Tariff sentences to ponder
Trump’s tariff policy is an agenda for pushing American output down the value chain, away from advanced manufacturing and toward making cheaper simpler goods and supplying raw materials to China.
That is from Matt Yglesias.
Friday assorted links
1. An alternate model for training economists?
2. RIP Luis Fernando Verissimo. Borges and the Eternal Orangutans is a very fun book for me.
3. Chinese guy builds Cat World, Department of Why Not?
4. “Walking Tall?” (NYT). The husband did it.
5. AI progressed more quickly than superforecasters had expected. Green energy did not.
They solved for the Kansas City Chiefs enforcement equilibrium
We examine how financial pressure influences rule enforcement by leveraging a novel setting: NFL officiating. Unlike traditional regulatory environments, NFL officiating decisions are immediate, transparent, and publicly scrutinized, providing a unique empirical lens to test whether a worsening financial climate shapes enforcement behavior. Analyzing 13,136 defensive penalties from 2015 to 2023, we find that postseason officiating disproportionately favors the Mahomes-era Kansas City Chiefs, coinciding with the team’s emergence as a key driver of TV viewership/ratings and, thereby, revenue. Our study suggests that financial reliance on dominant entities can alter enforcement dynamics, a concern with implications far beyond sports governance.
That is from a new piece by Spencer Barnes, Ted Dischman, and Brandon Mendez. Via the excellent Kevin Lewis.
Sentences to ponder
By ordering the U.S. military to summarily kill a group of people aboard what he said was a drug-smuggling boat, President Trump used the military in a way that had no clear legal precedent or basis, according to specialists in the laws of war and executive power.
Mr. Trump is claiming the power to shift maritime counterdrug efforts from law enforcement rules to wartime rules. The police arrest criminal suspects for prosecution and cannot instead simply gun suspects down, except in rare circumstances where they pose an imminent threat to someone.
Here is more from the NYT.
“Existence is evidence of immortality”
From philosopher Michael Huemer:
Do persons continue to exist after the destruction of their bodies? Many believe so. This might occur either because we have immaterial souls that persist in another, non-physical realm; or because our bodies will be somehow reanimated after we die; or because we will live on in new bodies in the physical realm.1 I shall suggest herein that the third alternative, “reincarnation,” is surprisingly plausible. More specifically, I shall argue (i) that your present existence constitutes significant evidence that you will be reincarnated, and (ii) that if the history of the universe is infinite, then you will be reincarnated.
My argument is entirely secular and philosophical. The basic line of thought is something like this. The universe has an infinite future. Given unlimited time, every qualitative state that has ever occurred will occur again, infinitely many times. This includes the qualitative states that in fact brought about your current life. A sufficiently precise repetition of the right conditions will qualify as literally creating another incarnation of you. Some theories about the nature of persons rule this out; however, these theories also imply that, given an infinite past, your present existence is a probability-zero event. Hence, your present existence is evidence against such theories of persons. Given an infinite past, it is conclusive evidence.
Here is the rest of the paper. Via Nabeel. So you do not need to read this paper just right now?
Thursday assorted links
1. Milei having some problems right now (FT).
2. Caplan and Hendren back and forth on the value of Medicaid.
3. How to influence chatbots. Cialdini still applies.
4. Seeing Wizard of Oz at The Sphere (NYT).
5. Patrick Collison on George Berkeley as development economist.
6. Ross Douthat interviews Dan Wang, who by the way is now on the bestseller lists (NYT).
It would take more than one paper to establish these claims
Nonetheless these are interesting results, worthy of further examination:
The measurement of intelligence should identify and measure an individual’s subjective confidence that a response to a test question is correct. Existing measures do not do that, nor do they use extrinsic financial incentive for truthful responses. We rectify both issues, and show that each matters for the measurement of intelligence, particularly for women. Our results on gender and confidence in the face of risk have wider applications in terms of the measurement of “competitiveness” and financial literacy. Contrary to received literature, women are more intelligent than men, compete when they should in risky settings, and are more literate.
That is from the September JPE, by Glenn W. Harrison, Don Ross, and J. Todd Swarthout. Here are ungated versions of the paper. Here is Bryan Caplan on the limitations of any single paper.
Oct.9th I am speaking for the Pioneer Institute in Boston, on federalism
My very interesting Conversation with Seamus Murphy
Here is the audio, video, and transcript. Here is the episode summary:
Seamus Murphy is an Irish photographer and filmmaker who has spent decades documenting life in some of the world’s most challenging places—from Taliban-controlled Afghanistan to Nigeria’s Boko Haram territories. Having left recession-era Ireland in the 1980s to teach himself photography in American darkrooms, Murphy has become that rare artist who moves seamlessly between conflict zones and recording studios, creating books of Afghan women’s poetry while directing music videos that anticipated Brexit.
Tyler and Seamus discuss the optimistic case for Afghanistan, his biggest fear when visiting any conflict zone, how photography has shaped perceptions of Afghanistan, why Russia reminded him of pre-Celtic Tiger Ireland, how the Catholic Church’s influence collapsed so suddenly in Ireland, why he left Ireland in the 1980s, what shapes Americans impression of Ireland, living part-time in Kolkata and what the future holds for that “slightly dying” but culturally vibrant city, his near-death encounters with Boko Haram in Nigeria, the visual similarities between Michigan and Russia, working with PJ Harvey on Let England Shake and their travels to Kosovo and Afghanistan together, his upcoming film about an Afghan family he’s documented for thirty years, and more.
And an excerpt:
COWEN: Now you’re living in Kolkata mainly?
MURPHY: No. I’m living in London, some of the year in Kolkata.
COWEN: Why Kolkata?
MURPHY: My wife is Indian. She grew up in Delhi, Bombay, and Kolkata, but Kolkata was her favorite. They were the years that were her most fond of years. She’s got lots of friends from Kolkata. I love the city. She was saying that if I didn’t like the city, then we wouldn’t be spending as much time in Kolkata as we do, but I do love the city.
It’s got, in many ways, everything I would look for in a city. Kabul, in a way, was a bit like Kolkata when times were better. This is maybe a replacement for Kabul for me. Kolkata is extraordinary. It’s got that history. It’s got the buildings. Bengalis are fascinating. It’s got culture, fantastic food.
COWEN: The best streets in India, right?
MURPHY: Absolutely.
COWEN: It’s my daughter’s favorite city in India.
MURPHY: Really?
COWEN: Yes.
MURPHY: What does she like about it?
COWEN: There’s a kind of noir feel to it all.
MURPHY: Absolutely.
COWEN: It’s so compelling and so strong and just grabs you, and you feel it on every street, every block. It’s probably still the most intellectual Indian city with the best bookshops, a certain public intellectual life.
MURPHY: It’s widespread. It’s not just elite. It’s everyone. We went to a huge book fair. It’s like going to . . . I don’t know what it’s like going to, Kumbh Mela or something. It’s extraordinary.
There’s a huge tent right in the middle, and it’s for what they call little magazines. Little magazines are these very small publications run by one or two people. They’ll publish poetry. They’ll publish interesting stories. Sadly, I don’t speak Bengali because I’d love to be reading this stuff. There are hundreds of these things. They survive, and people buy them. It’s not just the elite. It’s extraordinary in that way.
COWEN: Is there any significant hardship associated with living there, say a few months of the year?
MURPHY: For us, no. There’s a lot of hardship —
COWEN: No pollution?
MURPHY: Yes. The biggest pollution for me is the noise, the noise pollution.
Interesting throughout.
Pathbreaking paper on AI simulations of human behavior
By Benjamin Manning and John Horton:
Useful social science theories predict behavior across settings. However, applying a theory to make predictions in new settings is challenging: rarely can it be done without ad hoc modifications to account for setting-specific factors. We argue that AI agents put in simulations of those novel settings offer an alternative for applying theory, requiring minimal or no modifications. We present an approach for building such “general” agents that use theory-grounded natural language instructions, existing empirical data, and knowledge acquired by the underlying AI during training. To demonstrate the approach in settings where no data from that data-generating process exists—as is often the case in applied prediction problems—we design a highly heterogeneous population of 883,320 novel games. AI agents are constructed using human data from a small set of conceptually related, but structurally distinct “seed” games. In preregistered experiments, on average, agents predict human play better than (i) game-theoretic equilibria and (ii) out-of-the-box agents in a random sample of 1,500 games from the population. For a small set of separate novel games, these simulations predict responses from a new sample of human subjects better even than the most plausibly relevant published human data.
Here is a good Twitter thread. A broader AI lesson here is that you often have to put in a lot of work to get the best from your LLMs. And these results ought to have implications for the methods of psychology and some of the other social sciences as well.
What should I ask Jonny Steinberg?
Yes I will be doing a Conversation with him. From Wikipedia:
Steinberg was born and raised in the northern suburbs of Johannesburg, South Africa. He was educated at Wits University in Johannesburg, and at the University of Oxford, where he was a Rhodes Scholar and earned a doctorate in political theory. He taught for nine years at Oxford, where he was Professor of African Studies. He currently teaches at Yale University‘s Council on African Studies.
Three of Steinberg’s books – Midlands (2002), about the murder of a white South African farmer, The Number (2004), a biography of a prison gangster, and Winnie & Nelson (2023) – won South Africa’s premier non-fiction prize, the Sunday Times CNA Literary Awards making him the first writer to win it three times.
I am a special fan of Winnie & Nelson, which I consider to be one of the best books of the last ten years. He is currently working on a biography of Cecil Rhodes. So what should I ask him?
Wednesday assorted links
1. Where are all the trillion dollar biotech companies?
2. Ne Zha 2 is bombing in America.
3. More John Cochrane on the Fed.
4. Some new problems with the minimum wage.
5. “Complaining about feminization does not a man make”.
6. Did the rise of OpenAI stop Google from being broken up?
7. Geoffrey Hinton moves away from the Doomer position.
8. Car sketch with labels, noting GPT is probably not the best model for this task, at least not the sketch part of it.
The Simple Mathematics of Chinese Innovation
The NYTimes has a good data-driven piece on How China Went From Clean Energy Copycat to Global Innovator, the upshot of which is that the old view of China as simply copying (“stealing” in some eyes) no longer describes reality. In some fields, including solar, batteries and hydrogen, China is now the leading innovator as measured by high-quality patents and scientific citations.
None of this should surprise anyone. China employs roughly 2.6 million full-time equivalent (FTE) researchers versus about 1.7 million in the United States. On a per-capita basis the U.S. is ahead—about 4,500 researchers per million people versus China’s 1,700—but population scale tips the balance. China simply has more researchers in absolute terms. If you frame it in terms of rare cognitive talent, as in my post on The Extreme Shortage of High IQ Workers—the arithmetic is even more striking: 1-in-1,000 workers (≈IQ 145) ~170,000 in the U.S. labor force and ~770,000 in China. Scale matters.
In the 20th century the world’s most populous countries were poor but that was neither the case historically nor will it be true in the 21st century. The standard of living in China remains well below that in the United States and China may never catch U.S. GDP per capita, but quantity is a quality of its own. More people means more ideas.
To be clear, the rise of China and India as scientific superpowers is not per se a threat. Whiners complain about US pharmaceutical R&D “subsidizing” the world. Well, Chinese pharmaceutical innovation is now saving American lives. Terrific. Ideas don’t stop at borders, and their spread raises living standards everywhere. It would be wonderful if an American cured cancer. It would be 99% as wonderful if a Chinese scientist did. What matters is that when more scientists attack the problem, the odds of a cure rise so we should look favorably on a world with more scientists. That is progress.
The danger is not China’s rise but America’s mindset. Treat science as zero-sum and every Chinese patent looks like a loss. But ideas are nonrival: a Chinese breakthrough doesn’t make Americans poorer, it makes the world richer. A multi-polar scientific world means faster growth, greater wealth, and accelerating technology—even if America wins a smaller share of the Nobels.
Read it and weep
A global bond sell-off deepened on Wednesday, driving the yield on the 30-year US Treasury to 5 per cent for the first time since July, as investors’ fears over rising debt piles and stubbornly high inflation dominated trading.
Longer-dated bonds bore the brunt of the selling, with the yield on the 30-year Treasury up 0.03 percentage points at 5 per cent and Japan’s 30-year bond yield hitting a record high of 3.29 per cent.
In the UK, long-term borrowing costs climbed further after reaching their highest level since 1998 on Tuesday. The 30-year gilt yield rose 0.06 percentage points to 5.75 per cent.
Here is more from the FT.
Where are the trillion dollar biotech companies?
In today’s market, even companies with multiple approved drugs can trade below their cash balances. Given this, it is truly perplexing to see AI-biotechs raise mega-rounds at the preclinical stage – Xaira with a billion-dollar seed, Isomorphic with $600M, EvolutionaryScale with $142M, and InceptiveBio with $100M, to name a few. The scale and stage of these rounds reflect some investors’ belief that AI-biology pairing can bend the drug discovery economics I described before.
To me, the question of whether AI will be helpful in drug discovery is not as interesting as the question of whether AI can turn a 2-billion-dollar drug development into a 200-million-dollar drug development, or whether 10 years to approve a drug can become 5 years to approve a drug. AI will be used to assist drug discovery in the same way software has been used for decades, and, given enough time, we know it will change everything [4]. But is “enough time” 3 years or half a century?
One number that is worth appreciating is that 80% of all costs associated with bringing a drug to market come from clinical-stage work. That is, if we ever get to molecules designed and preclinically validated in under 1 year, we’ll be impacting only a small fraction of what makes drug discovery hard. This productivity gain cap is especially striking given that the majority of the data we can use to train models today is still preclinical, and, in most cases, even pre-animal. A perfect model predictive of in vitro tox saves you time on running in vitro tox (which is less than a few weeks anyway!), doesn’t bridge the in vitro to animal translation gap, and especially does not affect the dreaded animal-to-human jump. As such, perfecting predictive validity for preclinical work is the current best-case scenario for the industry. Though we don’t have a sufficient amount and types of data to solve even that.
Here is the full and very interesting essay, from the excellent Lada Nuzhna.