The Ig Nobel Prize in Economics this year went to Pavlo Blavatskyy for Obesity of politicians and corruption in post-Soviet countries:
We collected 299 frontal face images of 2017 cabinet ministers from 15 post-Soviet states (Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine and Uzbekistan). For each image, the minister’s body-mass index is estimated using a computer vision algorithm. The median estimated body-mass index of cabinet ministers is highly correlated with conventional measures of corruption (Transparency International Corruption Perceptions Index, World Bank worldwide governance indicator Control of Corruption, Index of Public Integrity). This result suggests that physical characteristics of politicians such as their body-mass index can be used as proxy variables for political corruption when the latter are not available, for instance at a very local level.
Other prizes here.
You may laugh but don’t forget that the great Andre Geim won an Ig Nobel prize in 2000 for levitating a frog and then won a Nobel prize in 2010 for graphene. I consider this one of the greatest accomplishments in all of science.
Photo Credit: Journal of Wildlife Diseases.
Talking with Ezra is always both fun and enlightening for me, here is his partial summary of the episode:
So we begin this conversation by discussing the case for and against economic growth, but we also get into lots of other things: why Cowen thinks the great stagnation in technology is coming to an end; the future of technologies like A.I., crypto, fourth-generation nuclear and the Chinese system of government; the problems in how we fund scientific research; what the right has done to make government both ineffective and larger; why Cowen is skeptical of universal pre-K (and why I’m not); whether I overestimate the dangers of polarization; the ways in which we’re getting weirder; the long-term future of human civilization; why reading is overrated and travel is underrated; how to appreciate classical music and much more.
Phoebe Yao, founder and CEO of Pareto, “a human API delivering the business functions startups desperately need.” Here is the Pareto website. She was born in China, formerly of Stanford, and a former classical violist. (By my mistake I left her off of a previous cohort list, apologies!)
BeyondAging, a new group to support longevity research.
Gavin Leech, lives in Bristol, he is from Scotland, getting a Ph.D in AI. General career support, he is interested in: “Personal experimentation to ameliorate any chronic illness; reinforcement learning as microscope on Goodhart’s law; weaponised philosophy for donors; noncollege routes to impact.”
Valmik Rao, 17 years old, Ontario, he is building a program to better manage defecation in Nigeria.
Samantha Jordan, NYU Stern School of Business, with Nathaniel Bechhofer, for a new company, “Our platform will accelerate the speed and quality of science by enabling scientists to easily manage their data and research pipelines, using best practices from software engineering.” Also a Progress Studies grant.
Nina Khera, “I’m a teenage human longevity researcher who’s interested in preventing aging-related diseases, especially those related to brain aging. In the past, I’ve worked with companies like Alio on computation and web-dev-based projects. I’ve also worked with labs like the Gladyshev lab and the Adams lab on data analysis and machine learning-based projects.” Her current project is Biotein, about developing markers for aging, based in Ontario.
Thus, to analysts, picking one such meta-analysis may feel as hard as picking a single “best study.” This paper responds by taking the meta-analysis another step, estimating a meta-analysis (or mixture distribution) of six meta-analyses. The baseline model yields a central VSL of $7.0m, with a 90% confidence interval of $2.4m to $11.2m. The provided code allows users to easily change subjective weights on the studies, add new studies, or change adjustments for income, inflation, and latency.
In a large, randomized clinical trial conducted with thousands of patients over the past six months, researchers at McMaster University tested eight different Covid-19 treatments against a control group to figure out what works.
One drug stood out: fluvoxamine, an antidepressant that the Food and Drug Administration has already found to be safe and that’s cheap to produce as a generic drug.
…This study, called the TOGETHER study, is a lot bigger — more than 3,000 patients across the whole study, with 800 in the fluvoxamine group — and supports the promising results from those previous studies. The authors released it this week as a preprint, meaning that it is still under peer review.
Patients given fluvoxamine within a few days after testing positive for Covid-19 were 31 percent less likely to end up hospitalized and similarly less likely to end up on a ventilator. (Death from Covid-19 is rare enough that the study has wide error bars when it comes to how much fluvoxamine reduces death, meaning it’s much harder to draw conclusions.) It’s a much larger effect than any that has been found for an outpatient Covid-19 treatment so far.
The role of Fast Grants is discussed toward the end, note that for us this was a major investment and done on very short notice, as befits the name Fast Grants.
Various domains of life are improving over time, meaning the future is filled with exciting advances that people can now look forward to (e.g., in technology). Three preregistered experiments (N = 1,602) suggest that mere awareness of better futures can risk spoiling otherwise enjoyable presents. Across experiments, participants interacted with novel technologies—but, via random assignment, some participants were informed beforehand that even better versions were in the works. Mere awareness of future improvement led participants to experience present versions as less enjoyable—despite being new to them, and despite being identical across conditions. They even bid more money to be able to end their participation early. Why? Such knowledge led these participants to perceive more flaws in present versions than they would have perceived without such knowledge—as if prompted to infer that there must have been something to improve upon (or else, why was a better one needed in the first place?)—thus creating a less enjoyable experience. Accordingly, these spoiling effects were specific to flaw-relevant stimuli and were attenuated by reminders of past progress already achieved. All told, the current research highlights important implications for how today’s ever better offerings may be undermining net happiness (despite marking absolute progress). As people continually await exciting things still to come, they may be continually dissatisfied by exciting things already here.
Here is the audio and transcript. Here is part of the summary:
Zeynep joined Tyler to discuss problems with the media and the scientific establishment, what made the lab-leak hypothesis unacceptable to talk about, how her background in sociology was key to getting so many things right about the pandemic, the pitfalls of academic contrarianism, what Max Weber understood about public health crises, the underrated aspects of Kemel Mustapha’s regime, how Game of Thrones interested her as a sociologist (until the final season), what Americans get wrong about Turkey, why internet-fueled movements like the Gezi protests fizzle out, whether Islamic fundamentalism is on the rise in Turkey, how she’d try to persuade a COVID-19 vaccine skeptic, whether public health authorities should ever lie for the greater good, why she thinks America is actually less racist than Europe, how her background as a programmer affects her work as a sociologist, the subject of her next book, and more.
Here is one excerpt:
COWEN: Max Weber — overrated or underrated as a sociologist?
TUFEKCI: Part of the reason he’s underrated is because he writes in that very hard-to-read early 19th-century writing, but if you read Max Weber, 90 percent of what you want to understand about the current public health crisis is there in his sociology. Not just him, but sociology organizations and how that works. He’s good at that. I would say underrated, partly because it’s very hard to read. It’s like Shakespeare. You need the modern English version, conceptually, for more people to read it.
I would say almost all of sociology is underrated in how dramatically useful it is. Just ask me any time. Early on, I knew we were going to have a pandemic, completely based on sociology of the moment in early January, before I knew anything about the virus because they weren’t telling us, but you could just use sociological concepts to put things together. Max Weber is great at most of them and underrated.
COWEN: Kemal Mustafa — overrated or underrated?
TUFEKCI: Why? My grandmother — she was 12 or 13 when she was in the Mediterranean region — Central Asia, but Mediterranean region, very close to the Mediterranean. She was born the year the Turkish Republic had been founded, 1923, and she was 13 or so. She was just about to be married off, but the republic was a little over a decade — same age as her. They created a national exam to pick talented girls like her. The ones that won the exam got taken to Istanbul to this elite, one of the very few boarding high schools for girls.
The underrated part isn’t just that such a mechanism existed. The underrated part is that the country changed so much in 13 years that her teacher was able to prevail upon the family to let her go. To have a 13-year-old be sent off to Istanbul, completely opposite side of the country, to a boarding school for education — that kind of flourishing of liberation.
I’m not going to deny it was an authoritarian period, and minorities, like Kurds, during that period were brutally suppressed. I can’t make it sound like there was nothing else going on, but in terms of creating a republic out of the ashes of a crumbling empire — I think it’s one of the very striking stories of national transformation, globally, within one generation, so underrated.
There are numerous interesting segments, on varied topics, to be found throughout the dialog.
Publishing in economics proceeds much more slowly on average than in the natural sciences, and more slowly than in other social sciences and finance. It is even relatively slower at the extremes. We demonstrate that much of the lag, especially at the extremes, arises from authors’ dilatory behavior in revising their work. The marginal product of an additional round of re-submission at the top economics journals is productive of additional subsequent citations; but conditional on re-submission, journals taking more time is not productive, and authors spending more time is associated with reduced scholarly impact. We offer several proposals to speed up the publication process. These include no-revisions policies; limits on authors’ time revising articles, and limits on editors waiting for dilatory referees.
Publishing takes a long time in economics. Consequently, many authors release “working” versions of their papers. Using data on the NBER working paper series, we show that the dissemination of economics research suffers from an overcrowding problem: An increase in the number of weekly released working papers on average reduces downloads, abstract views, and media attention for each paper. Subsequent publishing and citation outcomes are harmed as well. Furthermore, descriptive evidence on viewership and downloads suggests working papers significantly substitute for the dissemination function of publication. These results highlight inefficiencies in the dissemination of economic research even among the most exclusive working paper series and suggest large social losses due to the slow publication process.
Is less attention for each paper necessarily a bad thing?
“A new research study by one of us and his Johns Hopkins colleagues found that of the $42 billion the National Institutes of Health spent on research last year, less than 2% went to Covid clinical research…
● Of the $42 Billion 2020 NIH annual budget, 5.7% was spent on
● Public health research was underfunded at 0.4% of the 2020 NIH
● Only 1.8% of the 2020 NIH budget was spent on COVID-19 clinical
● Average COVID-19 NIH funding cycle was 5 months
● Aging was funded 2.2 times more than COVID-19 research
● By May 1, 2020, 3 months into the pandemic, the NIH spent 0.05%
annual budget on COVID-19 research
● Of the 1419 grants funded by the NIH:
• NO grants on kids and masks specifically
• 58 studies on social determinants of health
• 57 grants on substance abuse
• 107 grants on developing COVID-19 medications
• 43 of the 107 medication grants repurposed existing drugs
Ouch. Here is a not entirely random sentence from the report:
The COVID-19 pandemic has only exacerbated the NIH institutional challenges and inability to reallocate funds quickly to
Here is another damning sentence, though it damns someone other than the NIH:
…to date, no research has investigated NIH COVID-19 funding patterns to the best of our knowledge.
Double ouch. Might the NIH have too much influence over the allocation of funds to be investigated properly? Rooftops, people…
…participants who trust science are more likely to believe and disseminate false claims that contain scientific references than false claims that do not. Second, reminding participants of the value of critical evaluation reduces belief in false claims, whereas reminders of the value of trusting science do not. We conclude that trust in science, although desirable in many ways, makes people vulnerable to pseudoscience.
That is the theme of my latest Bloomberg column, here is one excerpt:
The notion that the future will be weirder than we think, and come sooner, is a possibility raised by Holden Karnofsky, the co-chief executive officer of Open Philanthropy. It’s an intriguing and provocative idea.
I consider genetic engineering, longevity research, finding signs of life on other planets, neural engineering, and AI as possible developments, plus a bit more.
…these changes are far more radical than those that occurred between 1921 and today. Compared to 1921, we are much wealthier and more secure — but a lot of basic structures of the world remain broadly the same. I don’t think that much of what we can do now would strike our 1921 predecessors as magical, though the speed and power of our computers might surprise them. Nor would visitors from 1921 think of us as somehow not human.
Of course none of these developments are inevitable. Another very weird future is entirely possible: that we humans use our creative energies for destruction, causing civilization to take some major and enduring steps backwards.
Either way, the future is not just more and nicer suburbs, better pay and new forms of social media. All those are likely to happen, but they won’t be the biggest changes. When it comes to the future of the human race, we — and our children, for those of us who have any — may turn out to be especially important generations. I very much hope we are up to this moment.
The U.S. agency leading the fight against Covid-19 gave up a crucial surveillance tool tracking the effectiveness of vaccines just as a troublesome new variant of the virus was emerging.
While the Centers for Disease Control and Prevention stopped comprehensively tracking what are known as vaccine breakthrough cases in May, the consequences of that choice are only now beginning to show.
Here is more from Bloomberg, tragic and stupid throughout.
From Google’s Deep Mind:
In recent years, artificial intelligence agents have succeeded in a range of complex game environments. For instance, AlphaZero beat world-champion programs in chess, shogi, and Go after starting out with knowing no more than the basic rules of how to play. Through reinforcement learning (RL), this single system learnt by playing round after round of games through a repetitive process of trial and error. But AlphaZero still trained separately on each game — unable to simply learn another game or task without repeating the RL process from scratch.
…Today, we published “Open-Ended Learning Leads to Generally Capable Agents,” a preprint detailing our first steps to train an agent capable of playing many different games without needing human interaction data. We created a vast game environment we call XLand, which includes many multiplayer games within consistent, human-relatable 3D worlds. This environment makes it possible to formulate new learning algorithms, which dynamically control how an agent trains and the games on which it trains. The agent’s capabilities improve iteratively as a response to the challenges that arise in training, with the learning process continually refining the training tasks so the agent never stops learning. The result is an agent with the ability to succeed at a wide spectrum of tasks — from simple object-finding problems to complex games like hide and seek and capture the flag, which were not encountered during training. We find the agent exhibits general, heuristic behaviours such as experimentation, behaviours that are widely applicable to many tasks rather than specialised to an individual task. This new approach marks an important step toward creating more general agents with the flexibility to adapt rapidly within constantly changing environments. (Bold added, AT).
In other news, South Africa awarded the first ever patent to an AI.
It seems they do:
We document appearance effects in the economics profession. Using unique data on PhD graduates from ten of the top economics departments in the United States we test whether more attractive individuals are more likely to succeed. We find robust evidence that appearance has predictive power for job outcomes and research productivity. Attractive individuals are more likely to study at higher ranked PhD institutions and are more likely to be placed at higher-ranking academic institutions not only for their first job, but also for jobs as many as 15 years after their graduation, even when we control for the ranking of PhD institution and first job. Appearance also predicts the success of research output: while it does not predict the number of papers an individual writes, it predicts the number of citations for a given number of papers, again even when we control for the ranking of the PhD institution and first job. All these effects are robust, statistically significant, and substantial in magnitude.
That is from a recent paper by Galina Hale, Tali Regev, and Yona Rubinstein. Via John Chilton.