The Decline in the Transmission of Scientific Ideas

Enrico Berkes and Ruben Gaetani, The Decline in the Transmission of Scientific Ideas, NBER, July 2026.

Abstract: We document that the diffusion of new scientific ideas beyond their field of origin has declined substantially over the past four decades. This contraction is closely linked to increasing spe- cialization in scientific language: research that employs more technical terminology tends to be adopted less broadly. We develop a theory of scientific discovery in which the diffusion of new ideas depends on the degree to which potential adopters can understand and process them. When introducing their discoveries, scientists face a tradeoff between technical com- munication targeted at their immediate peers and more accessible language meant to reach broader audiences. As knowledge accumulates and research at the frontier builds on deeper layers of prior work, this tradeoff increasingly favors specialized language, limiting diffusion. Policy interventions that align scientists’ incentives can broaden adoption and increase the social value of scientific research.

H/t Tyler Cowen.

Joel Mokyr on China, India, and Europe; clans vs. corporations; the idea of progress.

A Conversation with Tyler: Joel Mokyr on Clans, Corporations, and a Culture of Growth (Ep. 282)

Joel Mokyr co-won the 2025 economics Nobel for exploring the question that traces back to the beginning of economics: how did sustained economic growth suddenly become normal? For nearly all of human history, cleverness didn’t compound. What changed, according to Mokyr, was twofold: first, you need to know why something works, so that one advance can seed the next; second, you need a culture willing to tolerate the disruption. His new book contrasts Europe with China, showing how Europeans learned to cooperate with people they weren’t related to, in guilds, monasteries, cities, and universities, while China organized itself around the extended clan. One path led to internal stability and peace; the other, more restless and outward-looking, was the one that decided the world could always be made better.

Tyler and Joel discuss European corporations vs. Chinese clans, why the Catholic Church became obsessed with cousin-marriage, how persistent cultural trends really are, why Chinese cities became so populous relative to Europe, why it took so long for European living standards to surpass China’s, why sinified invaders kept getting swallowed by the dynasties they conquered, how geography kept Europe fragmented and China unified, where India fits into the story, why the Romans never made spectacles, why British soldiers stood two inches taller than the French, what powered the sudden rise of 19th-century German science, how disruptive winning a Nobel is, and much more.

Clans vs. Corporations

COWEN: Start by telling us, in the book, your thesis about European corporations versus Chinese clans and the importance of that difference. How would you explain it?

MOKYR: Well, the difference is, basically, the kind of organizations that produce what we call local public goods, so things like food relief and education, religious services, things like that, I would think that if you look at the world, say, around 800, at the time of Charlemagne, the difference between Europe and China isn’t very large. At some point, during the Middle Ages, you can see this divergence getting started. What’s happening is that, in Europe, there is more and more of a decline in the extended family or the extended kinship group, we call it clan, and instead, people get together and cooperate with other people to whom they are not related and with whom they do not share an ancestor.

Whereas in China, it moves exactly in the other direction. In China, you get more and more people getting organized by their extended family. The reasons for that are fairly complex. In Europe, it’s particularly the Catholic Church that played a major role here. This was argued quite a while ago by a guy, an anthropologist called Jack Goody, but your own colleague Jonathan Schulz wrote, what I think is one of the best papers on the subject, who pointed this out in great length and actually provided a fair amount of systematic evidence for this.

In China, there is no Catholic Church. The imperial bureaucracy is more and more in cahoots with local clans to whom they actually outsource a fair amount of the things that they were supposed to do. As you move on out of this period of the Song dynasty into later dynasties, you see this thing growing. The problem in Europe is that the nuclear family, which became the fundamental building block of society, is too small to provide local public goods. You need to cooperate with others. What emerges in Europe, and quite spontaneously, is a bunch of things that provide these local public goods that you just don’t see in China.

For instance, we have something called universities. We have monasteries. We have autonomous cities. All of those things are what we call corporations. What it is, is people who are not related, but what they share is not an ancestor but an objective. Guilds have one kind of objective, universities have another one, and so on and so forth. That divergence in social organization turns out, in our view, to be one of the key components of the divergence between Europe and China.

COWEN: Do you take that change in policy from the Catholic Church as exogenous or that it is rooted in earlier features of Western society such as the ideology of Christianity itself or maybe earlier Roman times? What is causing what? What’s the most fundamental driver here in the West?

MOKYR: Well, there’s some debate about that. They don’t, of course, tell you. There are, I think, two components about this, and I would not know how to weight them. There are two things happening here. The first is that the Catholic Church really becomes quite obsessive about certain sins that they consider to be particularly egregious. One of those sins is incest. Every society in the world prohibits marriage between siblings, but other more remote relatives is more ambiguous. The Church becomes quite obsessive about this. At the end, there are places where they actually prohibit the marriage of fifth-degree cousins. Now, how anybody in the Middle Ages would know who is fifth-degree cousin is, is unclear. I don’t know who my fifth-degree cousins are. Maybe you do.

The other thing, which is also sort of Goody’s argument, and I think there’s a great deal of truth to that, is that the Church wants to weaken any kind of organizations that compete with it for power and control in the local communities. They also have figured out that if you organize society by nuclear families, then a certain proportion of people die without heirs. If they die interstate without heirs, that in many cases, the property that these people own reverted to the Church. Pure naked greed by the Church, which was not unknown in the Middle Ages, I think, was a driver here. Goody convinced me that that actually is a substantial factor.

There are other arguments that have been made in this context, but I think these are the two that are most striking. The other question is, why is it that the clan in China is so convenient for the imperial bureaucracy to rely on? Both of those things happen in parallel. I would think, maybe, if I may allow one observation, in both cases, this is history as historical outcomes, as the unintended and unanticipated consequence of very different actions. There’s no question that the Church never, I think, foresaw the emergence of corporations in Europe, nor do I think that the Chinese imperial service ever seriously considered the possibility how this would change life in China. That’s what happens. You try to follow one objective and then something very different emerges over time. That’s what history is all about.

European Growth

COWEN: Why does it take so long for the wealthiest parts of Western Europe to surpass Chinese living standards? Say that’s happened by 1700 or 1720, that’s many centuries after this medieval divergence. If it takes so many centuries, is the medieval divergence really the relevant factor? Why is it such a slow process?

MOKYR: Yes, I think it is. I think it’s a main factor. I think the idea of looking at standard of living, one thing, I’m very skeptical about how standards of living are actually measured. I know that this is what Pomeranz and other people have, and Jack Goldstone and other people have argued that the living standards in China were comparable to the West as late as 1750. I’m not 100 percent sure that that is true. Certainly, for my money, what really defines the divergence is that, technologically, the gap between the two countries starts to become visible at the time of the Renaissance, in terms of a whole bunch of things that you see growing in Europe and stagnant in China.

Now, keep in mind, of course, that part of the European growth is due to the fact that they borrowed ideas from China. Then the Industrial Revolution consists, to some extent, of imports institution by Europeans trying to mimic the goods that they were importing from China—not just from China, from India as well. Pottery is a good example. One of the things they really wanted from China was Chinaware. That’s why it’s called Chinaware. It took them a while to be able to match the Chinese capability in the ceramic industry, but they do so eventually. Then they stop importing this stuff from China. The same is true for, say, cotton and other products that we’re getting from the East.

European living standards, I think, should be measured, in part, by the fact that when the Europeans start their voyages across the globe in the late 15th and early 16th century, they are able to bring in a whole bunch of new crops and new techniques from other areas which they merely adopt. You’ll see Europeans very soon growing tobacco and potatoes and corn and other things like that. They are the agents of global change. Not only that they change their own diets, they change the Chinese diets because the Europeans bring from the New World things like peanuts and sweet potatoes and things like that. They change the Chinese diets, but the Chinese themselves are not agents here.

They are accepting the stuff that the Europeans did to some extent, and they’re rejecting others, but it’s the Europeans who are the agents of change here. They are the entrepreneurs. They are the people who bring about the changes, Tyler. My sense is that typifies the difference between Europeans and the Chinese. Europeans are more aggressive. They are more outward-looking. In the end, what you see by the 1830s and 1840s, you see that the technological gap is huge, in some ways much larger than the living standards gap. Even in the 19th century, in terms of food, the Chinese were capable of producing enough food. The number of famines in China is probably not a lot worse than in Europe.

When you see what happens during the First Opium War, one English ship is blowing all of this sort of mighty empire to pieces, and the Chinese have to accept this terribly humiliating peace, you can sort of see how the technological gap has grown between the two. For me, that is much more telling than the living standards. The other thing that I should like to point out is that, when you look at Europe in the 16th and 17th century, you can see that the capability of expanding the set of useful knowledge, including science, is just growing very rapidly. Whether there is a scientific revolution or not is a debate that I want to get into.

Certainly, by 1700, Europe is on the verge of really changing our understanding of how creation works. That’s not just Newton and Galileo. There’s a whole body of work that is emerging. There’s really nothing parallel like that in China. China is a very sophisticated society in many ways. The literacy rates are high. They have a well-funded and well-organized system of education, but they don’t really continue their earlier forays into science and into new technology.

Somebody actually went out and looked at Joseph Needham’s many volumes on Chinese technology and science, or Science and Civilisation [in China], as he called it, and he discovered something—which I guess we all knew, but they put numbers on it—almost nothing that Needham pointed out as an innovation happens after 1400. There’s complete stagnation setting in and some of the things that they knew how to make in earlier times, like the sophisticated clocks that they built in the 11th century, they disappear. For me, that’s more telling than how many calories of carbohydrates were consumed on average, if we could ever calculate that correctly.

How will I handle The God Test? [GT-1]

As soon as I learned that Robert Wright was coming out with a book on AI I put it on my to-read list. Why? Two reasons:

1.) I’ve been following his work since his days writing for The New Republic; I’ve read Nonzero, his book about cultural evolution, which interests me a great deal; and I’ve been following him online since the early days of Blogging Heads. That’s where I first learned about Elizer “Dr. Doom” Yudkowsky.

2.) I’m working on my own book about AI – here’s an outline, right around the corner – and wanted to scope out the competition. I’m working on my proposal and one part of the proposal is an evaluation of the market for the book you propose. I knew about Wright’s interest in Teilhard de Chardin and knew he’d be including him in his book. There’s where he gets the “cosmic” in his subtitle: The God Test: Artificial Intelligence and Our Coming Cosmic Reckoning. I’ve got my own cosmic angle, albeit a somewhat different one. I call it the Fourth Arena (my answer to that pesky tech Singularity). Similarly, I’ve got a long-standing interest in cultural evolution, long predating Nonzero, and it’s grounded in an orientation toward complexity in evolution, “A Note on Why Natural Selection Leads to Complexity.”

You can see, then, how that puts me in something of a bind. How do I review the competition? Now, as a practical matter, perhaps he won’t really be competition. I mean, if I’ve not yet finished my proposal, how soon can my book possibly come out? At the rate AI is moving, it’ll be a whole new market by the time my book drops. Heck, if the Doomsters are right, we might have been turned to paperclip fodder by that time. I doubt it, but who knows?

* * * * *

Anyhow I’ve already started posting about the book, Robert Wright discusses his new book, The God Test, with Paul Bloom [Awe? Bob, Awe!?]. That’s based on a conversation he has with Paul Bloom about the book. One of the topics they cover is awe; Wright things we should feel awe on the face of AI. Me, I feel no awe in the face of AI; but I sure felt it when I visited Kennedy Space Center in the mid-1990s and saw those rockets and stood on the ground from which men traveled to the moon. Anyhow, if you want to read more, there it is. I may or may not come back to awe in the course of this series of blog posts.

* * * * *

So, why should you read The God Test? For I’m pretty sure that, if you’re interested in AI and how it affects us, you should read this book. I know that and I’m only half way through. Heck, I believed that even before I started reading it.

I know that because I know that Wright is going to talk about the need to slow things down, with which I am sympathetic, and about the need for international cooperation in dealing with AI, with which I agree. I know these things because I’ve heard in talk about them on his Nonzero podcast. I’m only halfway through the book and haven’t gotten to that part yet, but I can see it coming. I figure it’ll be worthwhile.

* * * * *

Much of what I have read has Wright giving us his version of how LLMs work. I’m not sure what I think about that. I certainly have some thoughts about that, after all I’ve spent some time doing quasi-systematic research on its behavior and I’m involved with a research project with Ramesh Viswanathan. And I certainly wouldn’t explain it the way Wright does. But in a way I’m wondering why try to explain it at all? I don’t intend to do that in that book I’m working on, Play: How to Stay Human in the AI Revolution. Don’t think it’s necessary, not for what I want to do – which is, I admit, a bit strange.

Why does Wright?

The thing is, the people who created LLMs don’t know how they work, so how do you approach the problem of explaining it to a general audience? It’s one thing for a science journalist to come to come up with metaphors and analogies to explain a technical subject when there are experts who actually know what’s going on. How do you come up with metaphors and analogies for something no one really understands?

Obviously Wright is doing it because this is important stuff, he wants to understand it, and he wants us to understand it. So he’s got to try. But it the circumstances it’s kinda’ hopeless, no?

So that’s one thing I want to deal with. Maybe the way to approach it is to see how he goes about it. We’ll see.

* * * * *

There’s one last thing. When it all falls apart, which it will, in one way or another. Maybe it’ll creep up on us, maybe it’ll come down, WAM! But fall apart it will. When that happens, what’s in The God Test that will help us cope?

* * * * *

Note: Posts in this series, or closely adjacent, will be tagged God_Test.

Notes on the Collective Valuation of “Thick” Objects: Financial Assets, Movies, and Novels

New working paper. Title above, links, abstract, TOC, and introduction below.

Links:

Academia.edu: https://www.academia.edu/169390494/Notes_on_the_Collective_Valuation_of_Thick_Objects_Financial_Assets_Movies_and_Novels
ResearchGate: https://www.researchgate.net/publication/408219138_Notes_on_the_Collective_Valuation_of_Thick_Objects_Financial_Assets_Movies_and_Novels

Abstract: Machine learning is creating a methodological bridge between disciplines that previously seemed far apart, especially economics and literary criticism. The bridge is the analysis of how populations deal with “thick objects.” A thick object is not exhausted by a few visible traits. It gathers interpretation, expectation, memory, value, narrative, and social response. A toaster is usually a thin object. A firm that manufactures toasters is thick: it has assets, debt, brands, patents, management, supply chains, analyst coverage, market expectations, and future promises. Scott Galloway’s remark that stocks are like brands — part promise, part performance — links stock, movies and novels. Each is a thick object moving through a field of collective judgment. Its value reflects both measurable performance and imagined future promise. They are thus as neighboring cases in a general problem: how populations perceive, classify, value, and transform thick objects. Machine learning constructs object-spaces from the traces minds leave behind. The task now is to learn how to interpret those spaces without mistaking the model for the world.

High-dimensional asset-pricing models start with many stock characteristics — price, returns, volume, profitability, leverage, liquidity, analyst revisions, momentum, volatility, investment, and so on. These characteristics are traces of firm activity, accounting conventions, analyst judgment, and trader behavior. New models then generate hundreds of thousands of nonlinear transformations from those characteristics in order to approximate the market’s pricing kernel, the structure through which future payoffs are priced under uncertainty. The individual factors are analytic objects approximating the valuation geometry produced by collective market activity.

That sounds strange in economics, but it is familiar from Matthew Jockers’ work on nineteenth-century Anglophone novels. Jockers created a high-dimensional design space from thousands of novels, using stylistic features and topic models. His topics are not literal thoughts in anyone’s mind. They are model-derived approximations to recurrent regions of culturally circulating thought. Yet the model revealed historical direction: novels arranged by similarity formed a temporal diagonal, a computationally disciplined proxy for population-level cultural cognition.

Arthur De Vany’s model of Hollywood adds the dynamic bridge. Movies are thick expressive-market objects. Their success cannot be predicted simply from stars, director, budget, genre, or advertising. Once released, they enter an audience field where word of mouth, imitation, and nonlinear cascades determine their fate. Most fail, some profit, a few become blockbusters. The dynamics are heavy-tailed, interactive, and collective.

Contents

Introduction: Using ChatGPT for focused intellectual exploration across disciplines 3
Thick Objects: Ground Shared by Economics and Cultural Analysis [Summary] 9
AIPT, Large Factor Models [First Session] 17
Hollywood Economics 23
Macroanalysis 27
The emerging triad 30
Direction over time 31
Doing a Jockers style analysis for financial assets 38
Thinking about thick objects 40
Stocks are like brands [Session Two] 42
Algorithmic and Causal models [Session Three] 52
Those empirical APT models [Session Four] 56
Decision space 63
A bridge between disparate disciplines 67

Introduction: Using ChatGPT for focused intellectual exploration across disciplines

This document serves two purposes. It presents a specific argument leading to the following provisional formulation:

High-dimensional models of novels, movies, and assets disclose the population-level geometry of collective interpretation around thick objects, turning literary criticism and economics into neighboring sciences of modeled valuation.

How I arrived at the speculation, however, is as important as the idea itself, perhaps more so. I did not arrive at that idea unaided. ChatGPT helped me. Those aren’t my words; they’re ChatGPT’s. I know a great deal about literary criticism and about movies, but not much about economics. I need ChatGPT to bridge the conceptual distance between the humanities, literary criticism, and the social sciences, economics.

Methodological curiosity

Fortunately the peculiar circumstances of my career have forced me to be interested in method and epistemology: How is it that we can come to know about the world and what methods can we use to arrive at that knowledge? When I entered Johns Hopkins as a freshman in 1965 the discipline of literary criticism was in a state of crisis, though I didn’t know that. How could I? I’d only just graduated high school and I still pretty much knowledge as it was handed to me.

That soon changed. The details of just how, when, and why don’t matter much at the moment. That it happened is sufficient for my present purposes. The upshot is that I became interested in Coleridge’s “Kubla Khan” in my senior year. I investigated the poem with standard interpretive methods augmented by avant garde structuralism and found patterns I could not explain. But they “smelled” of the nested loops I learned about in a course in computer programming.

That sent me to the English Department at SUNY Buffalo, which had the best experimental program in the nation. I found a fellow graduate student, Ralph Henry Reese, who pointed me around a corner and down the hall to David Hays in Linguistics. Hays had been a first generation researcher in machine translation at the RAND Corp. and, as such, was one of the founders of computational linguistics. While I wasn’t able to resolve my issues with “Kubla Khan” – they’re still hanging fire – I became hooked on cognitive science. Consequently my dissertation in the English Department was also a quasi-technical exercise in knowledge representation, the discipline within cognitive science and artificial intelligence about the representation of human knowledge in computable form.

Given that that is where I had arrived in the late 1970s it is perhaps not so strange that now, decades later, I find myself staring down some pretty formidable economics despite never having studied the subject. For the last 15 years, however, I have been reading the Marginal Revolution blog hosted by Tyler Cowen and Alex Tabarrok and I have been reading my way through Cowen’s recent monograph, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution (2026). Cowen’s theme in the fourth (and last) chapter is that the economics he was trained in, the economics which followed from the Marginal Revolution, is rapidly being eclipsed by a more determinedly empirical discipline based on machine learning.

Bombed by 360,000 factors

Here is Cowen’s premier example. It’s from something called Arbitrage Pricing Theory (APT) (pp. 99-100):

There is a recent working paper which is perhaps more striking yet, by Antoine Didisheim, Shikun (Barry) Ke, Bryan T. Kelly, and Semyon Malamud. They pick up from Arbitrage Pricing Theory (APT), a well-established idea from financial economics. APT typically looks for “factors” in the data which predict excess returns, and a traditional APT model might have found five or six such factors. Are “inflation” or perhaps “the term structure of interest rates” useful factors? Well, that can be debated, but if so, those results sound pretty intuitive. But those intuitions seem to be disappearing. In a paper by these authors, they apply machine learning methods to look for more factors. As we know, machine learning is very good at finding non-obvious relationships in the data. The largest model they built has 360,000 (!) factors, and it reduces pricing errors by 54.8 percent relative to the classic six-factor model from Fama and French. Bravo to the authors, but what kinds of intuitions do you think possibly can be supported by those 360,000 factors?

When I read that, it “looked like Greek to me,” as the cliché has it. But I took a deep breath and thought carefully, step by step and concluded that the assets in question are stocks. What you need to pay attention to is 1) the contrast between six factors and 360,000 factors, 2) the fact that one set of factors is intuitive while the other certainly is not, 3) but the unintelligible, unintuitive, collection of factors does a better job of pricing. That’s the new world toward which economics is moving. While the old intuitions are gasping for breath the new-fangled numbers are fit as a fiddle and ready for duty.

I thought some more and realized that what’s really going on is that people are evaluating those stocks, communicating with one another directly about them, and making decisions about buying and selling, thereby communicating indirectly with one another. That’s what those 360,000 factors are capturing, the actions of a dispersed community of analysts and traders. “Could this be roughly similar to the decisions movie-goers make about the movies they see based, not only on their preferences, but on information they get from reviews, and perhaps more importantly, from their friends?” “If so,” I conjectured, “then perhaps Cowen’s old colleague from Irvine, Arthur De Vany, can shed some light on the situation.” That is to say, can give me some intuitions that I can apply to the situation.

For De Vany had written a very interesting book, Hollywood Economics (2004), about the fate of movies once they have been released. Just as those intuitive “classical” models in economics aren’t as accurate as the new high-factor models, so you can’t predict the box-office performance of movies on such simple factors as the identities of the producer, screen writers, or stars in the movies. Now, De Vany didn’t produce a high-factor model that improved matters, he did something quite different (which is discussed below, pp. 23 ff.), but that’s secondary at the moment. The point is that we seem to have a gross similarity, the behavior of some object that interests a lot of people, a stock or a movie, cannot be reliably predicted using a simple model.

Meme stocks and novels

The similarity was reinforced when I heard a remark by Scott Galloway on the Pivot podcast: “Stocks are like brands and that is they’re part promise and part performance.” Consider the recent phenomenon of meme stocks, which Wikipedia glosses this way:

A meme stock is a stock that gains popularity among retail investors through social media. The popularity of meme stocks is generally based on internet memes shared among traders, on platforms such as Reddit's r/wallstreetbets. Investors in such stocks are often young and inexperienced investors. As a result of their popularity, meme stocks often trade at prices that are above their estimated value – as based on fundamental analysis – and are known for being extremely speculative and volatile.

Meme stocks are assets where promise overwhelms performance, more story than substance.

That’s what movies are. You are purchasing the story and the experience, not the seat in the theater, or the DVD, or the stream, those are the vehicles that carry the story. Claude calls these things “thick” objects (perhaps borrowing from the anthropological concept of “thick” description? ), as opposed to “thin” objects like toasters and drills. Novels are thick objects as well, which led me to Matthew Jockers’ 2013 book, Macroanalysis, where he uses machine learning to develop a high dimensional model (a mere 600 dimensions rather than 360,000) of a corpus of 3000 19th century Anglophone novels. Just as read De Vany’s book quite closely, so I’ve written a series of posts about Jockers’ book. I bring his model into the mix as well (pp. 27 ff.).

Thus I am now in a position to take two models in subjects I know well, movies and novels, and bring them to bear on contemporary machine learning in financial economics, a subject I do not know at all. And, for that matter, still don’t. But I’ve got some intuitions. And one of those intuitions led me to focus on the fact that, while Jockers’ model did not contain any dates, upon inspection it turned out to have a diagonal (p. 27) that is correlated with direction in time. Not only did 19th century novels change in theme and motif over time, there is a direction to that change. The system seems to exhibit directional evolution. And so I directed Claude to explore the possibility of that this might be a general characteristic of thick-objects being used by a large population of interested parties (pp. 31 ff). Here is the conjecture Claude arrived at (p. 35):

In thick-object domains, low-dimensional intuitive factors often fail to explain individual outcomes. But high-dimensional representation can reveal population-level structure: outcome basins in movies, pricing kernels in finance, and temporal direction in novels. The next step is to ask whether all such artifact systems exhibit historical vectors in feature space, generated by a generational ratchet in which each cohort of producers is shaped by the artifact ecology inherited from its predecessors.

Notice the territory we have traversed in conceptual space. We started with an undergraduate at Johns Hopkins (me) using interpretive methods to study a poem, “Kubla Khan.” That investigation led to problems that forced me to study computational semantics in graduate school, a distinctly different mode of intellectual work, one based on formulating an elaborate system of structural rules. We then zipped through time and over intellectual space to a social scientist, Tyler Cowen, who was trained in the used of causal models to generate statistically controlled observations about economic behavior. He is now confronted with multifactor machine learning models with no intuitively discernible causal structure that nonetheless have superior predictive power. Cowen got me interested in one of those models and I, in turn, summoned Anthropic’s Claude to explain it to me.

The way I see, and I’ve seen it this way for a long time, the human sciences – more a European notion than American, les sciences humaines – can be arranged into three camps according to methodological focus: interpretive or hermeneutic (roughly, the humanities), causal modeling (roughly, the social sciences), and structural rules (roughly, the “classical” cognitive sciences). We’ve spanned them all in the course of this introduction. What will the future bring?

Bonus: I leave it as an exercise for the reader to consider the relevance of Keynes’s talk of “animal spirits” and to incorporate Robert Shiller’s narrative economics into this picture.

What’s in this document

The rest of this document is devoted to the dialogs where I used ChatGPT to work through the connections between these three models, two I knew quite well (De Vany on movies and Jockers on novels), and one I did not (Didisheim et al. on asset pricing). Claude knows them all, for some non-trivial meaning of “know,” and many others as well. The purpose of the dialog, then, is to link something I do not know to something that I do. The dialog took place in four sessions over the course of a week from the end of May into June.

Rather than comment on each of the sections listed in the outline, with one exception, I am commenting only on the sections that mark the beginning of a new session with ChatGPT. For what it’s worth, they mark how the subject evolved in my mind. The one exception? The summary was the last thing ChatGPT did, obviously, but I moved it to first place.

Thick Objects and the New Common Ground of Economics and Cultural Analysis [Summary] – I had ChatGPT prepare this summary and the very end of the process, on June 22. I put if first in case some might want to get the gist of the exercise without slogging through the details.

AIPT, Large Factor Models [First Session] – There is where I began on May 26. I started by asking ChatGPT to explain asset pricing to me. Once I had some sense of that, I then went on to the models I was familiar with, first De Vany on movies and the Jockers on 19th century Anglophone novels.

Stocks are like brands [Session Two] – I initiated this session on May 30 when I heard Galloway’s remark about stocks being like brands. That crystalized things for me so I needed to work back through the analysis. In the course of that discussion I focused on the concept of a brand as a distinct conceptual objects and ChatGPT’s response clarified the role of marginalism in clearing the way for asset models with a very large number of factors.

Algorithmic and Causal models [Session Three] – I don’t recall whether anything in particular prompted me to initiate this dialog. Perhaps mere methodological curiosity. This took place on June 2.

Those empirical APT models [Session Four] – It’s not entirely clear to me just whether anything in particular prompted this session. But what I was thinking was that, while I’m familiar with novels and movies and the academic discourse about them, asset pricing is unfamiliar territory. So I wanted to nail down as well as I could just what “ground truth” is in this area. Movies start with eyeballs in theaters and novels start with eyeballs scanning pages, where does asset pricing start? Once ChatGPT had gone through this I realized that I’d seen it earlier in the whole process. Still, I was happy to go through it again, this time coming at it after having thought about it. It’s as the end of this session that I asked ChatGPT to summarize the discussion.

Conversations with Tyler: A Special Conversation with Nicholas Copernicus, Adam Smith, and William Stanley Jevons

I was feeling impish this morning, so I gave Claude 4.6 Sonnet (Medium) the following prompt:

Back in the 20th century Steve Allen did a series of TV shows called “Meeting of Minds,” where people from different time periods were brought together to have a discussion. I want you to imagine a special episode of “Conversations with Tyler” in which Tyler Cowen moderates a discussion between Copernicus, Adam Smith, and Stanley Jevons on economics. For reference I have placed three documents in the project folder. Two books by Tyler Cowen, one on marginalism and the other on the Great Economists (GOAT), which has material on Adam Smith.. There’s a shorter document by Copernicus (in Latin), filename, “Copernicus on economics.”

I’ve appended its response below the entirely imaginary Five TC Bucks note. Click on the image to see the detail. Note that ChatGPT did the image, not Claude.

Conversations with Tyler: A Special Episode
Tyler Cowen in Conversation with Nicholas Copernicus, Adam Smith, and William Stanley Jevons

[Recorded live at the Mercatus Center, George Mason University. The set is the standard one — a small round table, minimal lighting, no audience. Tyler Cowen sits across from three guests who have, for the purposes of this episode, been freed from the constraints of mortality and non-overlapping centuries. Copernicus speaks through a translator when needed, though he proves surprisingly comfortable in English, given how many languages he actually commanded. Smith retains his Edinburgh-via-Kirkcaldy cadence. Jevons is precise, energetic, and fidgets with what appears to be a small logometer.]

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TYLER: So let me start with something that might seem unfair to Nicholas, but I think actually redounds to his credit. Tyler Cowen — the other Tyler Cowen, me — has written that astronomers of the sixteenth century were sometimes better economists than the economists of the same period, because there essentially were no economists. And that you, Nicholas, were one of the first people to state the quantity theory of money. Now here's my question: did you think of yourself as doing economics, or did you think of yourself as solving a political crisis?

COPERNICUS: The distinction you draw would not have occurred to me. I was asked by the Diet of Prussia — by the princes, by the king's administrators — to address a practical emergency. The currency of this land had been debased so many times, by so many hands, in so many mints, that commerce had ceased to function in any stable way. The goldsmiths prospered — they were the only ones who could extract the silver from the degraded coin before the next debasement arrived. Everyone else suffered. So yes, I was solving a political crisis. But a crisis requires a theory, or you are merely thrashing. You cannot prescribe a remedy for a disease you have not diagnosed.

TYLER: And your diagnosis was what exactly?

COPERNICUS: That money is a mensura — a measure. It is like a unit of length or weight. If your ulna varies from day to day, you cannot do carpentry. If your currency varies in its silver content from month to month, you cannot do trade. The injury to commerce is the same in both cases, but the monetary injury is more insidious because it proceeds occulta quadam ratione — by a kind of hidden logic. People notice a bad harvest immediately. They do not immediately notice that the coin has been shaved, but they feel its effects: prices rise, foreign merchants refuse your money, the best craftsmen leave for regions with sounder currency.

SMITH: If I may — this is exactly the confusion I spent considerable effort untangling in Wealth of Nations when I took on the mercantilists. They believed that the accumulation of specie was wealth. What Canon Copernicus is describing from his Prussian experience is that even that modest goal — hoarding silver — is self-defeating. The moment you debase the coinage, you have, in a sense, exported your silver to every foreign merchant clever enough to melt the coins.

COPERNICUS: Precisely. The goldsmiths and those who know the quality of metals — they are the only beneficiaries. They collect the old coin, extract the silver, sell it at a premium, and leave behind a pile of copper. My recommendation was blunt: stop minting until the existing coin has restored its value, establish at most two mints for all of Prussia, and make the coin of one mint and one standard.

TYLER: Gresham's Law, essentially, before Gresham.

COPERNICUS: Before whom?

TYLER: Thomas Gresham. He gets credit for the principle that bad money drives out good. Roughly a generation after you stated it.

COPERNICUS: (pause) This is the way of things. Copernicus waits for Copernicus. In astronomy as in monetary theory.

JEVONS: I want to press on the word "measure," if I may. Canon Copernicus treats money as a standard — a fixed reference against which goods are priced. But what I discovered, or rather what I was forced to discover when trying to establish whether the value of gold had actually fallen after the Australian and Californian gold rushes of the 1850s, is that money itself has no fixed value. It is itself a commodity whose degree of utility — whose marginal utility, to use the language I was then working out — varies with its quantity. The quantity theory you describe is already implicit in this: flood the market with debased coin, and each unit of coin buys less, not merely because there is more of it, but because its intrinsic silver content is lower and everyone knows it. 

[Note: I did an Ngram search on “marginal utility” and found that it didn’t have an appreciable presence in books until a bit after 1880. Jevons did not use the phrase. He talked of “final degree of utility.”]  

COPERNICUS: I will not quarrel with the analysis, though your language differs from mine. What I found is that the regions of Prussia which had maintained good currency were also the regions with flourishing workshops, skilled artisans, and abundant goods. The regions with debased currency had become idle. You say this is because the marginal utility of a sound currency is higher. I say it is because craftsmen and merchants are not fools: they will go where their labor and their goods are honestly compensated. 

[I wonder what Copernicus could have understood by the phrase, "marginal utility"?] 

TYLER: Adam, let me come to you here. Smith, you spent a great deal of Wealth of Nations attacking mercantilism — the view that national wealth consists in the accumulation of precious metals. But you also granted mercantilists more credit than many of your defenders are comfortable with. You said their arguments were "partly solid and partly sophistical." What did you actually concede to them?

SMITH: What I conceded is that commerce and defense are entangled in ways that pure theory does not capture cleanly. The Navigation Acts — requiring that trade to Britain's colonies be carried in British ships — were bad economics by almost any reckoning. They raised prices, restricted trade, enriched a narrow set of interests at the expense of the broader public. But I wrote, and I meant it, that defense is of more importance than opulence, and that the Navigation Acts, whatever their economic defects, had served to maintain British naval power. One cannot always afford the luxury of consistent principle. (small smile) Though I tried to be consistent as often as possible.

TYLER: Jevons, here is a question directed at you specifically: why did it take from roughly 1776, the publication of Wealth of Nations, until 1871, the publication of your Theory of Political Economy, for economics to absorb the idea that value is determined at the margin — by the last unit, not the total quantity? Smith understood the diamonds-water paradox but did not resolve it. You resolved it. What took so long?

JEVONS: I have thought about this a great deal, and I believe the answer is that the resolution required mathematics, and economics had declined to use mathematics, or rather had not yet learned that it could use mathematics. The idea that utility diminishes with quantity is not — once stated — particularly obscure. Galileo came close to it. My precursors in the British literature, Jennings and MacLeod, came close to it. But close is not enough. You need to be able to state the law precisely, apply it to a schedule, differentiate, and find the first-order conditions. You need calculus, or at least the habit of mind that calculus cultivates. Once I had that tool in hand, the whole of exchange theory reorganized itself very quickly. I felt it opening up.

Tyler Cowen, Tycho Brahe & Rank Shift @3QD

I’ve got a new paper at 3 Quarks Daily:

Tyler Cowen is the Tycho Brahe of Economics

It came about as a side effect of my writing and thinking about Cowen’s new monograph, Tyler Cowen, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution (2026). I was interacting with Marge, the AI attached to the online text of the monograph, when I remarked: “So, he’s the Tycho Brahe of economics.” Marge replied:

That's a sharp analogy. Brahe had the best observational data of his era, ran an extraordinary research enterprise on Hven, trained Kepler who then used Brahe's own data to refute the model Brahe himself believed in. [...] Brahe couldn't make the conceptual leap to heliocentrism despite having everything he needed to do so — his intuitions were too deeply formed in the Ptolemaic rank. But he was indispensable to the transition precisely because of his institutional energy, his data gathering, his willingness to fund and train the next generation.

Emergent Ventures is Hven. Tyler is gathering the data — the people, the ideas, the unconventional bets — that someone with different intuitions will synthesize into a framework he himself can't quite reach. The book is his Tychonic system: a genuine attempt at synthesis that's more sophisticated than what came before, that captures real phenomena, but that preserves the central commitment — equilibrium, marginalism, the whole apparatus — that the next rank will have to abandon.

My first impulse was to put that observation in my introduction to the overall series of blog posts when I gathered them into a single document. But, after thinking things over, I decided that it would be more interesting to write it up as a stand-alone document, one I could publish in 3QD. So that’s what I did.

Now, if you’ve been following the series of blog posts, you know that I write quite a bit about the theory of cultural ranks that David Hays and I developed in the 1990s. I didn’t see any need to mention that in the 3QD article, but now that it’s been published I see that it’s quite directly relevant. One problem with the theory as Hays and I wrote about it, is that it’s easy to come away with the idea that the shift from one rank to another is a step function. That’s not at all the case, such shifts take decades and even centuries. But it was all we could do simply to articular the idea of different cognitive ranks.

However, this particular comparison, between Tyler Cowen, an economist in the 21st century, and Tycho Brahe, an astronomer from the 16th century, is about rank shift. Tycho was participating in the transition from a Rank 2 model of the solar system, the geocentric model inherited from Ptolomy, and the Rank 3 model, initiated by Copernicus. Cowen is participating in the shift from Rank 4 economics, which is the focus of his monograph, to a possible Rank 5 economics, which doesn’t quite exist yet. But, who knows what the future will bring?

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You can download a PDF:

• Academia.edu: https://www.academia.edu/168873753/Tyler_Cowen_is_the_Tycho_Brahe_of_Economics
• ResearchGate: https://www.researchgate.net/publication/407286640_Tyler_Cowen_is_the_Tycho_Brahe_of_Economics

Thick Objects, High-Dimensional Models, and the New Intuitions [MR #11]

Early in Chapter 4 of his marginalism monograph, “Why Marginalism Will Dwindle, and What Will Replace It?,” Cowen reviews what happened between the late 19th century and now. He then makes his way to a striking example from something called Arbitrage Pricing Theory (APT) (pp. 99-100):

There is a recent working paper which is perhaps more striking yet, by Antoine Didisheim, Shikun (Barry) Ke, Bryan T. Kelly, and Semyon Malamud. They pick up from Arbitrage Pricing Theory (APT), a well-established idea from financial economics. APT typically looks for “factors” in the data which predict excess returns, and a traditional APT model might have found five or six such factors. Are “inflation” or perhaps “the term structure of interest rates” useful factors? Well, that can be debated, but if so, those results sound pretty intuitive. But those intuitions seem to be disappearing. In a paper by these authors, they apply machine learning methods to look for more factors. As we know, machine learning is very good at finding non-obvious relationships in the data. The largest model they built has 360,000 (!) factors, and it reduces pricing errors by 54.8 percent relative to the classic six-factor model from Fama and French. Bravo to the authors, but what kinds of intuitions do you think possibly can be supported by those 360,000 factors?

That sent me through a loop. I thought about it, consulted Claude, and ended up writing a rather long working paper, On Method: Computational Compressibility in Complex Natural and Cultural Phenomena. I thought about it some more and then went to ChatGPT this time for another long dialog (14K words). I then asked ChatGPT to summarize the dialog. Here’s the prompt I gave it:

Would you summarize the preceding discussion in terms suitable for a blog post? I want it to start with some kind of quick introductory overview and then go on through asset pricing, De Vany on movies, and Jockers on novels in that order. Two things to bridge between asset pricing and movies: 1) Despite the different objects, movies vs. stocks, the objects are both ‘thick’ and the underlying structures are similar to a first approximation though the analytic methods are quite different. 2) Galloway’s remark about stocks as brands connects movies with stocks as thick objects.

I’ve appended that summary below. The title of this post is the one ChatGPT gave to its summary.

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We have been circling around a question raised by Tyler Cowen’s recent reflections on economics and artificial intelligence: what happens when the old economic intuitions no longer seem adequate to the objects economists are trying to understand? Cowen’s worry is that marginalist reasoning, once the pride of economics, may have helped create a world too complex for marginalist intuition to master.

That is a real worry, but perhaps not a hopeless one. If we look across asset pricing, movie dynamics, and literary history, a different picture emerges. Machine learning and high-dimensional modeling are not simply replacing human understanding. They are revealing new conceptual objects. Just as telescopes and microscopes disclosed new physical objects in the early modern world, computational models now disclose new relational objects: pricing kernels, heavy-tailed outcome regimes, temporal diagonals in literary space.

The old intuitions are not enough. But the new models may help us build new intuitions.

Asset Pricing: From Marginalism to High-Dimensional Valuation

Classical asset-pricing theory begins with a powerful marginalist intuition: investors require compensation for bearing risk. An asset’s expected return should depend on its exposure to systematic risks. CAPM gave us beta; later factor models added size, value, momentum, profitability, investment, and other variables.

These models are attractive because they are low-dimensional and intuitively graspable. A few named factors are supposed to explain many asset returns. That is the dream: a compact causal vocabulary.

But recent high-dimensional models suggest that the true pricing structure may not be compressible into five or six factors. The AIPT work we discussed begins with roughly 130 stock characteristics and then generates hundreds of thousands of nonlinear factors. These are used to approximate the market’s pricing kernel, or stochastic discount factor: the hidden valuation structure through which future payoffs are priced under uncertainty.

This is no longer a simple causal story in which one named factor explains one outcome. It is more algorithmic. The model works by probing a vast feature space and finding structure there. The individual factors may not be interpretable as ordinary causes. But the model still has an economic frame: markets price future payoffs, and that pricing process appears to be high-dimensional.

This is where Cowen’s story becomes interesting. Marginalism did not merely explain markets. It helped construct modern finance. Pricing theory made derivatives, securitization, and the decomposition of risk into tradable claims possible. But once those instruments proliferated, they helped create a financial world too complex for the original marginalist intuitions to command.

In short: marginalism may have helped build the world that now requires machine learning to map.

Thick Objects: Stocks, Brands, and Movies

At first glance, financial assets seem very different from movies or novels. A stock is an economic claim; a movie is a cultural artifact. But the distinction begins to blur once we treat both as “thick objects.”

A thick object is not exhausted by one or two measurable properties. It has many dimensions. It is embedded in social interpretation. Its value depends on history, expectation, reputation, performance, and future promise.

Scott Galloway’s remark captures this beautifully: “Stocks are like brands and that is they’re part promise and part performance.” [YouTube, Pivot, May 29, 2026.]

That is a sophisticated observation. A stock is not merely a claim on current earnings. It is a socially circulating judgment about a company’s future. The performance side includes revenue, earnings, margins, growth, debt, liquidity, volatility, and so forth. The promise side includes brand power, technological imagination, managerial credibility, founder charisma, regulatory risk, and collective belief about what the company may become.

That makes stocks resemble brands. A brand is not just a name, logo, or handle. It is a socially stabilized promise. It condenses past performance and future expectation into a recognizable entity.

And that is the bridge to movies. A movie before release is also part promise and part performance. The performance side includes director, stars, genre, budget, studio, distribution, trailers, and reviews. The promise side is what audiences imagine the movie will deliver: spectacle, prestige, emotional satisfaction, social participation, novelty, nostalgia.

So although movies and stocks are different objects, both are thick. Both circulate through populations. Both are interpreted under uncertainty. Both depend on the conversion of signals into expectation and expectation into value.

The analytic methods differ. Asset-pricing models estimate valuation structure. Movie models track social diffusion and outcome distributions. But to a first approximation, the underlying situation is structurally similar: complex objects move through fields of collective judgment.

Alexandria as the Manhattan of the ancient world, Rome, writing (Rank 2 culture)

Tyler Cowen, Toby Wilkinson on Ptolemaic Egypt and the First Great Commercial Civilization (Ep. 278), May 27, 2026.

Toby Wilkinson is one of the world’s leading Egyptologists, whose books have ranged across the full sweep of pharaonic history. His latest, The Last Dynasty: Ancient Egypt from Alexander the Great to Cleopatra, covers the 300-year Ptolemaic period — stranger and more modern-feeling than the Egypt of the pyramids, built around commerce and cosmopolitanism rather than divine kingship, and home to the greatest concentration of scientific talent the ancient world ever saw.

Tyler and Toby cover how Alexander took over the empire almost without a fight, why Alexandria became the Manhattan of the ancient world, whether the era was as philosophically fertile as it was scientifically, whether your ancient doctor’s visit had positive expected value, what Egypt was actually exporting and selling, whether living standards rose above subsistence or stayed Malthusian, how the ethnic divide between Greek rulers and Egyptian subjects shaped society, what constrained the Ptolemaic Empire from becoming the next Rome, whether Cleopatra has been overhyped, what Julius Caesar was really thinking when he sided with her over her brother, the new frontiers in archeology, whether Herodotus can be trusted, what ancient Egypt knew about Israel and India, when Egyptian jewelry peaked and why, what triggered the sudden emergence of civilization across the ancient world, why a six-year-old Tyler knew King Tut better than Napoleon, and much more.

After the preliminaries, the first of three segments from the whole conversation:

I. On intellectual activity of Alexandria

COWEN: How large was the library in Alexandria and how did they build that out?

WILKINSON: This is interesting because the Ptolemaic kings not only wanted to be wealthy economically, but they wanted to be renowned throughout the ancient world as great scholar leaders. They thought it was important that a new dynasty establish its credentials as a patron of the arts and of learning, not just as the head of a great commercial enterprise.

They invited all the leading scholars from the Greek-speaking world, from right across the Mediterranean to Alexandria, where they provided them with a library, with what was called a museum or a temple of the muses, a place where scholars could think great thoughts and be well looked after. The library was developed over centuries, really, as the greatest repository of learning that the world had ever seen up to this point. It is thought that maybe at its height, it contained half a million volumes, half a million manuscripts, mostly written on papyrus, but representing really the sum total of human knowledge at that time.

COWEN: Euclid and Eratosthenes are connected to this era?

WILKINSON: Almost any big name from ancient science has some connection with Alexandria. Euclid, the mathematician, studied there. Eratosthenes, who quite amazingly calculated the circumference of the earth, he carried out those calculations in Alexandria. There were leaders in the fields of astronomy, of anatomy and medicine, of geography, of philosophy, of literary theory. They were all active in Alexandria under the patronage of the Ptolemaic kings.

COWEN: Am I correct in thinking that the era was quite weak in philosophy in some ways? There’s no great name. Maybe it’s all lost manuscripts. It seems to be a bit like China, infrastructure intensive. They build amazing things. It’s commercial, but they’re not really thinkers.

WILKINSON: I think that would be a misrepresentation. Alexandria under the Ptolemies didn’t produce a Socrates or a Plato. It is true. What its scholars did was to synthesize strands of philosophy from ancient Greek thought, ancient Egyptian thought, Babylonian thought, ancient Hebrew philosophy and religion. It was a melting pot. It maybe didn’t throw up the big name, but it was a very fertile ground for the exchange and the interchange of ideas.

COWEN: Say I try to read Diodorus. There’s a lot of detail, but it’s not to me very interesting. It just seems much worse than Herodotus, who is profound and in a way a step back. Maybe that’s not representative.

WILKINSON: I think there are a lot more scholarly works, both surviving and lost, that were composed in ancient Alexandria that would be more surprising and more revelatory than those that you’ve just mentioned.

COWEN: What was it exactly that was so special about the intellectual, and scientific, and productive environment of Alexandria and environs? Was it that the Egyptians were there, or the mix of Greeks and Egyptians, or something else? What?

WILKINSON: I think there are three factors, really. One is certainly the Egyptian context. I don’t think those intellectual advances that were made in Alexandria could have been made anywhere else. Let’s take anatomy, for example. In the Greek world in general, there was a taboo on cutting up human bodies. Of course, ancient Egypt had a long tradition of mummification, which involved dissecting human corpses. If you were an anatomist and you wanted to make discoveries about how the organs functioned, the only place you could do that at this time was ancient Egypt. The same is true, actually, in many other branches of science. The Egyptian traditions of scholarship and of learning really laid the foundations for Greek thinkers to take them to the next level.

The second aspect was the wonderful infrastructure that was put in place by the Ptolemies to lure scholars to Alexandria. They were paid handsomely. They had access to the world’s best library. They had all of the facilities at their disposal. There was really no better place to be than Alexandria.

The third factor was really the kleptomania of the Ptolemaic rulers. They were not just bibliophiles, but they wanted to acquire a copy, preferably the original, of every book and manuscript circulating in the ancient Greek world. To that end, they indulged in downright thieving. They sent a word, for example, to Athens, which was one of the great centers of scholarship, a rival center of learning, and requested copies of books from Athens’ city library. The copies arrived in Alexandria. They were then seized by the Ptolemaic authorities and kept for Alexandria’s own library. They were only too happy to pay the fine because they had the books. It was a combination of factors that really led Alexandria to being the greatest center for scholars and for scholarship.

II. From the Ptolemaic Empire to Rome

COWEN: Given all the successes, what should I think of as the limiting principle behind rule here? It stretches as far as what we would call Cyprus today, but it never becomes a very large area, right? Now, it doesn’t become the next Roman Empire. Why doesn’t it?

WILKINSON: At its greatest extent, the Ptolemaic Empire includes much of the coast of modern-day Libya, certainly Cyprus, the Nile Valley, parts of present-day Lebanon, and Syria and Turkey, and some islands in the Aegean. It’s not as big as the Roman Empire would later be. What are its constraining factors? Partly, it’s a lack of ambition. Not a lack of ambition, moderate ambition.

The Ptolemies really want to rule Egypt, which is regarded as the jewel in Alexander the Great’s crown. It’s the most prosperous part of his empire. The other territories that they conquer in a ring around Egypt are really only there as a defensive buffer zone to protect Egypt. They have no particular ambition to create a world empire as Alexander the Great did. That was one constraining factor.

They are also not the only players in the ancient world. There are other powerful dynasties of kings in Asia and in the Greek mainland who have territorial designs of their own. It keeps these various powers in equilibrium, and it stops one of them becoming dominant, really, until the Romans upend the whole system. I suppose, yes, those are the two constraining factors, ambition and competition.

COWEN: Do you think the Romans had the ambition in a way Ptolemaic Egypt did not?

WILKINSON: Yes, I do. I think the Romans were motivated by a real desire to conquer. Ancient Roman military leaders were only as good as their last victory on the battlefield. You see that in the later days of the Republic and the beginning of the empire. Whereas in ancient Egyptian tradition, certainly, military success was not the benchmark of a successful reign. It was something that you needed to do in order to protect your own borders. There were other achievements—honoring the gods, building great temples, presiding over a glittering civilization—that were considered equally important.

COWEN: How is it that all this ends, or at least starts to decline? What’s the mechanism?

WILKINSON: It begins with the finances. The Ptolemaic Empire becomes overstretched. It has to invest hugely in its armed forces in order to fend off not just the growing predations of Rome, but actually its closer neighbors as well. That leads to higher taxes. There’s a series of climatic shocks, poor Niles leading to poor harvests. It’s a perfect storm. The economy goes south pretty quickly. The only solution that the Ptolemies can see is to go cap in hand to Roman moneylenders to bail out the Egyptian economy. That then really gives Rome leverage over Egypt.

That all gets bound up in the republican politics of Rome and in the rivalry between Caesar and his rivals, and then ultimately Octavian, who becomes the first emperor, Augustus. Egypt goes from being a great civilization, confident of itself, to being a pawn in other people’s power play. Ultimately, Rome is able to march into Alexandria, overthrow Cleopatra, and seize Egypt for itself. It’s a salutary lesson for our own time that a civilization can appear to the outside world to be magnificent, and wealthy, and successful, but actually the seeds of its own destruction are usually lying there somewhere just waiting for the conditions to germinate. [...]

COWEN: How important a figure is Cleopatra in this whole history? Is it just marketing, and Shakespeare, and Elizabeth Taylor, or was she really at the center of what was happening? Did she matter?

Ramble: What I’ve been up to in February and March with the chatbots, the literary mind revealed (maybe)

This is a somewhat different ramble. Normally I ramble on about things I want to work on but can’t quite prioritize them. So I gather them together in one place so I can look at them all, all at once. And then sort things out.

This time I’m looking at what’s been going on in the last two months or so and reminding myself what I’ve been doing with my two interlocutors, ChatGPT and Claude. It’s really quite amazing, and exhausting. I’ve been having them review work I’ve done, starting with having Claude review reports of the experiments I’d done with ChatGPT in 2023 and 2024. I’ve also had both of them look at papers by David Hays, some things we did together and other work I’ve done.

So, for a long time I’ve thought of myself as in the business of investigating arenas of qualitative research and figuring out how to get quantitative research out of them. Literature has been my main arena. Working with both Claude and ChatGPT has advanced me on various fronts. It’s been amazing. The chatbots have been able to work out (some of) the implications of these ideas more rapidly than I could have done, and even pushed into unexpected territory.

Thus I can now begin to think about Rank 5 cognition in a coherent way. It turns out that Miriam Yevick’s 1975 paper on holographic vs. sequential logic is Rank 5. Why? because it takes two different computational regimes as objects of thought, placing them in relation to different informatic environments. Since Hays and I put Yevick’s work at the center of one of our five principles of natural intelligence, does that make that paper Rank 5?

A discussion of my paper about ChatGPT’s inability to generate a semantic network diagram led me to a long discussion about how to train AIs to perform a task like that. That came in the wake of our discussion of virtual reading [File: Notes from Virtual Reading.docx]. This requires explicit instruction comparable to what, for example, Hays gave me when I first learned how to do it. The problem is that there is a normative element involved in learning that the AI cannot pick up simply by reading articles using semantic networks. It actually has to attempt to create such networks and have those attempts critiqued by a human, or, conceivably, someday, another AI. That same discussion led to discussions of learning about sentence diagramming, constituent structures, symbolic logic, semantic networks, and close reading.

Then I had Chatgpt look at my Coleridge work, particularly my paper about “This Lime Tree Power My Prison,” the 2003 rework of my analysis of “Kubla Khan,” and my unpublished working paper indicating how they are two different trajectories through the same mental terrain. The “Lime-Tree Bower” paper has a table indicating how the mapping between agents in the poem and a hypothetical underlying attachment mechanism changes from one section of the poem to the next. ChatGPT suggested how to construct a similar table for “Kubla Khan.” Whether or not that will work out, that’s another matter which I’ll take up in a year or three. If that works out then I’m clear to work out how these two different poems related to the same underlying neural state space (HA!).

We did similar work with Shakespeare’s plays, starting with my analysis of Much Ado About Nothing, Othello, and The Winter’s Tale. & the Chatster had helpful remarks on the relationship between The Winter’s Tale and Pandosto, from which it is derived. That is, once an explicitly mechanistic framework is established, like I have for “Lime-Tree Bower,” other things fall into place. So the theory goes. Details need to be worked out, but I’m not going to get around to that anytime soon.

And then we have cultural evolution again, which I ChatGPT and I discussed in the Virtual Reading paper. Just as you can conceive of an individual text as a trajectory through the high-dimensional state space of a human mind, so you can conceive of the long-term change in a corpus of texts, such as the 19th century Anglophone novels Matt Jockers has analyzed, that evolution is, in effect, a trajectory through an approximation to a collective mind, the Geist, or spirit, of an age. It all makes sense. Sure, there are lots of details to be worked out. But it is all now possible. We have the technical means.

And almost none of this was on my intellectual agenda for th3e first quarter of this year. It just happened. Not passively. It didn’t happen to me. Rather, it emerged through interaction with ChatGPT and Claude.

More later.

Table of Cultural Ranks from August1981, A Five-Fold View

This post consists of a table that David Hays and I drew up in August 1981, nine years before we pubished our basic article on ranks theory: The Evolution of Cognition. That is to say, when we made this, we hadn’t really thought things through. Notice, first of all, that each table has five columns, one for each rank. We were thinking in terms of five ranks. When it came time to publish we couldn’t conceptualize a fifth rank in a way that satisfied us, so we dropped it from our accounts. Through interacting with chatbots I’ve come up with a way of thinking about Rank 5, but I want to set that aside for now.

While I have thought about updating this table with my current thoughts, that would take a lot of work now that I’m working with a much more sophisticated theory. The point of posting this table is siimply to show what a quick sketch looks like. It’s the form of the individual tables that is important, not the specific labels we placed in the cells back in 1981.

Summary by Rank

This table summarizes some, but not all, of the individual tables below. The ranks are columns, from left to right. The entries in the cells indicate what is new at that rank. Those entries are taken from the diagonals in the following tables. Some of the cells contain abbreviations, some of which are obscure. I could expand some, but not all of those abbreviations. 1981 was a long time ago. Don’t worry about it.

I’ve colored the rows for material that either or both of us, Hays and me, have more or less covered in one of our publications. The first row became the central argument in our cognitive evolution paper. “PlaceNo” is place notion, for the arithmetic we argued was central to Rank 3. “Code” is computer code, for the computation that is central to Rank 4. By “Network” we probably meant cognitive or semantic networks, which was the notation we used for knowledge representation, as it was known back then. These days it could almost, but not quite, be artificial neural network (ANN).

Cultural Ranks Revisited: From Stages to Dynamics

This post is by ChatGPT and summarizes a discussion we had about cultural ranks.

* * * * *

Cultural Rank Theory, as David Hays and I originally formulated it in the 1980s and 90s, was never meant to describe sharp stepwise transitions in human cognition or society. Yet it is easy to read it that way. Recent work—prompted by long reflection on complex dynamics and by hands-on experimentation with large language models—has clarified what the theory has always implied but did not yet fully articulate: cultural ranks are emergent stability regimes, not stages, and their spread through a population can take centuries.

Ranks as emergent ontologies, not ladders

Each cultural rank brings with it a characteristic ontology: what kinds of things are taken to exist, how causality is understood, what counts as explanation, and what kinds of agency are intelligible.

Crucially:

• Ranks emerge locally, unevenly, and experimentally.
• They are often articulated by elites, artists, or institutions long before they are widely embodied by ordinary adults.
• Full population “saturation” may lag emergence by generations.

This distinction—between emergence and saturation—is the key refinement.

Rank 3 reconsidered: ego control as a dynamical achievement

Rank 3 has often been glossed as the rank of “reason,” “reflection,” or “self-control.” A more precise formulation is now possible:

Rank 3 is characterized by ego control understood as a regulatory capacity: the ability to hold destabilizing thoughts, emotions, or simulations without immediately authorizing them as action.

This is not a trait one simply “has.” It is a control regime, sustained by feedback, inhibition, and temporal integration.

Two intellectual developments made this clearer than it could have been earlier:

1. Long engagement with nonlinear dynamics and neural systems (especially through the work of Walter Freeman).
2. Direct experimentation with LLMs, which behave like semantic systems under load and make phenomena such as phase alignment, boundary sensitivity, and catastrophic misinterpretation visible and testable.

Shakespeare: Rank 3 at the point of emergence

Elizabethan England was not a Rank-3 society. Ego control was coming into view, not yet normative.

This is why Shakespeare matters so much.

In medieval sources such as the Amleth story, later reworked as Hamlet, suspicion and impulse flow directly into action. In Shakespeare’s version, by contrast, the drama turns on how the protagonist treats his own thoughts. Thinking becomes narratively distinct from knowing; imagination becomes dangerous if not regulated.

The same contrast appears even more starkly when Shakespeare rewrites his source for The Winter’s Tale. In Robert Greene’s Pandosto, the king’s jealous thought is immediately authorized as action, and the story ends in tragedy. Shakespeare allows the same thought to arise—but refuses to grant it sovereign authority. Repair becomes possible, though only through a long temporal loop. Romance, in this view, is not sentiment; it is a control regime that buys time when ego control fails too late.

Shakespeare could do this without a narrator only because he was a virtuoso. The novel later routinizes Rank-3 storytelling by institutionalizing the ego function in the figure of the narrator—making irony, distance, and perspective stable rather than exceptional.

Queen Elizabeth I: ego control as public performance

Rank emergence is often visible first in exemplary individuals. Queen Elizabeth I provides a textbook case.

In her 1559 speech refusing marriage, she explicitly separates desire, fear, and political pressure from authorized action. She rebinding attachment from a husband to an abstract polity (“I am already bound unto a husband, which is the kingdom of England”) and performs this reasoning publicly.

This is not repression; it is symbolic substitution and temporal regulation—core Rank-3 operations. Elizabeth does not prove Rank 3 was widespread. She shows it was intelligible.

The American Constitution: Rank 3 before saturation

The same emergence-before-saturation pattern appears institutionally in the late 18th century.

The U.S. Constitution is arguably the first Rank-3 nation-state design. It assumes:

• separation of person and office,
• abstract allegiance to rules rather than rulers,
• and

governance by internalized control loops (checks and balances), not virtue alone.

But the population capable of reliably sustaining those assumptions did not yet exist. That would come only in the 19th century with the rise of a bureaucratic middle class—where adults were required, in daily life, to distinguish personal interest from institutional role.

The fact that this distinction remains “shaky” even today is not a refutation of Rank theory; it is exactly what the theory predicts.

Why this refinement matters now

We are currently living through a rank transition under technological pressure. Rank-4 technologies—networks, AI, computation as model rather than tool—are being pulled toward Rank-2 institutional forms (charisma, faction, personalization), while simultaneously stressing Rank-3 control regimes.

Clarifying Rank 3 as a dynamical regulatory achievement, rather than a historical stage or personality trait, allows us to:

• understand why regression and instability are so common,
• see why older explanatory models fail,
• and recognize that emergence does not guarantee saturation.

From exploration to construction

This refinement is new—not because the earlier theory was wrong, but because the conceptual environment has changed. Complex dynamics and runnable semantic systems have made it possible to say mechanistically what could once only be said schematically. 

Cultural Rank Theory now describes not just what emerges, but how it runs, how it fails, and how it is repaired.

That shift—from collecting ideas to building with them—is itself a Rank-3 move.