Category: Science

Some reflections on GRE scores

The evidence indicates that GRE scores predict graduate school success, general intelligence, and also that SAT scores predict later success in science.  Here is further evidence, and here is yet further evidence.

You don’t have to think that “high GRE score fields” are better than “low GRE score fields.”  Many of my friends, for instance, think string theory is intellectually bankrupt, despite many of its proponents being very, very smart.  I don’t have an opinion on string theory per se, but my friends might be right, and in any case I would rather read books from cultural studies, a lower GRE score field.

If you wish to understand the relative strengths and pathologies of theoretical physics and cultural studies, you cannot do that without knowing that the former is a relatively high GRE score field (or the equivalent) and that the latter is a relatively low GRE score field (or the equivalent).

There are many top economists on Twitter, most of them Democrats, who would never ever utter a word about GRE scores in a blog post or on Twitter.  Yet when on an admissions committee, they will ruthlessly enforce the strictest standards for math GRE scores without hesitation.  Not only in top ten programs, but in top thirty programs and even further down the line in many cases.  It is very, very hard to get into a top or even second-tier economics program without an absolutely stellar math GRE score, and yes that is enforced by the same humans who won’t talk about the issue.

Just in case you didn’t know that.

Personally, I feel it has gone too far in that direction, and economics has overinvested in one very particular kind of intelligence (I would myself put greater stress on the old GRE subject test scores for economics, thus selecting for those with an initial interest in the economy rather than in mathematics).

When I did graduate admissions for George Mason University, I very consciously moved away from an emphasis on GRE scores, and for the better.  My first goal was simply to take in more students, and a more diverse group of students, and in fact many of the later top performers were originally “marginal” students by GRE standards.  Looking back, many of our top GRE-scoring students have not done better than the peers, though they have done fine.  For GMU these admission criteria are (in my view) more like the Rosen-Roback model than anything else, though I would readily grant Harvard and MIT are not in the same position.

If you are afraid to talk about GRE scores, you are afraid to talk about reality.

Alexander Wendt on why we should take UFOs seriously

He has more than just the usual hand-wringing, here is one excerpt:

Sean Illing

…What’s the Occam’s razor explanation for these UFO sightings?

Alexander Wendt

To me, the Occam’s razor explanation is ETs.

Here is another:

Sean Illing

If some of these UFOs are the products of alien life, why haven’t they made their presence more explicit? If they wanted to remain undetected, they could, and yet they continually expose themselves in these semi-clandestine ways. Why?

Alexander Wendt

That’s a very good question. Because you’re right, I think if they wanted to be completely secretive, they could. If they wanted to come out in the open, they could do that, too. My guess is that they have had a lot of experience with this in the past with civilizations at our stage. And they probably know that if they land on the White House lawn, there’ll be chaos and social breakdown. People will start shooting at them.

So I think what they’re doing is trying to get us used to the idea that they’re here with the hopes that we’ll figure it out ourselves, that we’ll go beyond the taboo and do the science. And then maybe we can absorb the knowledge that we’re not alone and our society won’t implode when we finally do have contact. That’s my theory, but who knows, right?

Here is the full piece, interesting and intelligent throughout.

Testing participation vs. testing capacity

This paper argues that testing participation –and not testing capacity–is the biggest obstacle to a successful “test and isolate”-strategy, as recently proposed by Paul Romer. If 𝑅0=2.5,at least 60percentof a population needs to participate in a testing program to make it theoretically possible to achieve an effective reproduction rate for the whole population,𝑅′′, below 1. I also argue that Paul Romer’s assumption about quarantine length is problematic,because it implicitly assumes that an infected and tested person is quarantined during the entire duration of the illness. With more realistic assumptions, where the fraction of the illness duration that is spent in quarantine depends on the test frequency, at least 80percentof the population must participate to keep 𝑅0′′<1, even if participants in the test program are tested every five days.Comprehensive testing,as proposed by Romer,is probably still a very cost-effective means of reducing the reproduction rate of the infection compared to mandatory lockdown policies, but it seems less promising than he suggests.How-ever, comprehensive testing might also reduce voluntary social distancing in a non-cost-effective way because testing and isolating infected individuals decreases the risk of infection for an individual if social distancing is not practiced.

Here is the full paper by Jonas Herby.

*Good Work if You Can Get It: How to Succeed in Academia*

That is the new Jason Brennan book, just out yesterday, here is a summary:

This candid, pull-no-punches book answers questions big and small, including

• Should I go to graduate school—and what will I do once I get there?
• How much does a PhD cost—and should I pay for one?
• What kinds of jobs are there after grad school, and who gets them?
• What happens to the people who never get full-time professorships?
• What does it take to be productive, to publish continually at a high level?
• What does it take to teach many classes at once?
• What does it take to succeed in graduate school?
• How does “publish or perish” work?
• How much do professors get paid?
• What do search committees look for, and what turns them off?
• How do I know which journals and book publishers matter?
• How do I balance work and life?

This realistic, data-driven look at university teaching and research will make your graduate and postgraduate experience a success.

Here is my blurb:

“In Good Work If You Can Get It, Jason Brennan tells it like it is. You will get the truth, the whole truth, and nothing but the truth. This is the one book to read about trying to become a professor.”

Self-recommended.  And here is Bryan Caplan’s excellent review.

Who wants to take UFO sightings more seriously?

That is the topic of my latest Bloomberg column, here is one excerpt:

Among my friends and acquaintances, the best predictor of how seriously they take the matter is whether they read science fiction in their youth. As you might expect, the science-fiction readers are willing to entertain the more outlandish possibilities. Even if these are not “little green men,” the idea that the Chinese or Russians have a craft that can track and outmaneuver the U.S. military is newsworthy in and of itself. So would be a secret U.S. craft, especially one unknown to military pilots.

The cynical view is that the science-fiction readers are a bit crazy and are trying to recapture the excitement of their youth by speculating about UFOs. Under this theory, they shouldn’t be taken any more seriously than Tolkien fans who wonder if orcs are hiding under the next stone.

The more positive view is that science-fiction readers are more willing to consider new ideas and practices. This kind of openness presumably is a good thing, at least in general, so why aren’t the opinions of more “open” observers accorded more respect? Science-fiction readers have long experience thinking about worlds that are very different from the current one, and perhaps that makes them more perceptive when something truly unusual does come along.

Some of the individuals who were early to see and point out Covid-19 risk, such as tech entrepreneur Balaji Srinivasan, also have taken the UFO reports seriously, perhaps due to the same flexibility of mind.

Do read the whole thing, the column does not excerpt easily.

Rapid progress from Fast Grants

I was pleased to read this NYT reporting:

Yet another team has been trying to find drugs that work against coronavirus — and also to learn why they work.

The team, led by Nevan Krogan at the University of California, San Francisco, has focused on how the new coronavirus takes over our cells at the molecular level.

The researchers determined that the virus manipulates our cells by locking onto at least 332 of our own proteins. By manipulating those proteins, the virus gets our cells to make new viruses.

Dr. Krogan’s team found 69 drugs that target the same proteins in our cells the virus does. They published the list in a preprint last month, suggesting that some might prove effective against Covid-19…

It turned out that most of the 69 candidates did fail. But both in Paris and New York [where the drugs were shipped for testing], the researchers found that nine drugs drove the virus down.

“The things we’re finding are 10 to a hundred times more potent than remdesivir,” Dr. Krogan said. He and his colleagues published their findings Thursday in the journal Nature.

The Krogan team was an early recipient of Fast Grants, and you will find more detail about their work at the above NYT link.  Fast Grants is also supporting Patrick Hsu and his team at UC Berkeley:

And the work of the Addgene team:

Washington Post covers Fast Grants

Here is the opening:

Economist Tyler Cowen first sounded the alarm that America is unprepared for a pandemic in 2005, when he wrote a paper outlining ways the country should respond and, for a few years, ran a blog focused on the possibility of an avian flu outbreak.

Fifteen years later, as a novel coronavirus brings Cowen’s fears into reality, the George Mason University professor is trying to fix what he and others view as a structural problem impeding the scientific response to the crisis: the months-long application and review process scientists must endure to get their research funded.

Here is the full story by Will Hobson.  Recommended.

My Conversation with Glen Weyl

I found it interesting throughout, the first half was on Covid-19 testing, and the second half on everything else.  Here is the audio and transcript.  Here is the summary:

Tyler invited Glen to discuss the plan, including how it’d overcome obstacles to scaling up testing and tracing, what other countries got right and wrong in their responses, the unusual reason why he’s bothered by price gouging on PPE supplies, where his plan differs with Paul Romer’s, and more. They also discuss academia’s responsibility to inform public discourse, how he’d apply his ideas on mechanism design to reform tenure and admissions, his unique intellectual journey from socialism to libertarianism and beyond, the common element that attracts him to both the movie Memento and Don McLean’s “American Pie,” what talent he looks for in young economists, the struggle to straddle the divide between academia and politics, the benefits and drawbacks of rollerblading to class, and more.

Here is one excerpt:

And:

And:

For me the most instructive part was this:

COWEN: What do you view yourself as rebelling against? At the foundational level.

But you will have to read or listen to hear Glen’s very good answer.

Definitely recommended.

Modeling COVID-19 on a network: super-spreaders, testing and containment

These would seem to be some important results:

To model COVID-19 spread, we use an SEIR agent-based model on a graph, which takes into account several important real-life attributes of COVID-19: super-spreaders, realistic epidemiological parameters of the disease, testing and quarantine policies. We find that mass-testing is much less effective than testing the symptomatic and contact tracing, and some blend of these with social distancing is required to achieve suppression. We also find that the fat tail of the degree distribution matters a lot for epidemic growth, and many standard models do not account for this. Additionally, the average reproduction number for individuals, equivalent in many models to R0, is not an upper bound for the effective reproduction number, R. Even with an expectation of less than one new case per person, our model shows that exponential spread is possible. The parameter which closely predicts growth rate is the ratio between 2nd to 1st moments of the degree distribution. We provide mathematical arguments to argue that certain results of our simulations hold in more general settings.

And from the body of the paper:

To create containment, we need to test 30% of the population every day. If we only test 10% of the population every day, we get 34% of the population infected – no containment (blue bars).

As for test and trace:

Even with 100% of contacts traced and tested, still mass-testing of just over 10% of the population daily is required for containment.

The authors are not anti-testing (though relatively skeptical about mass testing compared to some of its adherents), but rather think a combination is required in what is a very tough fight:

Our simulations suggest some social distancing (short of lockdown), testing of symptomatics and contact tracing are the way to go.

That is all from a new paper by Ofir Reich, Guy Shalev, and Tom Kalvari, from Google, Google, and Tel Aviv University, respectively.  Here is a related tweetstorm.  With this research, I feel we are finally getting somewhere.

Early detection of superspreaders by mass group pool testing

Most of epidemiological models applied for COVID-19 do not consider heterogeneity in infectiousness and impact of superspreaders, despite the broad viral loading distributions amongst COVID-19 positive people (1-1 000 000 per mL). Also, mass group testing is not used regardless to existing shortage of tests. I propose new strategy for early detection of superspreaders with reasonable number of RT-PCR tests, which can dramatically mitigate development COVID-19 pandemic and even turn it endemic. Methods I used stochastic social-epidemiological SEIAR model, where S-suspected, E-exposed, I-infectious, A-admitted (confirmed COVID-19 positive, who are admitted to hospital or completely isolated), R-recovered. The model was applied to real COVID-19 dynamics in London, Moscow and New York City. Findings Viral loading data measured by RT-PCR were fitted by broad log-normal distribution, which governed high importance of superspreaders. The proposed full scale model of a metropolis shows that top 10% spreaders (100+ higher viral loading than median infector) transmit 45% of new cases. Rapid isolation of superspreaders leads to 4-8 fold mitigation of pandemic depending on applied quarantine strength and amount of currently infected people. High viral loading allows efficient group matrix pool testing of population focused on detection of the superspreaders requiring remarkably small amount of tests. Interpretation The model and new testing strategy may prevent thousand or millions COVID-19 deaths requiring just about 5000 daily RT-PCR test for big 12 million city such as Moscow.

Speculative, but I believe this is the future of our war against Covid-19.

The paper is by Maxim B. Gongalsky, via Alan Goldhammer.

A vaccine from Oxford?

In the worldwide race for a vaccine to stop the coronavirus, the laboratory sprinting fastest is at Oxford University.

Most other teams have had to start with small clinical trials of a few hundred participants to demonstrate safety. But scientists at the university’s Jenner Institute had a head start on a vaccine, having proved in previous trials that similar inoculations — including one last year against an earlier coronavirus — were harmless to humans. That has enabled them to leap ahead and schedule tests of their new coronavirus vaccine involving more than 6,000 people by the end of next month, hoping to show not only that it is safe, but also that it works.

The Oxford scientists now say that with an emergency approval from regulators, the first few million doses of their vaccine could be available by September — at least several months ahead of any of the other announced efforts — if it proves to be effective.

Here is more from the NYT.  I do not have a personal opinion on the specifics of this development, but it seems worth passing along.

Fast Grants update

Fast Grants has now made over 100 grants and contributed over $18 million in funding biomedical research against Covid-19, all in a little over two weeks’ time since project conception.  If you scroll down the home page, you can see a partial list of winners (we are more concerned with getting the money out the door than keeping the list fully updated, but it will continue to grow).

Fast Grants is part of Emergent Ventures, a project of the Mercatus Center, George Mason University.  And I wish to thank again all of those who have contributed to this project, either financially or otherwise.  A partial list of financial contributors can be found at the above link as well.

People are dying from coronavirus because research is too slow

For years, there’s been talk about making the clinical trial process more standardized, and cheaper, so that the same rules would apply each time a study needed to be run. There’s even been discussion that what are known as pragmatic trials — large, simple, randomized studies in which less data are collected — might be conducted using electronic health records. But that hasn’t happened at the pace it should.

The reason involves another part of the problem. Clinical trials are principally run by drug and medical device companies in order to obtain regulatory approvals, with public health authorities only picking up the slack in rare examples. But the result is that we have not built a system that would make studies simpler; most patients have little opportunity to participate in research; and we are too slow to figure out what works.

What would the system look like if we fixed it? It would make it easier to study drugs for heart disease, where studies are so large and expensive that many companies don’t test their medicines. It would ease studies for rare cancers, which are currently problematic because the right patients are hard to find. And it could create a medical information superhighway that would power health care through the next century.

That is from Matthew Herper in StatNews.  Via Malinga Fernando.