Labs where the modern study of information theory was begun by C. E. Shannon to analyze the information carrying capacity of telephone lines (a subject of great interest to a telephone company). Landauer naturally used these tools by viewing human memory as a novel "telephone line" that carries information from the past to the future. The capacity of this "telephone line" can be determined by measuring the information that goes in and the information that comes out, and then applying the great power of modern information theory.

Landauer reviewed and quantitatively analyzed experiments by himself and others in which people were asked to read text, look at pictures, and hear words, short passages of music, sentences, and nonsense syllables. After delays ranging from minutes to days the subjects were tested to determine how much they had retained. The tests were quite sensitive--they did not merely ask "What do you remember?" but often used true/false or multiple choice questions, in which even a vague memory of the material would allow selection of the correct choice. Often, the differential abilities of a group that had been exposed to the material and another group that had not been exposed to the material were used. The difference in the scores between the two groups was used to estimate the amount actually remembered (to control for the number of correct answers an intelligent human could guess without ever having seen the material). Because experiments by many different experimenters were summarized and analyzed, the results of the analysis are fairly robust; they are insensitive to fine details or specific conditions of one or another experiment. Finally, the amount remembered was divided by the time allotted to memorization to determine the number of bits remembered per second.

The remarkable result of this work was that human beings remembered very nearly two bits per second under all the experimental conditions. Visual, verbal, musical, or whatever--two bits per second. Continued over a lifetime, this rate of memorization would produce somewhat over 10^9 bits, or a few hundred megabytes.

Hangzhou photos

Below are some photos from Hangzhou (click for larger versions). Earlier in the week we had more meetings at Alibaba concerning the establishment of a social science data observatory sponsored by Alibaba Cloud Computing, making use of data from Taobao, Alipay, Alibaba.com, etc. Related post.

See here for an Economist special report on the Big Data deluge. Imagine you could observe 100+ million users transacting on a platform with 400 million item listings -- Taobao volume equals about 2 percent of all Chinese retail sales, and growing fast :-)

Yesterday we gave a presentation to about 200 technical staff who were attending a company-wide retreat at a resort outside Hangzhou.

Today I have to turn my physics brain back on as I am giving a talk at Zhejiang University (Zheda). Until now I have been simulating a social / computational scientist :-)

Two book recommendations

Very high recommendations for the following books, which I stockpiled in advance of my trip to China, but ended up reading most of before departure...

The Lost Books of the Odyssey by Zachary Mason, a Borges-ian retelling of the Odyssey in 44 short stories.

Perfect Rigor by Masha Gessen, on Grisha Perelman and the proof of the Poincare / Geometrization Conjecture. I already knew a bit about the completion of Hamilton's program thanks to colleagues who work on Ricci flow. What I found most interesting in the book is the insider description of the Soviet math system, including the nurturing of young talents and the training for competitions like the IMO.

Cognitive thresholds

[See here for updated results.]



This graph (click for larger version) shows the upper division in-major GPAs of U Oregon physics and math graduates from a 5 year period. The math GPAs were computed using a specific set of rigorous courses taken by graduate school bound majors. One might reasonably associate "mastery" of a subject with GPA > 3.5 -- roughly, the minimum threshold to be admitted to graduate school (i.e., students who earn equal numbers of A's and B's in their upper division core courses are borderline for most graduate programs). We find that no student with SAT-M less than roughly 600 was able to attain this level of mastery.

This data provides at least modest evidence for a cognitive threshold required for physics and mathematics. That is, the a priori probability that a student with SAT-M < 600 will perform well enough to be admitted to graduate school in these subjects is extremely low. Note many poorly performing students switch majors, and hence do not populate the lower left corner of our graph.

A simple but plausible model for college performance might include two factors: 1. ability (measured by SAT) and 2. conscientiousness or effort (for simplicity, an uncorrelated random variable, probably normally distributed). The predicted GPA might depend linearly on the sum of the two factors, each measured in SD units. (See bottom figure here for a similar predictor of GPA.) Applying such a model to our data, we would conclude that even a student in the, e.g., 90th percentile of work ethic has a low probability of attaining mastery if their SAT-M score is below 600.

To reiterate, SAT-M 600 seems to be the lowest score at which even a very motivated student has a chance for mastery. From the data one might guess that only for SAT-M well above 700 do students have more than a 50 percent chance of obtaining GPA > 3.5. That is, a student with average motivation or conscientiousness probably needs SAT-M well above 700 to have a high probability of obtaining mastery.

We were unable to find any similar threshold (either in SAT-V or SAT-M) in other majors, including economics, sociology, history, philosophy, biology, chemistry, etc. (More about this data set, including data on other majors, here.)

The percentile equivalents of SAT-M scores for various groups can be found here. For the total SAT-taking population, 600 is about 75th percentile. For the overall population, it might be 85th percentile. SAT-M of 750 is roughly 98th percentile for the total SAT-taking population.

For comparison, here is the analogous plot for English (black) and History (blue) majors versus SAT-R (verbal). If a threshold exists it is probably at SAT-R of 450 or so.

Great Firewall and blogging

I will be behind a certain Great Firewall next week, so blogging might be interrupted :-(

I have taken some precautions, we'll see how it goes.

The perils of precocity

Last night at dinner my 4 year old daughter asked me about death.

"Do all princesses grow old?" she asked.

"But I don't want to die!" she said, knowing, it seems, full well what that meant.

"We might go on to something else after we die," I offered.

"Oh." She thought about that for a while.

My son doesn't seem concerned about any of this, although he does occasionally ask me what the biggest number is. (He also wants to know about the biggest dinosaur and the fastest rocket.) I usually ask him to name the largest number he can think of, and then point out that I can always add one to that to get an even larger number.

When I was a kid I spent a lot of nights pondering life and death and infinity. I still do.

James Cameron's machines

A nice essay on James Cameron and Avatar in the NY Review of Books.

The Wizard: ... The fascination with the seeming invincibility of sophisticated mechanical objects, and an accompanying desire to slough off human flesh for metal (and a celebration of flesh so taut it may as well be metal: Cameron's camera loves to linger on the tightly muscled bodies, male and female, of the soldiers so often featured in his violent films), is a recurrent theme in the techno-blockbusters that cemented the director's reputation in the late 1980s and throughout the 1990s. Aliens famously ends with Weaver's character, Ellen Ripley, battling the dragonish alien monster queen after strapping herself into a giant forklift-like machine whose enormous pincers she mechanically controls by maneuvering her own slender arms—a technology that puts the puny human, finally, on a par with her gigantic, razor-toothed, acid-bleeding adversary.

... A violent variation on the same mechanical bodysuits reappears, memorably, in Avatar, which culminates in a scene of bloody single combat between a Na'vi warrior and the evil Marine colonel, who has strapped himself into one such machine. If anything, the recurrent motif of humans inserting themselves into mechanical contraptions in order to enjoy superhuman powers reaches its fullest, most sophisticated expression in the new movie, whose characters can literally become other, superhuman beings by hooking themselves up to elaborate machines. All this seems to bear out the underlying truth of a joke that Linda Hamilton, the actress who played Sarah Connor in the Terminator movies, told about her first, unhappy interactions with the director (whom she later married and divorced): "That man is definitely on the side of the machines."

Seeds of AI at Google

Semantic meaning from statistical learning and mechanical turk workers like you and me :-)

WIRED: ... Google’s engineers have discovered that some of the most important signals can come from Google itself. PageRank has been celebrated as instituting a measure of populism into search engines: the democracy of millions of people deciding what to link to on the Web. But Singhal notes that the engineers in Building 43 are exploiting another democracy — the hundreds of millions who search on Google. The data people generate when they search — what results they click on, what words they replace in the query when they’re unsatisfied, how their queries match with their physical locations — turns out to be an invaluable resource in discovering new signals and improving the relevance of results. The most direct example of this process is what Google calls personalized search — a feature that uses someone’s search history and location as signals to determine what kind of results they’ll find useful.1 But more generally, Google has used its huge mass of collected data to bolster its algorithm with an amazingly deep knowledge base that helps interpret the complex intent of cryptic queries.

Take, for instance, the way Google’s engine learns which words are synonyms. “We discovered a nifty thing very early on,” Singhal says. “People change words in their queries. So someone would say, ‘pictures of dogs,’ and then they’d say, ‘pictures of puppies.’ So that told us that maybe ‘dogs’ and ‘puppies’ were interchangeable. We also learned that when you boil water, it’s hot water. We were relearning semantics from humans, and that was a great advance.”

But there were obstacles. Google’s synonym system understood that a dog was similar to a puppy and that boiling water was hot. But it also concluded that a hot dog was the same as a boiling puppy. The problem was fixed in late 2002 by a breakthrough based on philosopher Ludwig Wittgenstein’s theories about how words are defined by context. As Google crawled and archived billions of documents and Web pages, it analyzed what words were close to each other. “Hot dog” would be found in searches that also contained “bread” and “mustard” and “baseball games” — not poached pooches. That helped the algorithm understand what “hot dog” — and millions of other terms — meant. “Today, if you type ‘Gandhi bio,’ we know that bio means biography,” Singhal says. “And if you type ‘bio warfare,’ it means biological.” ...


[mike siwek lawyer mi]

The Mike Siwek query illustrates how Google accomplishes this. When Singhal types in a command to expose a layer of code underneath each search result, it’s clear which signals determine the selection of the top links: a bi-gram connection to figure it’s a name; a synonym; a geographic location. “Deconstruct this query from an engineer’s point of view,” Singhal explains. “We say, ‘Aha! We can break this here!’ We figure that lawyer is not a last name and Siwek is not a middle name. And by the way, lawyer is not a town in Michigan. A lawyer is an attorney.”

This is the hard-won realization from inside the Google search engine, culled from the data generated by billions of searches: a rock is a rock. It’s also a stone, and it could be a boulder. Spell it “rokc” and it’s still a rock. But put “little” in front of it and it’s the capital of Arkansas. Which is not an ark. Unless Noah is around. “The holy grail of search is to understand what the user wants,” Singhal says. “Then you are not matching words; you are actually trying to match meaning.”

Gender differences in "extreme" mathematical ability, part 2

We had Andrew Penner of UC Irvine here last week to discuss his paper Gender Differences in Extreme Mathematical Achievement: An International Perspective on Biological and Social Factors. PDF.

I posted back in 2007 on some earlier research of Penner's which showed an 8 percent larger variance in male math ability already at the beginning of kindergarten. (This is not so different from the adult difference in variance.)

For a relatively balanced overview of this topic see Women's underrepresentation in science: Sociocultural and biological considerations; Ceci, Stephen J.; Williams, Wendy M.; Barnett, Susan M. Psychological Bulletin. Vol 135(2), Mar 2009, 218-261. Abstract, PDF.


In the more recent paper Penner claims that national variation in gender gaps in mathematical ability implies that the effect is culturally moderated. While I don't doubt that culture affects development of mathematical ability, and perhaps in such a way as to favor males, I question whether his paper or other recent papers relying on international tests like TIMSS and PISA really have the statistical power to investigate this issue very well. It is already hard to capture national differences in average ability level from tests of only a few thousand students (ensuring that these students are representative of the whole population is difficult); gender gaps are even smaller effects and therefore more sensitive to statistical and systematic error. See here (figure 4) for a convincing demonstration that PISA data on country by country gender gaps is noise dominated: the gaps are not stable between the 2003 and 2006 results. Only by aggregating the data over many countries do we arrive at a stable gap. This makes me suspicious of TIMSS results because PISA has significantly larger statistics. A meta-analysis suggests that cultural effects, while perhaps non-zero, are relatively small.

Andrew and I had an interesting discussion about his paper; my side is summarized in the message and two figures below.

Andrew,

Sorry I had to leave early from your talk and didn't get to discuss this in person. As I mentioned yesterday and in my earlier email, country level gender gaps are not stable between PISA 2003 and 2006, whereas the meta-analysis gap, averaging over all countries, is stable. This to me is clearly a signal that the PISA country level data on gender gaps is dominated by statistical error, and makes me strongly suspect the same is true for TIMSS.

In your talk you said that a biological model would imply the same gender gap in every country, and that country by country variation would undermine the biological model. However, you neglected to mention that statistical error would lead to country by country variation (of measured gaps) even in the biological model.

In the 1995 TIMSS table below there are 8 "gold standard" countries that complied with the statistical procedures. The data from the remaining countries would be suspect, since, as I mentioned, getting a representative sample for a country of millions is not an easy task. In particular, the standard error for countries outside the first group of 8 is likely to be much larger than quoted. (See the column labeled "Difference" in the table. The number in parenthesis is the standard error for the gender gap.)

For the gold standard countries, it appears that all gender gaps are within roughly 1-2 standard deviations (using the standard error given) of the group average, with the exception of Hungary which is an outlier. This suggests that the variation within this group could be entirely statistical. That is, if one formulated a "null model" with constant gender gap across countries, and asked whether TIMSS disfavors that model, the answer might be no, at least not in a statistically significant way. (Actually I suspect that the standard error given is an underestimate, because of systematic errors in the sampling procedures even in the gold standard countries.) Note within this set of countries there is a lot of variation on your societal indicators.

To summarize, I think the claim that TIMSS data supports country level variation in gender gaps has to be considered carefully for statistical significance. As I mentioned, I doubt one can really trust the TIMSS quoted standard errors, so a real test would be time stability of (measured) gender gaps -- a test which PISA fails.

One final comment on your talk: it seems to me that all of the societal variables you listed (labor force participation, wage gap, etc.) have changed significantly in the last 40 years in the US. Nevertheless, I believe gender gaps on the SAT-M (a truly large statistics measurement) have not narrowed during that time. (See second figure below.)

Steve

(Click for larger versions.)

Charlie Munger at Caltech

This video is from a 2008 DuBridge lecture. More Munger.

I'm surprised he didn't get into any trouble for his comments at 1:00 (one hour) into the talk. It's also worth listening to his perspective on derivatives and financial engineering as an end run around regulation and margin requirements at 1:17. He's pretty rough on social scientists and financiers in the early discussion.

Financier flows

We can now track human capital flows as well as capital flows :-)

Despite apocalyptic predictions, it appears (from LinkedIn data) that most financiers who lost their jobs during the credit crisis remained in the industry.

LinkedIn analytics: ... At LinkedIn we have a unique view into the ebbs and flows of labor markets and one trend we noticed was there were beneficiaries of these large-scale upheavals. In particular, we saw substantial spikes in user activity for the following 5 companies during major financial events:

Barclays
Credit Suisse
Citigroup
Bank Of America
JP Morgan Chase

Each of these firms saw an increase in the LinkedIn activity of their employees, measured by member registrations or updates to the individual’s company title on LinkedIn. This activity coincided with key corporate announcements such as the acquisition of Merrill Lynch by Bank Of America, or the Lehman Brothers bankruptcy announcement.

Where did all these employees go? One hypothesis is that many of the employees left the financial industry. According to the LinkedIn data set, that just isn’t true. There are a handful of people that did transition to other industries and start new careers, but most stayed in the financial space.

(Thanks to Tim O'Reilly for pointing this out to me.)

Quants!

This book seems to be mainly about the alpha-seeking variety of quant, as opposed to the risk managing or derivatives pricing kind. A little gee-whizzy for me, but might be good for some insight into the activities of groups like PDT, AQR, etc.

WSJ: ... At Morgan Stanley's investing powerhouse Process Driven Trading on Monday, Aug. 6, founder Peter Muller was AWOL, visiting a friend near Boston. Mike Reed and Amy Wong manned the helm, PDT veterans from the days when the group was nothing more than a thought experiment, its traders a small band of young math whizzes tinkering with computers like brainy teenagers in a cluttered garage.

On Wall Street, they were all known as "quants," traders and financial engineers who used brain-twisting math and superpowered computers to pluck billions in fleeting dollars out of the market. Instead of looking at individual companies and their performance, management and competitors, they use math formulas to make bets on which stocks were going up or down. By the early 2000s, such tech-savvy investors had come to dominate Wall Street, helped by theoretical breakthroughs in the application of mathematics to financial markets, advances that had earned their discoverers several shelves of Nobel Prizes. ...

The education of Nathan Myhrvold

I came across this long 1998 interview with Nathan Myhrvold, which covers his childhood, education as a physicist, brief postdoc with Hawking, software startup and its acquisition by Microsoft, subsequent role there including the creation of Microsoft Research.

NM: Initially I wanted to go on in math. I talked to a professor at UCLA and they said the two best schools for mathematics were Princeton and Berkeley. I have been bad about procrastinating. So I put off applying, and I applied only to those two schools, which, if my kids do that, I’ll kill them. Of course they should apply to many, many schools, and they should keep their options open. I only applied to those two, and I got into both of them. I decided to go Princeton and I decided applied math was more interesting than pure math. So I went into this applied math program that could let you do whole variety of different things.

So the next degree I got was a master’s degree in mathematical economics. Then I finally got a Ph.D. in theoretical physics. A friend of mine said that I was trying to have more degrees than a thermometer. And they’re all on different topics, which in a way was a mistake because I could have been out much sooner if I had concentrated and focused on just one area.

DA: You were still in your young, early twenties.

NM: I was 23 so it wasn’t like I wasted that much time. And I’m glad I did because it was great to see all of these other fields and learn something about them.

DA: Were you considering spending your career as an academic?

NM: Oh absolutely, that was the only thing. I’m sure if you had interviewed me when I was in graduate school at Princeton, I would have been very full of myself about that. And I would explain in an enormous, articulate way about what else would one do? You know, what greater thing could one aspire to? But of course I’m not there.

...

NM: ... People have a lot of metaphors for entrepreneurship. I like two of those metaphors. One is white water rafting, and I say white water rafting because you have a skill in rafting that counts for something. I know a number of people that are great rafters. But you’re also going on this wild river, and the current is going, and you’re going to get splashed, and wet and thrown, and even the best rafters have been thrown out of the raft and capsized and everything else. It’s partially under your control, and it’s partially not under your control. And a lot of people don’t realize that.

I talked to a lot of people when I first started this company. There was a venture capitalist who had been an entrepreneur, and he was full of sage advice. I remember I was in his office, which was in the Bank America Tower in San Francisco. It was on the 50th floor with this stunning view. And he says, “You know, having a company is like having a baby.”

I said, “Okay.” He says, “No, no, no, it’s not like what you’re thinking. You’re thinking it’s like the man’s role in having a baby, a half hour of fun, and nine months later you pass out cigars and you’re a proud father.” He said, “No, it’s like the woman’s role in having a baby. It’s nine months of incredible discomfort and pain and all this; and then the hard work starts.” And I have to say he was right. You know at the time I listened to him. I heard him out. I didn’t realize how true that was.

...

DA: Did the whole company go to Microsoft?

NM: Yes, well most of the company came. There were about 15 people who were employees, and a whole bunch of them were part time. The eight full time people all came, and we weren’t sure how long it was going to last. We weren’t sure whether we would like Seattle. We weren’t sure whether Microsoft really would like us once they saw us up close. It turns out this group had an illustrious career at Microsoft. I’m still here. My brother Cameron is a Vice President here at Microsoft. He actually didn’t join when we first came up. He was the only full time guy not to join. He went back to Berkeley and finished up that last quarter, and joined the following year. The guy who’s the technical lead on Windows 3.1, Windows 95, and is now technical lead for natural language in the company, came from my company. The guy who is the technical lead for graphics and Windows NT came from my company. The guy who’s the technical lead for multi-media for a long time at Microsoft, came from my company. These eight guys that came up, all had stellar careers. We sort of spread out throughout Microsoft and wound up in very senior
positions. ...

See here for a NYTimes update on his company Intellectual Ventures, also discussed in this earlier blog post: Gladwell amongst the patent trolls.

Ethnic affinity

The essence of ethnic affinity:

“We have never met before, but I instantly know him. One look, one phrase, and I know where he grew up, how he grew up, where he got his drive and his sense of humor. He is New York. He is Jewish. He looks like my uncle Louis, his voice is my uncle Sam. I feel we’ve been together at countless weddings, bar mitzvahs, and funerals. I know his genetic structure. I’m certain that within the last five hundred years—perhaps even more recently—we shared the same ancestor.”

--- Robert Reich, Clinton administration Secretary of Labor, on his first face-to-face meeting with Fed Chairman Alan Greenspan

I chose this quote, not to focus on Jews in particular, but because Reich writes so well and truly. It would be the same for me and another Asian-American, or two white guys meeting in Cairo, or for me and another American (of any color) meeting in some foreign country. Reich is obviously quite self-aware, but the same effect is there even if entirely subconscious.

The value of hard work

Our scheduled departmental colloquium was cancelled this week, so my colleague Jim Schombert and I volunteered to talk about some work we're doing on the predictive power of the SAT. The talk is Thursday, Feb. 11, 2010 at 4 PM.

When I get my collaborator's approval I will post a link to all sorts of fun graphs. Here are some introductory slides I prepared to explain a bit about psychometrics to physicists. [Graphs are now available!]

Overall, the message is hopeful: SAT score only accounts for a fraction of total variation in college success. Other factors, such as hard work or conscientiousness, probably play at least as large a role. Nevertheless, the SAT has clear (statistical) predictive power -- about as much as high school GPA. One caveat to the abstract below is that "overachievers" (as defined) tend to be concentrated in certain majors (in rough order of prevalence: sociology, political science, humanities, biology, chemistry); they are harder to find in subjects like pure math, rigorous computer science and physics, which seem to have actual cognitive thresholds.

Jim Schombert and Steve Hsu will discuss some statistical research on SAT scores and UO grades based on a large corpus of student data. After a brief discussion of psychometrics (cognitive testing, the meaning of the SAT and GRE), the authors will discuss the points outlined below.


Title: The Value of Hard Work: College GPA Predictions From SAT Scores

Abstract:

We analyze a data set composed of the academic records of all undergraduates entering the University of Oregon from 2000-2004. We find correlations of roughly .3 to .5 between SAT scores and upper division, in-major GPA (henceforth, GPA). Interestingly, low SAT scores do not preclude high performance in most majors. That is, the distribution of SAT scores after conditioning on high GPA (e.g., > 3.5 or even 4.0) typically extends below 1000 (the average among test takers). We hypothesize that overachievers overcome cognitive deficits through hard work, and discuss to what extent they can be identified from high school records. Only a few majors seem to exhibit a cognitive threshold -- i.e., such that high GPA (mastery of the subject matter) is very unlikely below a certain SAT threshold (i.e., no matter how dedicated or hard working the student). Our results suggest that almost any student admitted to university can achieve academic success, if they work hard enough.

We find that the best predictor of GPA is a roughly equally weighted sum of SAT and high school GPA, measured in standard deviation units. We also analyze the performance of UO honors college students, a selected population which resembles that of elite private colleges. Finally, we observe that 1. SAT scores fluctuate little on retest (very high reliability), 2. SAT and GRE scores (where available) correlate at roughly .75, consistent with the notion that both tests measure a relatively stable general cognitive ability, and 3. the SAT distribution of students that obtained a degree does not differ substantially from that of the entering class.

Below is a graph showing the reliability of SAT scores. It gives the frequency of score differences (max minus avg or max minus min) for students who took the test more than once. The result for verbal (reading) scores is about the same. Improvements of more than 1 SD (100 points) are quite rare. It seems likely that among the thousands of students in this data set, at least a few used SAT prep courses, but apparently with limited success.




A partial list of the graphs available here. Note UO GPA is always in-major, upper division GPA. In the case of math and CIS (computer science) the grades are from a subset of especially rigorous courses in each department.

SAT combined vs UO GPA by major
SAT-M,V vs UO GPA by major
HSGPA vs UO GPA by major
SAT combined vs HS GPA

Other plots:
SAT profiles of graduates and non-graduates
retest reliability of SAT M, V
SAT-M,V and GRE correlation
Clark Honors College GPA and SAT vs overall UO population
UO GPA vs best (equal weight) SAT + HSGPA predictor

Clark Honors College (CHC) students are roughly equivalent to Berkeley or Cornell students, based on SAT and HSGPA. They outperform typical UO students, but you can see that even CHC GPAs (again, in-major, upper div) cover a wide range. So, it seems likely that UO students with high in-major GPAs have subject mastery similar to the better students at elite universities. The CHC students are the red dots in the graph below.




Here is UO GPA vs best predictor: equally weighted sum of SAT and high school GPA, measured in standard deviation units.

From physics to neuroscience

The Times has a nice interview with one of my former Caltech classmates, Princeton neuroscientist Sam Wang.

I can remember that even back in the 1980s there was a debate among the most ambitious students whether one should pursue math, theoretical physics or biology (typically molecular biology). This was true not only at Caltech, but at the various science camps I attended as a kid. A lot of "in the know" kids were already turning toward molecular biology as ripe for revolutionary progress (they were right!). I recall a few super ambitious characters at Caltech who tried to keep up with two or more subjects as long as they could. One guy we called "library back" carried a huge backpack around filled with books, and tried to keep up with both molecular biology and physics, finally succumbing to advanced quantum mechanics (I don't think he was able to get beyond the elementary course in the subject). My interest in pure math waned after an encounter with analyst Tom Wolff (math 108, freshman year -- he was a dedicated instructor, but I got a little too much exposure to a world-class analyst!), my interest in academic computer science dissipated after taking a course on AI (a subject I had a long interest in, but it was clear to me how distant the goal) and it was obvious to me that the Rube Goldberg mechanisms of biology were too complex and arbitrary for my tastes (I even find the Standard Model to be an ugly kluge!).

I seem to remember Sam as primarily a physics major, and I think he went on to graduate school at Stanford to study physics (or maybe biophysics), but he may have been a double major all along. It's great to see that he's making an impact in neuroscience! (The article doesn't mention his interest in election forecasting -- I guess NYTimes readers' heads would explode :-)

Q. ... WHY DID YOU BECOME A NEUROSCIENTIST?

A. I was at Caltech in 1985, and I took a class in classical mechanics and another in introductory cell biology. And I remember asking this physics instructor about second order corrections in Lagrangian dynamics. He said, “Oh yes, that’s been thought of,” while spewing out a bunch of equations on the blackboard. I then asked my biology instructor a question about neurotransmission. He kind of smirked at me and said, “Nobody knows the answer to that.”

That felt great! It was great to ask a basic question and learn the answer wasn’t known. So neuroscience seemed like the way to go.

...

Q. GOING BACK TO YOUR YOUTHFUL DECISION TO ABANDON PHYSICS AND TAKE UP NEUROSCIENCE: ANY REGRETS?

A. Never. My parents, who were immigrants, didn’t understand it at the time. My father’s proud of me now. But my mother really wanted me to be an M.D. Even after I got a Ph.D., she still wanted that. She once sent me a brochure about a medical school in the Caribbean where I could become an M.D. in a year. My mother died a few years ago. I cannot remember ever being able to adequately explain to her what I do. That has a little to do with why I wrote, “Welcome to Your Brain.” I wanted to show how neuroscience speaks to everyday life.

Learn Chinese!

The Times Opinion section has a debate over whether Americans will learn Chinese. To get an idea of how hard it is, watch this short video and ask yourself how long it will take you to instinctively differentiate the four tones. Of course, that's just the spoken language -- after that, there are those lovely ideograms :-)

Here's what Berkeley professor Bruce Fuller has to say:

... We are pathetically slow in realizing that East Asia will soon dominate the global economy. We believe, as did the last living Romans, that the American empire will reign forever. So, we fail to grasp the hard work, collective spirit and enormous investment in public institutions advanced by Chinese citizens.

We must learn the language and engage them at a human scale as first steps in appreciating the strengths of East Asian cultures. These virtues already lift America’s best universities. Over half of Berkeley’s undergraduates are now of East Asian descent.

Rather than bumbling along, government and corporate leaders should advance coherent policies for bilingualism. Europe began this process about four centuries ago. Washington moves quickly when military interests dominate. My Arabic-speaking son, Dylan, was offered $20,000 up front to staff intelligence outposts in the Middle East. But Mandarin? What’s the rush? The count of American high school students enrolled in Chinese classes is less than those studying German.

If you think the Arab world today poses a civilizational threat to the West, you are sadly deluded. The US has had its attention focused on the wrong competitors since 9/11.

Asian-American admissions in the Boston Globe

This Op-Ed appeared in the Boston Globe today. The author, a Yale grad and Babson College professor, wrote an earlier piece entitled My Lazy American Students, so she is no stranger to controversy :-)

Thanks to interlibrary loan I now have a copy of Thomas Espenshade's new book on elite admissions, but so far haven't found much that wasn't already leaked to the press. Related posts on Asian-American college admissions here.

Do colleges redline Asian-Americans?

By Kara Miller | February 8, 2010

SAT SCORES aren’t everything. But they can tell some fascinating stories.

Take 1,623, for instance. That’s the average score of Asian-Americans, a group that Daniel Golden - editor at large of Bloomberg News and author of “The Price of Admission’’ - has labeled “The New Jews.’’ After all, much like Jews a century ago, Asian-Americans tend to earn good grades and high scores. And now they too face serious discrimination in the college admissions process.

Notably, 1,623 - out of a possible 2,400 - not only separates Asians from other minorities (Hispanics and blacks average 1,364 and 1,276 on the SAT, respectively). The score also puts them ahead of Caucasians, who average 1,581. And the consequences of this are stark.

Princeton sociologist Thomas Espenshade, who reviewed data from 10 elite colleges, writes in “No Longer Separate, Not Yet Equal’’ that Asian applicants typically need an extra 140 points to compete with white students. In fact, according to Princeton lecturer Russell Nieli, there may be an “Asian ceiling’’ at Princeton, a number above which the admissions office refuses to venture.

Emily Aronson, a Princeton spokeswoman, insists “the university does not admit students in categories. In the admission process, no particular factor is assigned a fixed weight and there is no formula for weighing the various aspects of the application.’’

A few years ago, however, when I worked as a reader for Yale’s Office of Undergraduate Admissions, it became immediately clear to me that Asians - who constitute 5 percent of the US population - faced an uphill slog. They tended to get excellent scores, take advantage of AP offerings, and shine in extracurricular activities. Frequently, they also had hard-knock stories: families that had immigrated to America under difficult circumstances, parents working as kitchen assistants and store clerks, and households in which no English was spoken.

But would Yale be willing to make 50 percent of its freshman class Asian? Probably not.

Indeed, as Princeton’s Nieli suggests, most elite universities appear determined to keep their Asian-American totals in a narrow range. Yale’s class of 2013 is 15.5 percent Asian-American, compared with 16.1 percent at Dartmouth, 19.1 percent at Harvard, and 17.6 percent at Princeton.

“There are a lot of poor Asians, immigrant kids,’’ says University of Oregon physics professor Stephen Hsu, who has written about the admissions process. “But generally that story doesn’t do as much as it would for a non-Asian student. Statistically, it’s true that Asians generally have to get higher scores than others to get in.’’

In a country built on individual liberty and promise, that feels deeply unfair. If a teenager spends much time studying, excels at an instrument or sport, and garners wonderful teacher recommendations, should he be punished for being part of a high-achieving group? Are his accomplishments diminished by the fact that people he has never met - but who look somewhat like him - also work hard?

“When you look at the private Ivy Leagues, some of them are looking at Asian-American applicants with a different eye than they are white applicants,’’ says Oiyan Poon, the 2007 president of the University of California Students Association. “I do strongly believe in diversity, but I don’t agree with increasing white numbers over historically oppressed populations like Asian-Americans, a group that has been denied civil rights and property rights.’’ But Poon, now a research associate at the University of Massachusetts Boston, warns that there are downsides to having huge numbers of Asian-Americans on a campus.

In California, where passage of a 1996 referendum banned government institutions from discriminating on the basis of race, Asians make up about 40 percent of public university students, though they account for only 13 percent of residents. “Some Asian-American students feel that they lost something by going to school at a place where almost half of their classmates look like themselves - a campus like UCLA. The students said they didn’t feel as well prepared in intercultural skills for the real world.’’

But what do you do if you’re an elite college facing tremendous numbers of qualified Asian applicants? At the 2006 meeting of the National Association for College Admission Counseling, a panel entitled “Too Asian?’’ looked at the growing tendency of teachers, college counselors, and admissions officers to see Asians as a unit, rather than as individuals.

Hsu argues it’s time to tackle this issue, rather than defer it, as Asians’ superior performance will likely persist. “This doesn’t seem to be changing. You can see the same thing with Jews. They’ve outperformed other ethnic groups for the past 100 years.’’

Which leaves us with two vexing questions: Are we willing to trade personal empowerment for a more palatable group dynamic? And when - if ever - should we give credit where credit is due?

The new dating game

In case you are unfamiliar with terms like (no, this has nothing to do with portfolio theory): alpha, beta, neg, PUA, AFC, and chick crack, read the excerpt below. The photo above is just one of many from the site Hot chicks with douchebags. More details in this Wikipedia entry.

I spent my late teen years at an approximately all-male university near Los Angeles, so I endured way too much time at bars talking to women like the ones described in the article below (in case you are wondering, I had a very good fake ID, but that's another story). I remember a weeknight (happy hour!) at a club in Glendale, with a French guy (grad student, I knew him from the gym) who is now a professor of bioinformatics. I was just a kid -- all the women there were much older than I was. Pierre, I'll call him, had just finished dancing with a modestly attractive blonde and sat down at the bar with me. Are you really interested in her? I asked*. He winked at me and mouthed a single word: Practice :-)

The evo-psych explanations given below date back at least to Caltech guys (anthropologists of the LA singles scene) of the 1980s, and probably much earlier.

* Modern lingo: Would you really hit that?

Weekly Standard: ... In the late 1990s, Mystery developed a precise and exacting “algorithm” of moves and routines—pre-scripted lines to be practiced in the field—that are virtually guaranteed (according to Mystery at least) to lure a female into your bed after just seven hours in her company from a cold turkey meeting in a public place. ... The fundamental strategy is to “demonstrate higher value” (DHV, another Mystery acronym), to appear so fascinating that the woman will want to prove her worthiness to you, not the other way around. You don’t buy her a drink; you offer to let her buy you one. You don’t give her your phone number; you get her to give you hers, in what Mystery calls a “number closing.” If she asks you what you do for a living, you don’t mention the drone desk job that you actually hold down; you tell her you “repair disposable razors” (the choice of a Mystery disciple). You “peacock” (yet another Mystery coinage), which means donning outlandish, attention-grabbing attire. Mystery’s signature peacocking wardrobe includes a black fur bucket hat and matching black nail polish and eyeliner. On The Pickup Artist, he sported a seemingly inexhaustible supply of exotic headgear and man-baubles.

...

If it all sounds cheesy, tedious, manipulative, obvious, condescending to women, maybe kind of gay, it’s because it is. But here’s the rub: This stuff works. If you think men who peacock look ridiculous and unmanly, click onto the photo-website Hot Chicks With Douchebags, where spectacular-looking babes hang on the pecs of preening rednecks and “Jersey Shore”-style guidos sporting chest-baring shirts and product-stiffened fauxhawks. Watch the video “Learn Enough Guitar to Get Laid” on YouTube (three chords, max). In June 2005, Craig Malisow, a reporter for the Houston Press, trailed 24-year-old Bashev, a Bulgarian-born graduate student in engineering at Rice University and self-styled pickup expert, to a series of bars and clubs in Houston. Bashev had no intention of telling the 20-something HBs he met that his day job consisted of working with multivariable calculus. Instead he pointed to his shoes and informed them that he was a “foot model.” Then he launched into his canned opener: Did they think reality shows were “really real”? Sure, two groups of females on whom Bashev tried that line rolled their eyes and smirked, but three bars (and the same routine) later, he was relaxing in a lounge chair reading a shapely brunette’s palm (chick crack plus “kino,” a Mystery-ism that refers to getting a woman to crave your touch), and soon enough “her fingers were gently grasping the backs of his wrists,” Malisow observed. Within minutes, Bashev had not only number-closed but gotten a date for the following Wednesday.

Pickup mentors are relying, consciously or sub, on the principles of evolutionary psychology, which uses Darwinian theory to account for human traits and practices. Robert Wright introduced the reading public to evolutionary psychology in his 1994 book, The Moral Animal: Why We Are the Way We Are. He summarized what biologists had observed in the field: that among animals—and especially among our closest relatives, the great apes—males often fight each other for females and so the most dominant, or “alpha,” male has access to the most desirable, and perhaps all, of the females. But it’s the female of the species who ultimately makes the choice as to which member of the pack she will deem the alpha male. “Females are choosy in all the great ape species,” Wright wrote. He also noted that, for example, a female gorilla will be faithful—forced into fidelity, actually—to a single dominant male, but she will willingly desert him for a rival male who impresses her with his superior dominance by fighting with her mate. That’s because, as Darwin postulated, evolution isn’t merely a matter of survival of the fittest but also of the replication of the fittest, “selfish genes,” in the words of neo-Darwinian Richard Dawkins. Driven by instinctual desire for offspring, male primates chase fertile females so they can replicate themselves, while female primates choose strong males on the basis of survival traits to be passed on to young ones.

Evolutionary psychologists like David Buss in The Evolution of Desire (1994) and Geoffrey Miller in The Mating Mind (2000) have elaborated on these theories, arguing that the human brain itself, with its capacity for consciousness, reasoning, and artistic creation, evolved as an entertainment device for male hominids competing to impress the females in the pack. Dennis Dutton’s new book, The Art Instinct, makes much the same argument. Evolutionary psychologists postulate that the same physical and psychological drives prevail among modern humans: Men, eager for replication, are naturally polygamous, while women are naturally monogamous—but only until a man they perceive as of higher status than their current mate comes along. Hypergamy—marrying up, or, in the absence of any constrained linkage between sex and marriage, mating up—is a more accurate description of women’s natural inclinations. Long-term monogamy—one spouse for one person at one time—may be the most desirable condition for ensuring personal happiness, accumulating property, and raising children, but it is an artifact of civilization, Western civilization in particular. In the view of many evolutionary psychologists, long-term monogamy is natural for neither men nor women.

...

Evolutionary psychology also provides support for a truth universally denied: Women crave dominant men. And it seems that where men are forbidden to dominate in a socially beneficial way—as husbands and fathers, for example—women will seek out assertive, self-confident men whose displays of power aren’t so socially beneficial. This game of sexual Whack-a-Mole is played regularly these days in a culture that, starting with children’s schoolbooks and moving up through films and television, targets as oppressors and mocks as bumblers the entire male sex.

...

Living in the New Paleolithic can be hard on women, many of whom party on merrily until they reach age 30 and then panic. “They’re at the peak of their beauty in their early 20s—they’re luscious—but the guys their age don’t look as good, so they say to themselves: ‘Why do I want to get married?,’ ” notes Kay Hymowitz, a contributing editor to the Manhattan Institute’s City Journal, who is writing a book about the singles crisis. “Then they get to age 28, 29, and their fertility goes down and they’re not quite so luscious. But the guys their age are starting to make money, they look better, they’ve got self-assurance, and they’ve also got the pick of the 23-year-olds.”

Some argue, though, that it is actually beta men who are the greatest victims of the current mating chaos: the ones who work hard, act nice, and find themselves searching in vain for potential wives and girlfriends among the hordes of young women besotted by alphas. That is the underlying message of what is undoubtedly the most deftly written and also the darkest of the seduction-community websites, the blog Roissy in DC. Unlike his confreres, Roissy does not sell books or boot camps, and his site carries no ads. He also blogs anonymously, or at least tries to. (Purported photos of Roissy circulating on the Internet show a tall unshaven man in his late 30s with piercing blue eyes and good, if somewhat dissolute, looks.) The pseudonym Roissy derives from the chateau that was the setting for sadomasochistic orgies in The Story of O, the French pornographic classic of the 1960s which featured a beautiful young woman who couldn’t get enough of being violated and flagellated by masterful men. Roissy maintains that he is not an S&M-fetishist but picked the pseudonym because “chicks dig power.”

...

“The sexual revolution in America was an attempt by women to realize their own [hypergamous] utopia, not that of men,” Devlin wrote. Beta men become superfluous until the newly liberated women start double-clutching after years in the serial harems of alphas who won’t “commit,” lower their standards, and “settle.” During this process, monogamy as a stable and civilization-maintaining social institution is shattered. “Monogamy is a form of sexual optimization,” Devlin told me. “It allows as many people who want to get married to do so. Under monogamy, 90 percent of men find a mate at least once in their life.” This isn’t necessarily so anymore in today’s chaotic combination of polygamy for lucky alphas, hypergamy in varying degrees for females depending on their sex appeal, and, at least in theory, large numbers of betas left without mates at all—just as it is in baboon packs. The aim of Mystery-style game is to give those betas better odds. ...

Related: NYTimes on dating and gender imbalances on campus.

Hank on Charlie

"We prevented Armageddon."

"I did not see the housing bubble."

"We need a systemic risk regulator, with resolution authority to liquidate firms in an orderly way."

"Buy my book."

-- Henry Paulson interview with Charlie Rose.

Office space

These are not my offices, but I wish they were :-) Click for larger versions.

Money talks

In an earlier post 100 cents on the dollar I discussed the controversial AIG bailout, summarizing a WSJ narrative as follows:

IF AIG (credit rating) Downgrade,
THEN collateral provisions trigger in derivatives (CDS) contracts,
THEN AIG bankruptcy,
THEN systemic meltdown.

So the AIG counterparties played chicken with the Fed and got 100 cents on the dollar for $60 billion in derivatives contracts. Paid for by you and me and our kids :-/

If I have any of this wrong, experts please correct me -- I'm just trying to understand how the world works.

A reader with considerable expertise weighs in with the following analysis of the events of late 2008. While this person has more expertise on this matter than anyone I know, he may not be completely unbiased, considering his employer ;-)

Steve - I do think you have it wrong. The plot line is false, but it is also imbued with plenty of facts - so explanation is difficult.

Quick summary: An AIG bankruptcy filing did not hinge on the resolution of these contracts, thus there could not have been a game of chicken.

Details (greatly simplied but still quite wordy): This story is set in November of 2008, but to understand it we have to go back two months earlier - to September. In September 2008 the clearing price of mortgage-backed securities implied a high rate of mortgage default with low recovery on foreclosed property - a decline in real estate values such as never seen before. Observers were still debating whether prices were correctly forecasting widespread defaults or whether prices were merely depressed due to illiquidity. For institutions levered to real estate the distinction was very important because it meant the difference between short-term liquidity problems and long run insolvency which would force bankruptcy.

To understand liquidity problems, we need to understand collateral. In order to minimize credit risk to each other, financial institutions ask for collateral from each other. As trades slowly move up or down in value, cash and securities flow between institutions so that everyone owes each other approximately nothing and the effects of a sudden bankruptcy are much smaller than they otherwise would be. Having collateral in hand means not having to worry about why your counterparty's trades are marking down so low - you are 100% insured by the collateral and if prices rebound you'll just give it back. And if your counterparty goes bankrupt you won't take a loss. Lehman was long real estate and, due to falling prices and having to post collateral, come September they ran out of cash. Regulators shopped them to other banks whose traders pored over Lehman's books and concluded "the company is insolvent, not just illiquid" so "goodnight Lehman Bros".

AIG was in the same boat as Lehman however AIG is an insurance company - this opened up three problems. (1) Banks are usually regulated by federal entities like the Fed and the SEC, so they all fit into similar frameworks nationwide. However insurance companies are only regulated by states. States are not particularly great at regulating because they are small and every state does things differently. (As a side note for fans of health care reform, regulating insurance companies at the federal level and creating national competition is the single most important reform needed. Lack of this in the so-called reform bills is proof that the legislation is all about creating health care entitlement without any real reform.) You can't just call in JP Morgan or Goldman Sachs to tell you what to do here because a bank can't really analyze an insurance company over a weekend. (2) Next, the ratings agencies had assigned AIG their highest rating, AAA, meaning that people who didn't want to do a lot of their own credit analysis, but didn't want to take any risk of default, were the sort of people who bought AIG bonds - that is to say, AIG bonds were largely being held in accounts that were presumed to be taking no risk by their owners. If you go back to news stories from September 2008 you will find many shrill voices invoking the spectre of systemic melt-down, but those voices tend to come from holders of AIG bonds, not random investors worried about "the system". (3) As an insurance company the general public was very broadly exposed to AIG in pensions, annuities, home insurance, etc... and these people aren't holding any collateral at all, but would account for a lot of votes at the polls come November. So, because of these three complications, the same politicians who let Lehman go under decided, at about the same time, to bail out AIG.

Now, there are lots of ways to do a bailout. This very same month, the largest financial entities in the world, Fannie and Freddie, were bailed out - and basically by the federal government saying "we simply guarantee all the debts and obligations of Fannie and Freddie". (Note that, in the end, the agency bailout is where the "taxpayer" will take almost all his lumps. But the politicians don't talk about this too much because Washington spent decades exempting Fannie and Freddie from any meaningful regulation and encouraging them, via various acts of congress, to facilitate mortgage loans to people who probably couldn't afford homes, but would make U.S. home ownership "more diverse".) In the case of AIG the bailout took the form of an $85 billion loan. It is important to remember at this point the debate over whether real estate securities were down because a bunch of mortage defaults were coming or just because nobody wanted to own the bonds. The politicians were still hoping for the latter and hoping that a loan would tide AIG through its illiquid period and into a time when the securities recovered. The TARP legislation was debated this same month, and the original intent of TARP was to buy these securities, thus creating a market for them, and a recovery in their prices. (In the end, however, TARP was not used in this way. It was used to invest in banks, for which no legislation was required anyway, and auto companies - which was probably illegal.) The TARP debate informs us that in September the politicians were not believing the market-implied rate of mortgage default. So, AIG used its bailout money to keep collateral flowing on its real estate positions. The thinking on this was that it stopped the counterparties from closing out the trades and thus "locking in the loss".

Now let's move forward to November 2008, the subject of this post. By November those debating that real estate securities were down due to illiquidity rather than coming defaults had given up their argument. A wave of defaults was coming and everyone finally realized that a lot of people who took out sub-prime mortgages always had the intention of defaulting because they didn't want to own a home so much as speculate on real estate going up. Back at AIG, their positions had continued to go against them and it became pretty obvious that AIG was insolvent, not just illiquid, and a more aggressive bailout was going to be needed if they were not to declare bankruptcy. But now if AIG goes bankrupt the government is in for a loss too since it has made all these loans. So "in for a penny in for a pound" the feds decide to up the bailout. Among many issues to be sorted out in the fresh bailout is that AIG still has all these real estate positions which keep bleeding money and, since AIG made the wrong calls on this all along, the feds decide to close out all of these positions.

At this point, the "unlimited bailout" decision has been made, and the discussion over mopping up these positions is a sidebar, not super important at the time (but it will become very politically sensitive a year later). The feds would like to save a few bucks by negotiating the close-out on these trades with the banks. However, because all the banks are holding lots of collateral, and some of them are even insured against AIG defaulting, they expect to be paid in full just like everyone else exposed to AIG. The feds aren't calling PIMCO and asking them to take 80 cents for their AIG bonds, they are calling retirees and asking them to take 80 cents for their pension annuities, and the aren't calling up auto insurance holders and asking them to take 80 cents for fender-bender repairs. Further, the insured banks wouldn't take a loss even if AIG went bankrupt unless you buy the end-of-the-world systemic melt-down scenario. But, even if you believe that, why aren't all of AIG's creditors being asked to contribute? Indeed, why not everyone in the world, since all benefit from there being no global melt-down. The banks are not the primary beneficiaries of the bailout, the bondholders are - bondholders have no collateral. In essence, there is nothing for the fed to negotiate, so they abandon the effort.

So, back to Steve's question. AIG was going to go bankrupt without a bailout, and negotiating 80 cents on the dollar for its real estate positions would not have changed that either way - so no game of chicken. For political reasons, the decision was made to bail them out. The banks were not asked to pay a disproportionate amount towards that bailout, and intelligent minds can debate whether or not they should have, but the case is far from clear. Lastly, I will add that I am unconvinced that letting AIG go bankrupt would have created a systemic meltdown, but it is clear why those involved in the bailout would say that. It is human nature that when you make a convenient but unpopular choice, your first defense is likely to be that you didn't really have a choice. The choice to bailout AIG was convenient and unpopular, but I don't agree that it would have led to systemic meltdown, and the historical arguments on my side are quite strong. Panics happen every 10 to 20 years and it is never the end of the world.