Knowledge for 1990 Children

We picked up a yard sale book: People and Places: A Random House Tell Me About Book.* When I saw that the U.S.S.R. was a huge swath across the northern hemisphere (drawn as a Mercator projection), I checked the publication date. It was published in New York in 1991 by Random House.**

This content would have been considered uncontroversial knowledge for children. It was written by Boomers for Millennials, one year before The End of History came out.***

The first fact discussed is that the earth had about 5 billion people and they saw no end to population growth. The book states that the world could be up to 15 billion people within 60 years (which would be 2050). Today, it is predicted that world population will peak soon and then decline. Fertility rates in most rich countries are currently below replacement and birth rates are falling everywhere. I guess the authors didn’t see that coming.

On the next page is a matter-of-fact explanation that A.D. stands for Anno Domini. If there was a new edition printed today, they would likely follow the academic trend of using BCE/CE, to avoid referencing religion.

Much of the book is about culture, with illustrations. In today’s terminology, this might be considered an attempt at color-blindness. All of the major world religions are presented next to each other with a neutral/positive spin on each. Racial and gender representation is carefully balanced, like the stock images I grew up with in American public school.

Considering how many students were forced to learn remotely this year, I liked the section on the Australian School of the Air. Remote farm children talked to a teacher by radio and sent written work by mail.

At the end is the answer to, “How will we live in the future?” Jeff Bezos might be happy to know that they predict space travel will be more common and people will live in space colonies. The stated reason for space colonization was the predicted unrelenting population growth. There wasn’t a hint of pessimism about, for example, global warming.

Their diagram of a futuristic house has a “Main computer” prominently featured. They predicted that computerized machines would do more work for humans, which has already happened in the past 30 years. The idea of mobile computers and internet services was probably not considered. They imagined house-bound clunky robots that could follow simple instructions.

*Currently still available on Amazon

** Lithuania declared independence from the Soviet Union in 1990. I suppose the publishers couldn’t be bothered to stop the presses.

*** In 1991, Gen X may have been too old to be the target audience of a children’s’ book.

Informational Diabetes

We all recognize that in the Internet Age, it is easy to communicate and to access information.

For the infovores, this is a cause for celebration.

Others worry that this leads to “information overload”, and to the spread of “disinformation” and “misinformation”. While this is clearly true, complaints about it typically seem to come from elites longing for the days when they had the only microphone, before the Revolt of the Public. Its hard to banish “misinformation” without screening out differences of opinion and correct contrarians even if you want to- and for some, such “collateral damage” would in fact be the main goal. But clearly something is wrong with the current information environment.

In a recent podcast appearance, Balaji Srinivasan used a metaphor I like better- Informational Diabetes:

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Thread on Programming Ability

Ethan Mollick brought this Nature article to my attention. One of the authors Chantel Prat, is also on the thread.

The sample size for this study is only 36, so we should think of it as preliminary work toward understanding how people learn to program.

Their abstract, with emphasis added by me:

This experiment employed an individual differences approach to test the hypothesis that learning modern programming languages resembles second “natural” language learning in adulthood. Behavioral and neural (resting-state EEG) indices of language aptitude were used along with numeracy and fluid cognitive measures (e.g., fluid reasoning, working memory, inhibitory control) as predictors. Rate of learning, programming accuracy, and post-test declarative knowledge were used as outcome measures in 36 individuals who participated in ten 45-minute Python training sessions. The resulting models explained 50–72% of the variance in learning outcomes, with language aptitude measures explaining significant variance in each outcome even when the other factors competed for variance. Across outcome variables, fluid reasoning and working-memory capacity explained 34% of the variance, followed by language aptitude (17%), resting-state EEG power in beta and low-gamma bands (10%), and numeracy (2%). These results provide a novel framework for understanding programming aptitude, suggesting that the importance of numeracy may be overestimated in modern programming education environments.

Learning Python, at least at first, is more like learning a foreign natural language than it is like doing arithmetic problems.

There are still many open questions in this area, so I see this paper as an important small step in the right direction. I have also done a study on this topic.

Endless Frontiers: Old-School Pork or New Cold War Tech Race?

The Endless Frontiers Act passed the Senate Tuesday in a bipartisan 68-32 vote. What was originally a $100 Billion bill to reform and enhance US research in ways lauded by innovation policy experts went through 616 amendments. The bill that emerged has fewer ambitious reforms, more local pork-barrel spending, and some totally unrelated additions like “shark fin sales elimination”. But it does still represent a major increase in US government spending on research and technology- and other than pork, the main theme of this spending is to protect US technological dominance from a rising China. One section of the bill is actually called “Limitation on cooperation with the People’s Republic of China“, and one successful amendment was “To prohibit any Federal funding for the Wuhan Institute of Virology

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John Duffy Experiments and Crypto

John Duffy and Daniela Puzzello published a paper in 2014 on adopting fiat money. I think of that paper when I hear the ever-more-frequent discussions of crypto currencies around me. To research the topic, I went to John Duffy’s website. There I found a May 2021 working paper about adopting new currencies in which they directly reference crypto. Before explaining that interesting new paper, first I will summarize the 2014 paper “Gift Exchange versus Monetary Exchange.”

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More computer jobs than San Francisco

The U.S. Bureau of Labor Statistics reports Occupational Employment and Wages from May 2020 for

15-0000 Computer and Mathematical Occupations (Major Group). The website contains a few interesting insights.

Where are the computer jobs in the United States? When looking just at total numbers of jobs, three major population centers make it into the top 7 areas: NYC, LA, and Chicago. San Francisco is ahead of Chicago, while San Jose is behind Chicago. In terms of the total number of jobs, the D.C. area is ahead of any West Coast city. Is Silicon Valley not as central as we thought?

Here’s a map of the U.S. that isn’t just another iteration of population density.

When metropolitan areas are ranked by employment in computer occupations per thousand jobs, then New York City no longer makes the top-10 list. San Jose, California reigns at the top, which seems suitable for Silicon Valley. The 2nd ranked area will surprise you: Bloomington, IL. A region of Maryland and Washington D.C. shouldn’t surprise anyone. If you aren’t familiar with Alabama, then would you expect Huntsville to rank above San Francisco in this list?

Huntsville, AL is not a large city, but it is a major hub for government-funded high-tech activity. The small number of people who live there overwhelmingly selected in to take a high-tech job. For an example, I quickly checked a job website to sample in Huntsville. Lockheed Martin is hiring a “Computer Systems Architect” based in Huntsville.

Anyone familiar with Silicon Valley already knows that the city of San Francisco was not considered core to “the valley”. Even though computer technology seems antithetical to anything “historical”, there is in fact a Silicon Valley Historical Association. They list the cities of the valley, which does include San Francisco. (corrected an error here)

The last item reported on this Census webpage is annual mean wage. For that contest, San Francisco does seem grouped with the San Jose area, at last. The computer jobs that pay the most are in Silicon Valley or next-door SF. Those middle-of-the-country hotspots like Huntsville do not make the top-10 list for highest paid. However, if cost of living is taken into account, some Huntsville IT workers come out ahead.

Teaching through my R mistakes

I blogged earlier about a new textbook that I am adopting for an analytics course. The first few chapters are primarily an introduction to using the R coding language within RStudio. One of the resources I’m posting for students this week is screen capture videos of me manipulating data in RStudio.

Sometimes I make mistakes, shockingly. I’m a professional, and yet sometimes I still make careless typos in R. I found out that my version of R was outdated, right when I was in the middle of recording a lecture.

I could have deleted the footage of my mistakes. I could have re-recorded a clean smooth video in which I run command after command without saying “ok… I got an error”.

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Not all robots have faces

Through Twitter, I have become aware of the SNOO. I’m quoting SNOO literature

Unfortunately, babies don’t sleep well on flat, still beds in totally quiet rooms. In fact, over 50% of

babies still wake up once a night after 6 long months. That’s a problem because poor baby sleep

causes the #1 new parent stress: EXHAUSTION!

SNOO gives a perfect 4th trimester of gentle shushing and rocking…all night long. And, it quickly

responds to your babies’ fussies with stronger sound and jiggly motion…

The bed hears your baby cry and rocks them back to sleep! I can probably count the number of times I have slept through the night in the past 6 years on two hands. If used wisely, this machine sounds like an incredible gift to families. (I can also see problems if used unwisely.) If the baby is crying for longer than 3 minutes, then the machine turns off and the expectation is that a parent needs to step in.

This reminds me of Tyler Cowen’s book Average is Over.

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The Problem is the Science

The University has been the engine of basic science in the US and abroad for a long time. Any hand-wringing in recent years over its imminent obsolescence was borne of advances in remote learning and new found capacities to exponentially scale single instructors to reach tens, if not hundreds, of thousands of students across the globe. How, in this brave-ish new world, would matriculant tuition accruing to a handful of instructional specialists/celebrities continue to subsidize the scientific mission?

If the arrival of YouTube and Khan Academy gave credence to the academic apocalypse theory, then the coronavirus pandemic and the global adoption of Zoom instruction would surely make a reality of it. I will admit, for the first time in my career, I’m seeing the cracks in the edifice of the academy. And, yes, it was the pandemic that made them more prominent to me.

But its not on the educational side of our dual mission. It’s the science.

Dr. Katalin Kariko is very likely to win a Nobel prize for her immense contributions to our understanding of messenger RNA (mRNA) and how it can be manipulated to create an entirely new class of vaccines that, it is not hyperbole to say, stand to offer a global shift in health. The prospect is there for not just an HIV vaccine, or a broad-spectrum influenza vaccine, or a malaria vaccine, but the broad mitigation of viruses as a burden on humanity.

Dr. Kariko has been pursuing her scientific mission with a single-mindedness that jumps off the page in everything that has been written about her. What also jumps off the page, at least to those of use who have been trying make a career in academic research, is the university system that has worked diligently for decades to push Dr. Kariko, and her scientific mission, out of the academy. At every stage of the hiring, retention, and grant application process, Dr. Kariko’s research has been bludgeoned with not so much criticism or doubt, but what seems more like horrifying indifference. Grant reviewers saw little value, her colleagues noted that she lacked finesse in writing grant applications, and the academic institutions that employed her saw little value in employing someone, even for less than $60k a year in salary, that was unable to consistently bring in large grants (sidenote: her husband often estimated her effective wage to be roughly a dollar an hour: from the university’s point of view, it wasn’t the expense she represented on the balance sheet, it was the opportunity cost of the grants she wasn’t winning that someone else in her slot would).

This is a problem.

To be clear, this indifference is far more damning than any sort of broad disagreement would have been. The nature of science is such that most advances are incremental, but every now and then there are the rare revolutionary upheavals, where something we thought we absolutely knew for sure turns out to be completely wrong. That scientific mavericks that push such theories, most of which are completely wrong, meet resistance is natural (and probably optimal). But indifference is a problem, because indifference does more to reveal the underlying incentives propelling researchers. Universities were indifferent to her research because it wasn’t generating grant money, and that is the job she was hired to do.

Patents are great. Prestigious awards are welcome. Published papers are not entirely a waste of your time. But make no mistake, if you don’t successfully apply for grants, your days in academic science are numbered. I spent three years as an oddly appointed economist in arguably the greatest medical school of the last decade. I got to hang around brilliant physicians who spent a lot of their time every week actually (not figuratively or indirectly) saving lives. I also witnessed dedicated researchers break down into tears upon receiving the news that their grant application had been denied, which meant their contract with the university would not be renewed and their research career effectively terminated. I saw how little grant application aptitude correlated with talent or passion. I saw people thrive in system while others failed, with little in the way of scientific aptitude to distinguish them.

The most practical advice I was privy to was this: work in someone’s lab, pursue your project in parallel with their resources. Once you have an advance that would be worthy of a grant application, write up the application for a project you‘ve already completed. List your previous PI as a collaborator, promise exactly the results you already have, describe your budget, schedule, and proposed outputs in shocking detail, and then radically oversell the importance of the discovery. Once you win the grant, use that money to pursue your next project while writing up the outcome of your previous one. Once you have results, apply for yet another retrospective funding grant, and continue to daisy chain that until you win a massive grant, a coveted NIH R-1 perhaps, within which you can bundle a series of projects, hiring as many post-docs and early researchers as you can. You will then manage this team who will execute your research while hopefully starting their own retrospective grant application daisy chains. Is this a common strategy? I don’t know – it seems odd that the dates of human subjects testing could be obscured. But the point was made to me – this isn’t about science, this is a career life-or-death game where only the 20% of applicants are funded.

To be honest, I don’t care that people are gaming funding institutions. And, to be clear, “playing the game” is part of any career, no matter how idealistic you want to be. Academic research science is in deep, deep trouble, however, if grant application gamesmanship dominates scientific ingenuity in the talent acquisition and retention strategies of major universities. It means we’re no longer scientists, we’re rent-seekers. We’re the person in the village best at memorizing Mao’s Little Red Book: smart, talented, but in the end wasted. Or, much worse, we’re just poseurs.

Piecing together what I’ve read in articles and her Wikipedia entry, after Penn demoted her to adjunct status, Dr. Kakri found a home at BioNTech in 2013, where they and other biotech firms saw tremendous value in her work, yada yada yada, her research with Draw Weissman saved millions of lives going forward and maybe just the whole damn world.

Two takeaways:

  1. If Penn, after demoting her to being an adjunct, tries to claim her and her work as their own we riot.
  2. What is the marginal value of university research if all we’re producing is grant applications?

Part of the blame, of course, has to be placed at the door of the NIH and NSF grant application review process. But how much longer are they going to matter either?

  1. 2021 NSF Budget: $8.5 Billion
  2. 2021 NIH Budget: $43 Billion
  3. Tesla Market Cap: $650 Billion
  4. Elon Musk net worth: $167 Billion

The whole point of the NSF, NIH, and the academic research project is the production of the public good that is basic science. Absent private profit incentives, they should be able to pursue the big picture project that are too broad in application for private companies and the high risk-high reward projects that are or venture too risky even for venture capital.

The advantages of government agencies, however, are limited if they are overwhelmingly surpassed in scale by private market science. Even if 99% of firms can’t overcome the public goods problem, the 1% (ironically within public economics what would be referred to as “privileged groups”) of firms that stand to profit from advancing basic science have the scale to execute such ambitions. More importantly, however, they may also have better incentives. Yes, they are greedily trying to make a profit off of their innovations, but at least the innovation remains their goal.

I’m not worried about the value of university professors as educators. It turns out that education doesn’t scale as well as we thought. That there is tremendous value to be in a room together when you’re trying to pass on explicit, complex, and tacit knowledge. Nor am I worried in the slightest about capital-S Science. There is a bright future for any and every institution producing science, even the most basic, broadest science that no private company or patent strategy could ever exclude others from benefiting from. But, I’m afraid, there is no future for the production of grant applications or the institutions that pursue them at the expense of brilliant minds trying to solve our most important puzzles.

On Cylindrical Revolutions

The three technological innovations new to my life in the last year with the greatest impact are:

  1. Pfizer mRNA vaccines (price = $19.50, input costs: no less than $2 Billion, probably more)
  2. Amazon Basics Foam Roller (price $18.99, input costs: $4.44 per ounce of styrofoam)
  3. Zoom teleconferencing (price: $no idea what my school pays for it, input costs: $146 Million in venture funding)

The vaccine, of which I am scheduled to receive my first dose of tomorrow, will allow me to (sort-of) return to my pre-pandemic life. The introduction and regular use of a cylinder of high-density styrofoam has given me a better functioning left leg than I’ve enjoyed in 5 years. Zoom has arguably done more to maintain my the short-term integrity of my income (i.e. it’s allowed me to teach online effectively).

That is a very oddly shaped distribution of investments in high-utility yield innovations.

Biotechnology and medicine as a high investment, high risk, big payoff innovation game is well understood. Less known was whether or not a rapid “innovation on demand” vaccine project was an achievable outcome, no matter how much money was thrown at it. Turns out it was, and we’re left with what might be the most impressive feat of willed innovation since the moon landing. High-resolution teleconferencing technology, on the other hand, is exactly the kind of product we’ve grown accustomed to modern tech firms producing– the supply of such innovative products via the private capital-entrepreneurship pipeline is almost always less in question than the eventual demand it may or may not find in the marketplace.

But what of treating your muscles like sugar cookie dough? This is neither a sophisticated new composition of materials nor, at face value, a particularly complex theory of musculature. But, to my knowledge, this is not something even professional athletes were doing 7 years ago, yet now is both the bleeding edge of physical maintenance and such common knowledge that everyone who’s strained a muscle in the last 6 months currently has one of these cylinders leaning against a wall in their home. And, while I don’t mean to oversell it, the introduction of foam rolling has massively improved the quality of my life, not just when I try to play any sort of sport, but when I walk down a flight of stairs. It’s not crazy to suggest it may buy me an extra decade of easy use of my preferred mode of transportation, and while using my natural knees at that.

Investment in innovation is an interesting thing – there appears to be significant returns to scale at the micro, meso, and macro levels. Firms flush with capital can focus teams on single problems, fill them with talent, and grant them the keys to every piece of equipment deemed to hold even the slightest possibility of aid en route to an end product. There are simply innovative outcomes on the horizon for the Pfizers of the world that will never be available to scrappy new start-ups. At the same time, we can see the network-driven returns to scale in markets, a la Silicon Valley or Hollywood, that only begin to appear when a critical mass of agents all find themselves drawn to the bubbling creative soups that appears in the diners, salons, and coffee shops of whatever place has become the place.

But there are scale returns at the most macro of macro levels as well, and that is where we get miraculous cylinders of foam, as well as wheels on suitcases and the polymerase chain reaction. People are many things. Occupiers of space. Emitters of carbon dioxide. Consumers of fried dough. Sometimes while doing all three they also come up with ideas.

Humans as idea machines lies at the core of Michael Kremer’s theory of economic growth, and it is perhaps my favorite idea within economics in the last 40 years. Simply put, more people leads to more ideas. Population growth is not just a product of innovation, it is a source of it. Every individual is a lottery ticket that we hope pays off with a world changing eureka moment that the rest of us can benefit from and build on for all time going forward. More people, more lottery tickets.

Those organic globules of cognitive betting slips coalesce into the long tail of innovation return on investment. We take the brightest minds, throwing them and piles of cash at our biggest problems, hoping that for the closest thing to a assured payoff. But it’s within the billions of people, and their billions of bad ideas that sometimes aren’t, within which we get countless miracles that change our lives for the better bit by bit, one smoothened middle-aged stride at a time.