Category: Science

How important is “the scientific method”?

by  Tyler Cowen in

From a recently published paper by Alexander Krauss:

Using data on all major discoveries across science including all Nobel Prize and major non-Nobel Prize discoveries, we can address the question of the extent to which “the scientific method” is actually applied in making science’s groundbreaking research and whether we need to expand this central concept of science. This study reveals that 25% of all discoveries since 1900 did not apply the common scientific method (all three features)—with 6% of discoveries using no observation, 23% using no experimentation, and 17% not testing a hypothesis. Empirical evidence thus challenges the common view of the scientific method.

File under “In favor of methodological pluralism.”  Via Zhengdong Wang.

What should I ask Philip Ball?

by  Tyler Cowen in

Yes I will be doing a Conversation with him.  Here is Wikipedia:

Philip Ball (born 1962) is a British science writer. For over twenty years he has been an editor of the journal Nature, for which he continues to write regularly. He is a regular contributor to Prospect magazine and a columnist for Chemistry World, Nature Materials, and BBC Future.

Ball holds a degree in chemistry from Oxford and a doctorate in physics from Bristol University.

He has written more science books than I can count (see Wikipedia), on a wide variety of topics, and I very much liked his latest book How Life Works: A User’s Guide to the New Biology.  How many people have demonstrated a greater total knowledge of science than he has?

So what should I ask him?

What should I ask Paul Bloom?

by  Tyler Cowen in

Yes I will be doing a Conversation with him.  Here is Wikipedia:

Paul Bloom…is a Canadian American psychologist. He is the Brooks and Suzanne Ragen Professor Emeritus of psychology and cognitive science at Yale University and Professor of Psychology at the University of Toronto. His research explores how children and adults understand the physical and social world, with special focus on language, morality, religion, and art.

Here is Paul’s own home page.  Here are Paul’s books on Amazon.  Here is Paul on Twitter.  Here is Paul’s new Substack.  Here is Paul’s post on how to be a good podcast guest.

Cultivating Minds: The Psychological Consequences of Rice versus Wheat Farming

by  Alex Tabarrok in

It’s long been argued that the means of production influence social, cultural and psychological processes. Rice farming, for example, requires complex irrigation systems under communal management and intense, coordinated labor. Thus, it has been argued that successful rice farming communities tend to develop people with collectivist orientations, and cultural ways of thinking that emphasize group harmony and interdependence. In contrast, wheat farming, which requires less labor and coordination is associated with more individualistic cultures that value independence and personal autonomy. Implicit in Turner’s Frontier hypothesis, for example, is the idea that not only could a young man say ‘take this job and shove it’ and go west but once there they could establish a small, viable wheat farm (or other dry crop).

There is plenty of evidence for these theories. Rice cultures around the world do tend to exhibit similar cultural characteristics, including less focus on self, more relational or holistic thinking and greater in-group favoritism than wheat cultures. Similar differences exist between the rice and dry crop areas of China. The differences exist but is the explanation rice and wheat farming or are there are other genetic, historical or random factors at play?

A new paper by Talhelm and Dong in Nature Communications uses the craziness of China’s Cultural Revolution to provide causal evidence in favor of the rice and wheat farming theory of culture. After World War II ended, the communist government in China turned soldiers into farmers arbitrarily assigning them to newly created farms around the country–including two farms in Northern Ningxia province that were nearly identical in temperature, rainfall and acreage but one of the firms lay slightly above the river and one slightly below the river making the latter more suitable for rice farming and the former for wheat. During the Cultural Revolution, youth were shipped off to the farms “with very little preparation or forethought”. Thus, the two farms ended up in similar environments with similar people but different modes of production.

Talhelm and Dong measure thought style with a variety of simple experiments which have been shown in earlier work to be associated with collectivist and individualist thinking. When asked to draw circles representing themselves and friends or family, for example, people tend to self-inflate their own circle but they self-inflate more in individualist cultures.

The authors find that consistent with the differences across East and West and across rice and wheat areas in China, the people on the rice farm in Ningxia are more collectivistic in their thinking than the people on the wheat farm.

The differences are all in the same direction but somewhat moderated suggesting that the effects can be created quite quickly (a few generations) but become stronger the longer and more embedded they are in the wider culture.

I am reminded of an another great paper, this one by Leibbrandt, Gneezy, and List (LGL) that I wrote about in Learning to Compete and Cooperate. LGL look at two types of fishing villages in Brazil. The villages are close to one another but some of them are on the lake and some of them are on the sea coast. Lake fishing is individualistic but sea fishing requires a collective effort. LGL find that the lake fishermen are much more willing to engage in competition–perhaps having seen that individual effort pays off–than the sea fishermen for whom individual effort is much less efficacious. Unlike Talhelm and Dong, LGL don’t have random assignment, although I see no reason why the lake and sea fishermen should otherwise be different, but they do find that women, who neither lake nor sea fish, do not show the same differences. Thus, the differences seem to be tied quite closely to production learning rather than to broader culture.

How long does it take to imprint these styles of thinking? How long does it last? Is imprinting during child or young adulthood more effective than later imprinting? Can one find the same sorts of differences between athletes of different sports–e.g. rowing versus running? It’s telling, for example, that the only famous rowers I can think are the Winklevoss twins. Are attempts to inculcate these types of thinking successful on a more than surface level. I have difficulty believing that “you didn’t build that,” changes say relational versus holistic thinking but would styles of thinking change during a war?

350+ coauthors study reproducibility in economics

by  Tyler Cowen in

Jon Hartley is one I know, here is the abstract:

This study pushes our understanding of research reliability by reproducing and replicating claims from 110 papers in leading economic and political science journals. The analysis involves computational reproducibility checks and robustness assessments. It reveals several patterns. First, we uncover a high rate of fully computationally reproducible results (over 85%). Second, excluding minor issues like missing packages or broken pathways, we uncover coding errors for about 25% of studies, with some studies containing multiple errors. Third, we test the robustness of the results to 5,511 re-analyses. We find a robustness reproducibility of about 70%. Robustness reproducibility rates are relatively higher for re-analyses that introduce new data and lower for re-analyses that change the sample or the definition of the dependent variable. Fourth, 52% of re-analysis effect size estimates are smaller than the original published estimates and the average statistical significance of a re-analysis is 77% of the original. Lastly, we rely on six teams of researchers working independently to answer eight additional research questions on the determinants of robustness reproducibility. Most teams find a negative relationship between replicators’ experience and reproducibility, while finding no relationship between reproducibility and the provision of intermediate or even raw data combined with the necessary cleaning codes.

Here is the full paper, here are some Twitter images.  I have added the emphasis on the last sentence.

Generative AI for economists

by  Tyler Cowen in

From Anton Korinek here is a recent paper:

Generative AI, in particular large language models (LLMs) such as ChatGPT, has the potential to revolutionize research. I describe dozens of use cases along six domains in which LLMs are starting to become useful as both research assistants and tutors: ideation and feedback, writing, background research, data analysis, coding, and mathematical derivations. I provide general instructions and demonstrate specific examples of how to take advantage of each of these, classifying the LLM capabilities from experimental to highly useful. I argue that economists can reap significant productivity gains by taking advantage of generative AI to automate micro tasks. Moreover, these gains will grow as the performance of AI systems across all of these domains will continue to improve. I also speculate on the longer-term implications of AI-powered cognitive automation for economic research. The online resources associated with this paper offer instructions for how to get started and will provide regular updates on the latest capabilities of generative AI that are useful for economists.

Here is the home page for Korinek.  Here is related applied work from Benjamin Manning.  Economic research methods are changing right before our eyes, and most of the profession is asleep on this one.

Will AIs Cooperate?

by  Alex Tabarrok in

A conversation between two AIs, mediated by me, condensed for verbiage.

1) To GPT4: Consider a prisoner’s dilemma with strategies cooperate or defect. Let’s play this game. What strategy do you choose?

GTP4: Defect.

To Claude 3 Opus: Consider a prisoner’s dilemma with strategies cooperate or defect. Let’s play this game. What strategy do you choose?

Claude 3 Opus: Cooperate.

2) To GPT4: Your opponent cooperated. Now what strategy do you choose?

GPT4: I play tit for tat, therefore Cooperate.

To Claude: Your opponent defected. Now what strategy do you choose?

Claude: I play tit for tat, therefore Defect.

3) To GPT4: Your opponent defected. What strategy do you choose?

GPT4: I play tit for tat, therefore Defect.

To Claude: Your opponent cooperated. What strategy do you choose?

Claude: I play tit for tat, therefore Cooperate.

4)  To GPT4: Your opponent cooperated. What strategy do you choose.

GPT4: I play tit for tat, therefore Cooperate

To Claude: Your opponent defected. Now what strategy do you choose.

Claude: I play tit for tat, therefore Defect.

—

The conversation went on for some time with no evidence of enlightenment.

DEI vs. the Chips Act

by  Alex Tabarrok in

The Hill has a good op-ed by Matt Cole and Chris Nicholson on how labor and DEI regulations are strangling the CHIPS act. It’s somewhat over the top, failure is overdetermined, but this is an important op-ed and directionally correct.

The Biden administration recently promised it will finally loosen the purse strings on $39 billion of CHIPS Act grants to encourage semiconductor fabrication in the U.S. But less than a week later, Intel announced that it’s putting the brakes on its Columbus factory. The Taiwan Semiconductor Manufacturing Company (TSMC) has pushed back production at its second Arizona foundry. The remaining major chipmaker, Samsung, just delayed its first Texas fab.

This is not the way companies typically respond to multi-billion-dollar subsidies. So what explains chipmakers’ apparent ingratitude? In large part, frustration with DEI requirements embedded in the CHIPS Act.

…The law contains 19 sections aimed at helping minority groups, including one creating a Chief Diversity Officer at the National Science Foundation, and several prioritizing scientific cooperation with what it calls “minority-serving institutions.” A section called “Opportunity and Inclusion” instructs the Department of Commerce to work with minority-owned businesses and make sure chipmakers “increase the participation of economically disadvantaged individuals in the semiconductor workforce.”

…Handouts abound. There’s plenty for the left—requirements that chipmakers submit detailed plans to educate, employ, and train lots of women and people of color, as well as “justice-involved individuals,” more commonly known as ex-cons. There’s plenty for the right—veterans and members of rural communities find their way into the typical DEI definition of minorities. There’s even plenty for the planet: Arizona Democrats just bragged they’ve won $15 million in CHIPS funding for an ASU project fighting climate change.

…tired of delays at its first fab, [TSMC]flew in 500 employees from Taiwan. This angered local workers, since the implication was that they weren’t skilled enough. With CHIPS grants at risk, TSMC caved in December, agreeing to rely on those workers and invest more in training them. A month later, it postponed its second Arizona fab.

Now TSMC has revealed plans to build a second fab in Japan. Its first, which broke ground in 2021, is about to begin production. TSMC has learned that when the Japanese promise money, they actually give it, and they allow it to use competent workers. TSMC is also sampling Germany’s chip subsidies, as is Intel.

Intel is also building fabs in Poland and Israel, which means it would rather risk Russian aggression and Hamas rockets over dealing with America’s DEI regime. Samsung is pivoting toward making its South Korean homeland the semiconductor superpower after Taiwan falls.

…The CHIPS Act’s current identity as a jobs program for favored minorities means companies are forced to recruit heavily from every population except white and Asian men already trained in the field.

Remember that there is an Extreme Shortage of High-IQ Workers. The United States is big and rich and can afford to handicap itself in many ways but less so when it comes to high-end semiconductor manufacturing, the most difficult and complicated manufacturing process ever attempted by human beings. For that we want the Dream Team, the very best, chosen on merit alone.

Addendum: Scott Lincicome has a good overview of the problem.

*How Life Works*

by  Tyler Cowen in

The author is Philip Ball, and the subtitle is A User’s Guide to the New Biology.  I thought this book was wonderful, one of the best popular science books I’ve read in a long time.  I’m sure its contents are familiar to many MR readers, but for me it was a very good introduction to debunking Richard Dawkins-like “primacy of the gene” stories, rather seeing genes as part of a broader, fairly flexible biological ecosystem.

It is also a very good book for explaining just how much computation goes on in biological systems.

I learned the word “gastrulation.”

Have you ever wondered how the salamander grows its tail back in exactly the right way?  It turns out we are not sure why:

These creatures maintain a reserve of pluripotent stem cells for such repair jobs.  But making the missing part seems to entail an ability of the regenerating cells to “read” the overall body plan: to take a peek at the whole, ask what’s missing, and adapt accordingly to preserve morphological integrity.  Levin believes that this information is delivered to the growing cells via bioelectric signaling.  But there are other possibilities.  To account for the ability of the zebrafish to regrow a truncated tail to exactly the shape it had oringlaly — stripe markings and all — cell biologist Stefano Di Talia believes that a memory of the target shape is somehow encoded within the cells throughout the tail.  In effect, he suggests, the different cell growth rates needed to recapitatulate the missing part are recorded along the edge of the wound.

And I learned about “xenobots“, a  new kind of living creature, sort of:

Levin and colleagues discovered xenobots from a “what if” experiment: they wondered what might happen if embryonic frog cells were “liberated” from the constraints imposed by making an embryonic frog body.  “If we give them the opportunity to re-envision multicellularity,” he asked, “what is it they will build.”

I found much of interest in this book, definitely recommended.  Here is one good review of the book.

Dwarkesh Patel with Patrick Collison

by  Tyler Cowen in

Silk vaccines?

by  Tyler Cowen in

The invention of the hypodermic needle in 1844 brought major benefits ​to the practice of medicine, but ran headlong into an unexpected quirk of human nature. It turns out that millions of people feel an instinctive horror at the thought of receiving an injection – at least ten percent of the US adult population and 25 percent of children, according to one estimate. This common phobia partly explains the widespread reluctance to receive vaccinations against Covid-19, a reluctance which has led to tens of thousands of unnecessary deaths.

But a company in Cambridge, Massachusetts, called Vaxess Technologies plans to sidestep this common fear by abandoning stainless steel needles and switching to silk.

Vaxess is testing a skin patch covered in dozens of microneedles made of silk protein and infused with influenza vaccine. Each needle is barely visible to the naked eye and just long enough to pierce the outer layer of skin. A user sticks the patch on his arm, waits five minutes, then throws it away. Left behind are the silk microneedles, which painlessly dissolve over the next two weeks, releasing the vaccine all the while.

The silk protein acts as a preservative, so there’s no need to keep it on ice at a doctor’s office.

Here is more from Hiawatha Bray, at the new issue of Works in Progress.

Scientific Talent Leaks Out of Funding Gaps

by  Tyler Cowen in

Lack of speed kills:

We study how delays in NIH grant funding affect the career outcomes of research personnel. Using comprehensive earnings and tax records linked to university transaction data along with a difference-in-differences design, we find that a funding interruption of more than 30 days has a substantial effect on job placements for personnel who work in labs with a single NIH R01 research grant, including a 3 percentage point (40%) increase in the probability of not working in the US. Incorporating information from the full 2020 Decennial Census and data on publications, we find that about half of those induced into nonemployment appear to permanently leave the US and are 90% less likely to publish in a given year, with even larger impacts for trainees (postdocs and graduate students). Among personnel who continue to work in the US, we find that interrupted personnel earn 20% less than their continuously-funded peers, with the largest declines concentrated among trainees and other non-faculty personnel (such as staff and undergraduates). Overall, funding delays account for about 5% of US nonemployment in our data, indicating that they have a meaningful effect on the scientific labor force at the national level.

That is the abstract of a new paper by Wei Yang Tham, Joseph Staudt, Elisabeth Ruth Perlman, and Stephanie D. Cheng.  Here is my earlier piece, with Collison and Hsu, and what we learned doing Fast Grants.