Category: Science

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.

What should I ask Michael Nielsen?

by  Tyler Cowen in

I will be doing a Conversation with him.  No description of Michael quite does him justice, but here is Wikipedia:

Michael Aaron Nielsen (born January 4, 1974) is a quantum physicist, science writer, and computer programming researcher living in San Francisco.

In 1998, Nielsen received his PhD in physics from the University of New Mexico. In 2004, he was recognized as Australia’s “youngest academic” and was awarded a Federation Fellowship at the University of Queensland. During this fellowship, he worked at the Los Alamos National Laboratory, Caltech, and at the Perimeter Institute for Theoretical Physics.

Alongside Isaac Chuang, Nielsen co-authored a popular textbook on quantum computing, which has been cited more than 52,000 times as of July 2023.

In 2007, Nielsen shifted his focus from quantum information and computation to “the development of new tools for scientific collaboration and publication”, including the Polymath project with Timothy Gowers, which aims to facilitate “massively collaborative mathematics.” Besides writing books and essays, he has also given talks about open science. He was a member of the Working Group on Open Data in Science at the Open Knowledge Foundation.

Nielsen is a strong advocate for open science and has written extensively on the subject, including in his book Reinventing Discovery, which was favorably reviewed in Nature and named one of the Financial Times’ best books of 2011.

In 2015 Nielsen published the online textbook Neural Networks and Deep Learning, and joined the Recurse Center as a Research Fellow. He has also been a Research Fellow at Y Combinator Research since 2017.

In 2019, Nielsen collaborated with Andy Matuschak to develop Quantum Computing for the Very Curious, a series of interactive essays explaining quantum computing and quantum mechanics. With Patrick Collison, he researched whether scientific progress is slowing down.

Here is Michael’s Notebook, well worth a browse and also a deeper read.  Here is Michael on Twitter.  So what should I ask him?  (I’m going to ask him about Olaf Stapledon in any case, so no need to mention that.)

My Conversation with the excellent Ami Vitale

by  Tyler Cowen in

Here is the audio, visual, and transcript.  Here is the episode summary:

Ami Vitale is a renowned National Geographic photographer and documentarian with a deep commitment to wildlife conservation and environmental education. Her work, spanning over a hundred countries, includes spending a decade as a conflict photographer in places like Kosovo, Gaza, and Kashmir.

She joined Tyler to discuss why we should stay scary to pandas, whether we should bring back extinct species, the success of Kenyan wildlife management, the mental cost of a decade photographing war, what she thinks of the transition from film to digital, the ethical issues raised by Afghan Girl, the future of National Geographic, the heuristic guiding of where she’ll travel next, what she looks for in a young photographer,  her next project, and more.

Here is one excerpt:

COWEN: As you probably know, there’s a long-standing and recurring set of debates between animal welfare advocates and environmentalists. The animal welfare advocates typically have less sympathy for the predators because they, in turn, kill other animals. The environmentalists are more likely to think we should, in some way, leave nature alone as much as possible. Where do you stand on that debate?

VITALE: It depends. It’s hard to make a general sweeping statement on this because in some cases, I think that we do have to get involved. Also, the fact is, it’s humans in most cases who have really impacted the environment, and we do need to get engaged and work to restore that balance. I really fall on both sides of this. I will say, I do think that is, in some cases, what differentiates us because, as human beings, we have to kill to survive. Maybe that is where this — I feel like every story I work on has a different answer. Really, I don’t know. It depends what the situation is. Should we bring animals back to landscapes where they have not existed for millions of years? I fall in the line of no. Maybe I’m taking this in a totally different direction, but it’s really complicated, and there’s not one easy answer.

And:

COWEN: As you know, there are now social networks everywhere, for quite a while. Images everywhere, even before Midjourney. There are so many images that people are looking at. How does that change how you compose or think about photos?

VITALE: Well, it doesn’t at all. My job is to tell stories with images, and not just with images. My job as a storyteller — that has not changed. Nothing has changed in the sense of, we need more great storytellers, visual storytellers. With all of those social media, I think people are bored with just beautiful images. Or sometimes it feels like advertising, and it doesn’t captivate me.

I look for a story and image, and I am just going to continue doing what I do because I think people are hungry for it. They want to know who is really going deep on stories and who they can trust. I think that that has never gone away, and it will never go away.

I am very happy to have guests who do things that not everyone else’s guests do.

Vaccine Induced Social Amnesia

by  Alex Tabarrok in
Source: Clinique CME

NYT: In 2022, there were 941 reported cases of measles in the World Health Organization’s European region. Over just the first 10 months of last year, according to an alarming bulletin the W.H.O. issued in mid-December, there were more than 30,000.

This is the kind of spike — a 3,000 percent increase — that looks implausible in headlines….But as the year drew to a close, the European measles outbreak kept growing. Through December, case numbers in the region eventually reached over 42,000, and although the largest outbreaks were in countries most Americans regard as pretty remote (Kazakhstan, Kyrgyzstan and Russia), there is also a vicious surge in Britain, which may look plausibly to us as the canary in a coal mine. There, in just one of England’s nine regions, the West Midlands, 260 cases have been confirmed and dozens more suspected, in a country which, as a whole, recorded just two cases as recently as 2021.

As David Wallace-Wells explains, vaccination rates are still 90%+ and down only slightly but measles is so infectious that even amid an otherwise well-vaccinated population, it can sometimes find pockets of low vaccination populations and spread like wildfire.

Measles is an especially nasty infection because it can induce “immunological amnesia“, thereby making individuals more susceptible to pathogens that they previously were able to resist.

Ironically, just as measles can induce immunological amnesia, vaccines can induce social amnesia about the severity of diseases, thereby making society more susceptible to pathogens that they previously were able to resist.

My new podcast with Dwarkesh Patel

by  Tyler Cowen in

We discussed how the insights of Hayek, Keynes, Smith, and other great economists help us make sense of AI, growth, risk, human nature, anarchy, central planning, and much more.

Dwarkesh is one of the very best interviewers around, here are the links.  If Twitter is blocked to you, here is the transcript, here is Spotify, among others.  Here is the most salacious part of the exchange, highly atypical of course:

Dwarkesh Patel 00:17:16

If Keynes were alive today, what are the odds that he’s in a polycule in Berkeley, writing the best written LessWrong post you’ve ever seen?

Tyler Cowen 00:17:24

I’m not sure what the counterfactual means. Keynes is so British. Maybe he’s an effective altruist at Cambridge. Given how he seemed to have run his sex life, I don’t think he needed a polycule. A polycule is almost a Williamsonian device to economize on transaction costs. But Keynes, according to his own notes, seems to have done things on a very casual basis.

And on another topic:

Dwarkesh Patel 00:36:44

We’re talking, I guess, about like GPT five level models. When you think in your mind about like, okay, this is GPT five. What happens with GPT six, GPT seven. Do you see it? Do you still think in the frame of having a bunch of RAs, or does it seem like a different sort of thing at some point?

Tyler Cowen 00:36:59

I’m not sure what those numbers going up mean, what a GPT seven would look like, or how much smarter it could get. I think people make too many assumptions there. It could be the real advantages are integrating it into workflows by things that are not better GPTs at all. And once you get to GPT, say, 5.5, I’m not sure you can just turn up the dial on smarts and have it, like, integrate general relativity and quantum mechanics.

Dwarkesh Patel 00:37:26

Why not?

Tyler Cowen 00:37:27

I don’t think that’s how intelligence works. And this is a Hayekian point. And some of these problems, there just may be no answer. Like, maybe the universe isn’t that legible, and if it’s not that legible, the GPT eleven doesn’t really make sense as a creature or whatever.

Dwarkesh Patel 00:37:44

Isn’t there a Hayekian argument to be made that, listen, you can have billions of copies of these things? Imagine the sort of decentralized order that could result, the amount of decentralized tacit knowledge that billions of copies talking to each other could have. That in and of itself, is an argument to be made about the whole thing as an emergent order will be much more powerful than we were anticipating.

Tyler Cowen 00:38:04

Well, I think it will be highly productive. What “tacit knowledge” means with AIs, I don’t think we understand yet. Is it by definition all non-tacit? Or does the fact that how GPT-4 works is not legible to us or even its creators so much? Does that mean it’s possessing of tacit knowledge, or is it not knowledge? None of those categories are well thought out, in my opinion. So we need to restructure our whole discourse about tacit knowledge in some new, different way. But I agree, these networks of AIs, even before, like, GPT-11, they’re going to be super productive, but they’re still going to face bottlenecks, right? And I don’t know how good they’ll be at, say, overcoming the behavioral bottlenecks of actual human beings, the bottlenecks of the law and regulation. And we’re going to have more regulation as we have more AIs.

You will note I corrected the AI transcriber on some minor matters.  In any case, self-recommending, and here is the YouTube embed: