Category: Science

A reminder (for academics)

by  Tyler Cowen in

Yes, there are skills AIs haven’t mastered. But if your skill still appears to be the exclusive province of humans, that might mean the major AI companies do not yet consider it very important to master right away. Eventually it will rise to the top of the list.

Here is more from my Free Press essay on AI.  If not for the copied passage, it seems no one was noticing this book review? (NYT, read the emendation)

Sentences to ponder

by  Tyler Cowen in

This matters for the AI question, and the book leaves it unfinished. If the breakthroughs of the past required social conditions, not just cognitive capacity, then what does it mean when the next breakthroughs are produced by systems that have no social conditions at all? A neural net does not need a university chair or financial independence from the church. It does not need to reorganize its commitments. It does not, in any recognizable sense, have commitments. The machine that replaces the marginalist is not a better marginalist. It is a different kind of thing entirely.

That is from Jônadas Techio, presumably with LLMs, this review of The Marginal Revolution is interesting throughout.  And this:

Maybe the book demonstrates only that Cowen personally remains good at something the field no longer needs.

Scott Sumner on *The Marginal Revolution*

by  Tyler Cowen in

My favorite part of Tyler’s book is where he asks a very good but non-obvious question: Why did it take so long for economics as a field to develop a coherent model or framework of analysis? Much of the book discusses how three economists simultaneously developed marginal analysis, with a focus on the work of Stanley Jevons. Here I’ll briefly provide the intuition of marginal analysis and then explain why economics is both extremely easy but also quite difficult…

Tyler does a great job explaining why Jevon’s model of marginal analysis (which underlies most of modern microeconomics) is elementary on one level, but also something that wasn’t discovered until the 1860s because it was not at all obvious. Here’s how he concludes Chapter 3:

[This is TC now] By studying the slow intellectual development of economics, and contrasting it with other fields of study, we can learn the following:

1. Some insights are very hard to grasp, even if they are apparently simple once they are understood. People need to “see around corners” in the right way to understand these insights and incorporate them into their world views.

2. Economics is one of those fields, and that is why it took intuitive economic reasoning so long to evolve, marginalism included. Those of us who are educators, or who spend time talking to policymakers, should take this point very seriously.

3. Even very, very smart people are likely unaware that these “see around the corner” insights are missing – did Euclid rue that he did not have access to proper supply and demand and tax incidence theory? Probably not.

4. Economics is not the only such field that is hard to grasp, some other examples being segments of botany, geology, and evolutionary biology.

5. Scientific revolutions come about when many complementary pieces are in place, such as financial support, intellectual independence, and networks of like-minded others to talk with.

Those conditions help people to understand that “seeing around those corners” can bring both high social and professional returns.

Are there major conceptual corners that today still no one can see around? If so, how might we discover what they are? And why are we not working harder on this? Or are we?

Here is the rest of Scott’s commentary.  Here is the online book.

Henry Oliver calls it a Swiftian ending

by  Tyler Cowen in

To The Marginal Revolution: Rise and Decline, and the Pending AI Revolution, here is the very close of the book:

There is however a slightly scarier version of this story yet. Maybe our intuitions about the world, including the economic world, were never so strong in the first place. Maybe we put so much value on “intuitive” results, in 20th century microeconomics, as a kind of cope and also security blanket, to make up for this deficiency. But our intuitions, even assuming them to be largely correct, always were just a small corner of understanding, swimming in a larger froth of epistemic chaos. And now the illusion has been stripped bare, and the true complexities of economic reasoning are being revealed.

As Arnold Kling would say, “Have a nice day.”

Can I say again “Have a nice day”?

What is economics these days?

by  Tyler Cowen in

From The Marginal Revolution: Rise and Decline, and the Pending AI Revolution:

The day before drafting this paragraph, I blogged a paper on confidence gaps between men and women. It was a paper written by economists, published in the prestigious American Economic Review, the profession’s number one journal. Is this actually sociology, or personality or social psychology, or part of some gender studies field? No one in the economics profession cares to discuss that anymore. It is not that there is a dogmatic attachment to what used to be called “economic imperialism,” rather the view is that if the paper is good enough … it is good enough to publish. I also recently read a paper on using cell phone data to estimate how many people actually were attending church. Freakonomics guru Steve Levitt wrote and published well-known papers on the choice of baby names and corruption in Sumo wrestling116See Exley and Nielsen (2024), and on cell phones see Pope (2024)..

The dirty little secret is that what distinguishes economics as a field, right now, is a mix of higher standards, harder work, better math, and higher IQs. That is the real (dare I say marginal?) contribution of “empirical economics today,” not marginalism per se, though of course contemporary models typically are consistent with marginalist reasoning…

One modest sign of all these changes is how many advisors, when speaking to individuals considering economics graduate school, recommend math or even computer science as a possible background undergraduate major. While most are still undergraduate economics majors, if only because that is where their interest in economics came from, no one seems to mind if they are not. These days, a background in mathematics or computer science is at least as useful for the graduate work to come. Once you get to graduate school, you will have to learn plenty of math and programming anyway, so why not start off in those fields? The prevailing attitude is that the economics you can figure out along the way, or for some topics you may not need to know much of it at all. How complicated are all those economic principles anyway? General skills of apprenticeship and plain ol’ hard work are growing in importance too, as top graduate programs increasingly want their incoming students to have done a “predoc” with an accomplished researcher somewhere along the way.

That is from the chapter on the future of economics in a world with advanced AI.

Addendum: On The Marginal Revolution book, I would most of all like to thank Jeff Holmes for the great job he did on the project, all of the actual work (other than the writing) is from him.  He is also producer of CWT, I owe much to him!

*The Marginal Revolution: Rise and Decline, and the Pending AI Revolution*

by  Tyler Cowen in

I am offering a new piece of work — I do not quite call it a book — online and free.  It has four chapters, is about 40,000 words, is fully written by me (not a word from the AIs), and it is attached to an AI with a dual page display, in this case Claude.  Think of it as a non-fiction novella of sorts, you can access it here.  You can read it on the screen, turn it into a pdf (and upload into your own AI), send it to your Kindle, or discuss it with Claude.

Here is the Table of Contents:

1. What Is Marginalism?

2. William Stanley Jevons, Builder and Destroyer of Marginalism

3. Why Did It Take So Long for the Science of Economics to Develop?

4. Why Marginalism Will Dwindle, and What Will Replace It?

Here are the first few paragraphs of the work:

How is it that ideas, and human capabilities, become lost? And how is that new insights come to pass? If eventually the insight seems obvious, why didn’t we see it before? Or maybe we did see it before, but didn’t really know we were on to something important? Why do new insights arrive suddenly, in a kind of flood? How do new worldviews replace older ones?

And what does all of that have to do with the future of science, the future of research, and the future of economics in particular? Especially when we try to understand how the ongoing artificial intelligence revolution is going to reshape human knowledge, and the all-important question of what economists should do.

Those are the motivating questions behind this work, but I will address them in what is initially an indirect fashion. I will start by considering a case study, namely the most important revolution in economics, the Marginal Revolution (to be defined shortly). The Marginal Revolution made modern economics possible. What was the Marginal Revolution? How did it start? Why did it take so very long to come to fruition? From those investigations we will get a sense of how economic ideas, and sometimes ideas more generally, develop. And that in turn will help us see where the science, art, and practice of economics is headed today.

Recommended!  I will be covering it more soon.

The rise of China as a global innovator in pharma (incentives matter)

by  Tyler Cowen in

This paper examines China’s transition from pharmaceutical “free rider” to global innovator over the last decade. In 2010, China accounted for less than 8% of global clinical trials; by 2020, it had surpassed the US in annual registered clinical trial volume. To study this transformation, we compile a comprehensive, synchronized database spanning the pharmaceutical drug development supply chain, covering scientific publications, clinical trials, drug development milestones for China, the U.S., and Europe, alongside drug sales and government policies over the same period. We provide strong evidence that China’s rise was primarily driven by the National Reimbursement Drug List (NRDL) reform, which dramatically expanded the effective market size for innovative drugs. We document a sharp rise in both the quantity (86% increase) and novelty of drug trials post reform, with growth concentrated in reform-exposed disease categories, first- or best-in-class drugs, and among domestic firms. A decomposition exercise reveals that the NRDL reform accounts for 43% of the growth in oncology trial activity, nearly doubling the combined contribution of upstream knowledge accumulation and talent flows (24%), while other government policies play a minor role. Finally, dynamic gains from induced innovation exceed the reform’s static gains in consumer access to innovative drugs by threefold, underscoring the importance of accounting for the reform’s long-run effects on innovation incentives in addition to near-term improvements in drug affordability.

That is from a new NBER working paper by Panle Jia Barwick, Hongyuan Xia & Tianli Xia.  That said, by one metric all ten of the most influential science papers of the last decade came from the United States.

Is AI currently helping economic research?

by  Tyler Cowen in

The third possibility, that AI helps to weed out mistakes, is trickier for the discipline. This stage could become even more important if journals do start to be hit by a wave of AI-generated slop — or, perhaps more likely, good papers with so many appendices and robustness checks that even the most dedicated referee is defeated. (The real “Dr Robust” does not have infinite energy.)

Eager to embrace the new technology, several of the top five economics journals are already experimenting with Refine, an impressive AI-powered reviewing tool that scours economics papers for errors. Ben Golub, one of its creators, shared that even with papers that had been through referees at top journals, Refine was picking up problems in at least a third of cases.

Here is more from Soumaya Keynes at the FT.

Is Germany actually that good at research?

by  Tyler Cowen in

Jannik Reigl writes:

Germany’s remaining research strengths are disproportionately concentrated in fields with limited commercial value. Consider climate science. German institutions co-lead with the United States. The Max Planck Institute in Hamburg, the UK Met Office Hadley Centre, ECMWF in Reading: these are world-class operations. Klaus Hasselmann won the 2021 Nobel Prize in Physics for climate modeling. Genuine excellence. But climate research doesn’t directly generate economic returns. The value lies in technology. And yes, while some of the most important assets of the near future are subsumed under “climate technologies”, they are essentially the product of other research fields. Batteries, solar cells, carbon capture, and grid technology are all technologies stemming from engineering and materials science. These require strength in chemistry, materials science, and engineering. The fields where Germany is losing ground.

The Max Planck Society is Germany’s highest-performing research body in the Nature Index. Its ranking fell from 4th place globally in 2021 to 11th in 2025, an “unusually large” decline according to Nature. Chemistry tells the starkest tale: Max Planck consistently ranked in the top 5 from 2015 to 2021, then dropped to 10th in 2022, and sits at 14th in 2025. Physical sciences show a similar pattern: Max Planck held 2nd place from 2015 to 2022 before falling to 4th, where it has remained.

German patents were cited 14 percent less than comparable US patents in the 1980s, and that this gap widened to 41 percent by the 2000s. This represented a steeper decline than that observed for both the United Kingdom and Japan. More recent studies do not use the same dataset or methodology, but they point in a similar direction.

One reason might be that the top research institutes disincentivise high-risk high-reward R&D by denying young talent scientific independence. In the United States, the system is built on the ‘flat’ Principal Investigator (PI) model. A talented scientist in their early 30s can secure a tenure-track Assistant Professorship, win their own NIH or NSF grants, and run a fully independent lab. They succeed or fail on their own scientific agenda.

Germany, by contrast, operates on a hierarchical ‘fiefdom’ model.

Here is the full essay, via Emma.

A Fly Has Been Uploaded

by  Alex Tabarrok in

In 2024, the entire neuronal diagram of the fruit-fly brain–some 140,000 neurons and 50 million connections–was mapped. Later research showed that the map could be used to predict behavior. Now, Eon Systems a firm with some of the scientists involved in the fruit-fly research and with the goal of uploading a human brain has announced that they uploaded the fruit fly brain to a digital environment.

The digital fly appears to behave in the digital environment in reasonably fly like ways–this is not a simulation, the fly’s “sensors” are being activated by the digital environment and the neurons are responding. Some more details here.

N.b. this work is not yet published.

Addendum 1: Of course Robin Hanson is an advisor to Eon Systems.

Addendum 2: In other news, human brain cells on a chip learned to play Doom. No word on whether they were conscious or not.

Science should be machine-readable

by  Tyler Cowen in

One of the leading tasks of our time:

We develop a machine-automated approach for extracting results from papers, which we assess via a comprehensive review of the entire eLife corpus. Our method facilitates a direct comparison of machine and peer review, and sheds light on key challenges that must be overcome in order to facilitate AI-assisted science. In particular, the results point the way towards a machine-readable framework for disseminating scientific information. We therefore argue that publication systems should optimize separately for the dissemination of data and results versus the conveying of novel ideas, and the former should be machine-readable.

Here is the paper by A. Sina Booeshagh, Laura Luebbert, and Lior Pachter.  Via John Tierney.