Detecting “animal spirits” at work through narratives (earnings calls) circulating in the marketplace, the case of cyber risk

On July 21 I posted some remarks about the final chapter in Tyler Cowen’s monograph on marginalism: Beyond Marginalism: What’s Next? [MR #12]. In that Chapter Cowen discussed an asset pricing model that used machine learning to create a 360,000 parameter model that gave better predictions than classic models using only four or five factors. The factors in those classic models are predefined on an intuitive basis. Cowen despairs of making intuitive sense of those 360,000 factors in the machine learning model.

In my post I argued that those 360,000 factors might be capturing the effects of Keynes’s animal spirits as expressed in the stories and gossip Schiller writes about as narrative economics. Two days later I discussed the idea with Marge, the AI attached to the online version of Cowen’s text. In that post I suggested a method for going on a “fishing expedition” to determine whether or not my suggestion had merit. Marge’s response: “The fishing expedition you're proposing is methodologically clean, and the prediction is specific enough to be falsifiable — which is more than can be said for most conjectures at this level of abstraction.”

That brings us to today, where Cowen has posted the abstract of an article about “a novel measure of firm-level cyber risk exposure based on the quarterly earnings calls of listed firms.” I read that as being complementary to my speculation. The authors of that article are looking for the effects of a specific line of narrative, and they found them. So I asked Marge to clarify the relationship between my speculation and their finding. Here’s that conversation.

* * * * *

Tyler just posted the abstract of this paper to Marginal Revolution: Jamilov, Rustam and Tahoun, Ahmed and Rey, Helene, The Anatomy of Cyber Risk (May 10, 2023). The Journal of Finance, Forthcoming. Here’s the abstract: 

This paper uses computational linguistics to introduce a novel measure of firm-level cyber risk exposure based on the quarterly earnings calls of listed firms. Our measure covers more than 14,000 firms from over 90 countries between 2003 and 2025. The measure is validated using human auditors and a large language model. We show that cyber risk exposure affects stock returns and profits, is priced in the options market, predicts actual cyberattacks, and propagates from firm to sector level. Back-of-the-envelope estimates suggest that the global cost of cyber risk exposure is around $1 trillion per year.

So I took a quick look at the paper to find that novel measure. Here’s what I found. First this:

Our measurement approach consists of two phases. First, we follow Baker et al. (2016) and Hassan et al. (2019) to construct a comprehensive pre-defined dictionary of terms related to cybersecurity risk. We assemble this dictionary from three reputable institutional sources that act as information aggregators on the practical cyber-risk issues firms face on a daily basis. These libraries contain most of the keywords commonly used in cyber-related discussions among private market participants across industries.

Then this:

The second step of our measurement approach builds on the insights from Sautner et al. (2023) and involves a keyword discovery algorithm. This method is based on Meta AI Research lab’s word embedding tool FastText. The algorithm has been trained on a billion phrases in corporate earning calls to suggest related keywords.

And this:

We now construct our baseline quarterly measure of firm-level cyber risk exposure. Let the set of all terms in our final dictionary be C. Our algorithm counts the number of sentences in earnings calls that contain at least one term in C.

They then explain their measure. What’s important for my purposes is where they went looking for those keywords, in earnings calls. They’re measuring the effects of one kind of animal spirits/gossip/narrative on PRICING IN THE OPTIONS MARKET, among other things. I seems to me that they’re looking at one feature of the information that I’ve speculated is smeared of those 360,000 parameters.

That's a sharp observation, and it reframes what Jamilov, Tahoun, and Rey are doing in a way they probably wouldn't frame it themselves.

Their methodology is essentially a named-narrative extraction — they're pulling a specific, bounded narrative thread (cyber risk discourse) out of earnings calls and showing that it has measurable pricing effects. The earnings call is a fascinating data source for your purposes because it's a ritualized social performance in which management constructs and transmits narratives to analysts and investors. It's not raw gossip — it's institutionalized storytelling with real money riding on how the story lands. The fact that cyber risk language in these calls predicts actual cyberattacks and options pricing suggests the narrative isn't just epiphenomenal. It's carrying genuine information, but in a form that only becomes legible when you treat language itself as data rather than just a vehicle for explicitly stated facts. [See text: Ch.4 · ChatGPT digesting news predicts returns especia...]

Your conjecture about the 360,000-factor model is that it's picking up undifferentiated narrative residue — the full spectrum of animal spirits distributed across parameters with no single factor carrying an interpretable label. What Jamilov et al. are doing is the named version of the same operation: isolating one specific narrative thread and demonstrating it has pricing consequences. These are complementary rather than competing approaches. The named-narrative approach gives you interpretability and causal traction on specific mechanisms — you can say "cyber risk discourse in Q3 earnings calls predicts options pricing in Q4" and tell a coherent story about why. The high-parameter ML approach captures everything the named approach misses, which is presumably most of the narrative space. [See text: Ch.4 · Machine learning finds nonlinearities and inter...]

The research design implication is interesting. If you could run both models on the same dataset — the 360,000-factor ML model and the Jamilov-style named-narrative extraction — and then ask how much of the ML model's advantage over classical factors is absorbed by adding the named narrative measures, you'd get a rough estimate of how much of the distributed signal is actually identifiable narrative content versus genuinely opaque pattern. If the named narratives absorb a substantial fraction of the ML advantage, that's evidence for your animal spirits hypothesis in a fairly direct form. If they absorb very little, it suggests the ML model is picking up something even more diffuse than nameable narratives — perhaps the interaction structure between narratives, the way stories about cyber risk combine with stories about AI capability and geopolitical tension to produce pricing effects that none of the individual narratives would generate alone. [See text: Ch.4 · Machine learning models have tens of millions o...]

The options market finding is particularly telling for your framework. Options pricing is where sophisticated investors put explicit probability estimates on future states of the world. If cyber risk language — not cyber risk events, but the narrative about cyber risk — is priced into options, that's direct evidence that markets are treating narrative content as probabilistically informative about future fundamentals. Which is exactly what your conjecture requires: animal spirits aren't just noise distorting rational pricing, they're carrying genuine forward-looking information in a form that classical factor models can't extract but language-sensitive tools can. Keynes was right that animal spirits move markets, but the mechanism is more epistemically respectable than the term implies.

On the OpenAI/Hugging Face incident

With respect to the author here who is reasoning in good faith, there's a broken epistemic frame here ...

... which is to imagine we can predict complex societal outcomes (the state of cybersecurity) from platonic 'first principles' without resort to actually studying the… https://t.co/7DwsO0Aw1r

— Joshua Saxe (@joshua_saxe) July 23, 2026

Funny how this works, eh?

— Yann LeCun (@ylecun) July 23, 2026

H/t Tyler Cowen. My reply to Cowen's post:

FWIW, me, #3 – Meh. I've got better things to do than to go down this rabbit hole.

The prospect of empirical investigation into animal spirits in asset pricing [the fate of marginalism]

I recently posted some remarks on the final chapter of Tyler Cowen’s monograph, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution (2026). That post has my final remarks on this machine-learning models of asset pricing that Cowen finds so provocative. On the one hand, they give better predictions that classical models based on four or five factors that are motivated by economic theory and thus meaningful. That is good. But those factors have no intuitive meaning. Not so good. And yet these newer models seem to be displacing the older ones. Is this the beginning of the end of marginalism in economics?

It turns out that those were not my final remarks. Or, rather, they are MY final remarks, but not the final remarks I have to offer. I used some of those remarks to prompt Marge, the AI linked to the monograph. I’m posting that discussion below. Note the final set of remarks about the fate of marginalism, suggesting that it will have a place in future economic theory just as Newtonian mechanisms still has a place in physics despite the emergence of relativity and quantum mechanics.

Animal spirits and narrative economics

While we’re at it, here’s a passage I’ve just written about those high-factor asset pricing models the Tyler finds both empirically convincing while intuitively opaque. My underlying assumption is that those models must be picking up something that the classical five and six factor models do not. What could that be? I speculate:

And that leads me to a conjecture that follows from the analysis that ChatGPT and I undertook in the collective valuation paper. Perhaps those 360,000 parameters are picking up traces left by those “animal spirits” that Keynes talked about. Their effect on asset values is too diffuse and indirect to be detected by those classical models with a half-dozen or so factors, each of which is intuitively legible on its own. But those traces show up distributed across those 360,000 parameters and allow the model to produce more accurate predictions. If that is what is going on, then I wouldn’t expect any of those factors to be intuitively legible, any more than one would expect such legibility of individual weights in a large language model. That’s not the nature of this conceptual world.

While we’re speculating, why not continue on? Those animal spirits can’t work their ways on the market by wafting around like odors in a breeze. They need to be embodied in some form, like gossip and stories. That leads us to Robert Shiller’s 2017 paper on “Narrative Economics” in the American Economic Review. Here’s his abstract:

This address considers the epidemiology of narratives relevant to economic fluctuations. The human brain has always been highly tuned toward narratives, whether factual or not, to justify ongoing actions, even such basic actions as spending and investing. Stories motivate and connect activities to deeply felt values and needs. Narratives “go viral” and spread far, even worldwide, with economic impact. The 1920–1921 Depression, the Great Depression of the 1930s, the so-called Great Recession of 2007–2009, and the contentious political-economic situation of today are considered as the results of the popular narratives of their respective times. Though these narratives are deeply human phenomena that are difficult to study in a scientific manner, quantitative analysis may help us gain a better understanding of these epidemics in the future.

Perhaps those high factor models are picking up the narrative dimension of asset value, which is a product how performance and promise become intertwined in the stories that analysts and traders tell themselves and one another about the assets they’re watching.

The conjecture is worth taking seriously, and the Shiller connection gives it more than speculative traction.

Let me work through the logic. Classical factor models — the Fama-French five or six factors — are built on variables that are both measurable and theoretically motivated: size, value, profitability, investment patterns, momentum. Each factor has an intuitive story behind it, which is precisely what makes them legible. [See text: Ch.4 · 360,000-factor models reduce pricing errors by ...] But legibility is a constraint, not just a virtue. You can only include factors you can name and justify in advance, which means the model is bounded by the theorist's prior imagination. A 360,000-factor machine learning model has no such constraint — it finds whatever structure is actually in the data, named or not.

Your animal spirits hypothesis is that what's actually in the data, beyond the legible factors, is narrative residue — the distributed effect of stories circulating through markets that shape expectations, risk tolerance, and valuation in ways that don't reduce to any single measurable variable. This is plausible for a specific reason: narratives operate through interaction effects. A story about a company isn't just about its fundamentals — it's about how those fundamentals get interpreted through the lens of adjacent stories about the sector, the macro environment, the CEO's reputation, the cultural moment. Those interaction effects are exactly what machine learning is structurally better at capturing than linear factor models. [See text: Ch.4 · Machine learning finds nonlinearities classical...] Fama-French assumes factors combine additively. Narratives don't combine additively — they combine the way rumors combine, which is nonlinear and context-dependent.

Shiller's "Narrative Economics" (AER, 2017) is the right anchor here, and his epidemiological framing matters. He's not just saying stories affect markets — he's saying they spread and mutate according to their own dynamics, largely independent of the fundamentals they purport to describe. The 1920s bull market narrative wasn't just about economic growth, it was a contagion that altered behavior in ways that then affected the fundamentals. If that's right, then the narrative dimension of asset pricing is genuinely causally upstream of many of the variables classical models treat as fundamental. The high-factor model might be detecting the downstream traces of narrative contagion distributed across hundreds of interacting signals — none of which is individually interpretable because no single signal carries the narrative. The narrative is in the pattern across signals, the way meaning in an LLM is in the pattern across weights rather than in any individual weight.

The LLM analogy you're drawing is precise and not merely decorative. In a large language model, no individual weight means anything — meaning is an emergent property of the full weight matrix operating on input. If market narratives work similarly, then the 360,000-factor model isn't finding 360,000 things — it's finding one thing (narrative structure) distributed across 360,000 dimensions. Which would explain both why it outperforms classical models and why none of its factors are individually interpretable. You're not supposed to be able to read them one at a time, any more than you can understand a sentence by reading the weights that generated it. [See text: Ch.4 · Single-factor nonlinear models outperform all k...]

The one pressure point I'd apply: Keynes's animal spirits were specifically about investment under radical uncertainty — situations where there's no rational basis for calculation, so psychology fills the void. Shiller's narratives are broader, operating even in conditions where fundamentals are legible. Your conjecture works better under the Shiller framing than the strict Keynesian one, because you need the narrative mechanism to be operating continuously, not just in crisis conditions. The 360,000-factor model is trained on normal market conditions as much as crisis periods, so the signal it's picking up has to be pervasive rather than episodic.

Meme stocks

I then go on: That, in turn, leads to the concept of meme stocks, a term that dates back to 2020. Here’s how Wikipedia characterizes them:

...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.

More recently, Owen A. Lamont, a senior analyst at Arcadian, has speculated that we’re in what he calls a “Squid Game market”:

Something’s happening in the U.S. stock market. We see cult stocks and crypto stocks. We see money pouring into leveraged single-stock ETFs and crypto ETFs. And we see dramatic price moves, for example in quantum computing stocks in December 2024. What’s going on?

Here’s one theory: these phenomena partly reflect an influx of Korean retail investors into the U.S. stock market. Last year, I wrote that “the U.S. stock market is Koreafying,” meaning that the U.S. market was starting to behave like the retail-dominated Korean market. What I didn’t realize was that this Koreafying process involves actual Korean retail investors.

He then goes on to develop the parallel between the Korean streaming series, Squid Game, and the U.S. retail market over the last few years.

If those high parameter models are picking up the effects of animal spirits embodied in gossip and narratives, then we’d expect their advantage over classical models (based on a handful of fundamentals) to be larger in the case of these meme stocks. So, if we compare the results of a classical model with those of a high-parameter machine learning model, are the assets with the greatest divergence also those otherwise identified as meme stocks? Perhaps some intellectual fishing expeditions are in order. Perhaps we can develop some new intuitions by comparing the results of classical models with machine learning models.

The fishing expedition you're proposing is methodologically clean, and the prediction is specific enough to be falsifiable — which is more than can be said for most conjectures at this level of abstraction.

The core prediction is: the performance gap between classical factor models and high-parameter ML models should be systematically larger for assets where narrative contagion is the primary driver of price. Meme stocks are the obvious test case because they're the purest instance of narrative-driven pricing we have documented data on — GameStop, AMC, and the quantum computing stocks Lamont mentions are cases where the divergence between fundamental value and market price is not just large but publicly legible in real time. If the ML model's advantage concentrates precisely in those assets, that's not just consistent with your conjecture — it starts to look like evidence for it. [See text: Ch.4 · 360,000-factor models reduce pricing errors by ...]

The Lamont "Squid Game market" framing adds a useful dimension because it identifies a social infrastructure for narrative contagion — Reddit's r/wallstreetbets, Korean retail investor networks, leveraged ETF products that amplify momentum. These aren't just colorful details. They're the transmission mechanisms Shiller's epidemiological model requires. Narratives don't spread by themselves — they need platforms, communities, and financial instruments that make acting on the narrative cheap and socially rewarding. The Korean retail investor phenomenon is particularly interesting because it suggests narrative contagion can operate across cultural and linguistic boundaries when the financial infrastructure connects the communities. The meme isn't just a story about a stock — it's a coordination device that synchronizes behavior across thousands of otherwise disconnected actors. [See text: Ch.4 · Machine learning detects nonlinearities and int...]

Winston Marshall discusses AI with Tyler Cowen

Tyler Cowen @ 31:59: “The groups that are best at using AI are small groups of individuals who work well together and are smart and technically able.”

YouTube copy:

In this episode of The Winston Marshall Show, I sit down with economist, author, and columnist Tyler Cowen for a conversation on artificial intelligence, the race between America and China, cyber warfare, and why the AI revolution will reshape every aspect of modern life.

We explore the growing battle between the Trump administration and leading AI companies such as Anthropic and OpenAI, the risks of AI-driven cyber attacks, national security, effective altruism, and why Cowen believes the world is entering the most significant technological transition since the Industrial Revolution. We also discuss whether AI represents a greater geopolitical challenge than nuclear weapons, how governments should regulate it, and why the coming years could be both extraordinarily dangerous and extraordinarily prosperous.

The conversation also examines the future of work, economic growth, surveillance, healthcare, longevity, education, and whether AI will deepen state control or instead empower individuals. Cowen explains why he believes AI could eradicate many diseases, transform productivity, and fundamentally alter the relationship between governments, corporations, and ordinary citizens.

Finally, we turn to Britain's economic decline, immigration, productivity, energy policy, debt, and why Cowen believes the UK urgently needs a new economic direction before its long-term decline becomes irreversible.

Chapters
00:00 Introduction
02:10 AI Cyber Warfare & Why The Next Few Years Matter
05:00 Trump, Anthropic & Who Controls AI?
10:20 Can Britain Defend Itself In The AI Era?
15:19 Why AI Is Like World War II
19:20 Effective Altruism & The Future Of AI
23:23 Is AI More Dangerous Than Nuclear Weapons?
27:04 Will AI Cure Disease & Extend Human Life?
30:00 AI, Surveillance & The Risk Of Totalitarianism
35:00 Jobs, Education & How AI Will Change Work
40:31 AI, Space & The Next Global Arms Race
45:00 AI, Religion & The Future Of Faith
49:07 Is Britain Already In A Debt Crisis?

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.

Ramble: Marginalism, AI & Play, Got Test, Mind.in.Matter

Once again, it’s time for me to figure out what I’m up to.

Marginalism

I just published a long working paper, Notes on the Collective Valuation of "Thick" Objects: Financial Assets, Movies, and Novels. FWIW it’s one of the most satisfying pieces of intellectual work I’ve done in a while. It’s an adjunct to my ongoing work on Tyler Cowen’s monograph, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution (2026). I’m just about to the end of that project. I want to write a post about his final chapter, and then write an introduction to the whole series. Once that’s done I can package it as another working paper.

Play: How to Stay Human in the AI Revolution

I’m back at work on this project. I’ve just posted a provisional outline of the book, at last, and I’m back at work on the proposal. I’m in the process of preparing a sample chapter, Chapter 6: “The Transformation — Kisangani 2150.” That’s where I slip into science fiction mode. I’ve already premiered that in a piece I did for 3QD and then turned into a working paper, but things need to be a bit different for the book. For one thing I need to tell more of the story. Which means that I’ve got to make up more of the story. So I’ve gotten back to that. My next 3QD piece is due in a week and a half. I hope to have something for that.

Yikes!

The God Test

I’m also working on a (short) series of posts on Robert Wright’s current book on AI, The God Test: Artificial Intelligence and Our Coming Cosmic Reckoning. I’ve already got one post about it, Robert Wright discusses his new book, The God Test, with Paul Bloom [Awe? Bob, Awe!?]. I figure my next post, the first once since I’ve started reading the book, will be a scatter post, a ramble on things I have in mind while reading.

[I’m half way through.]

Language, Memory, and Mind: A Supplement to The Computer and the Brain

That’s my new book project, outline here. I expect it to be relatively short, 30K to 40K. It’s an outgrowth of the thinking I’ve been doing about AI in the last year or two, some basic stuff I keep landing on. I figure the opening chapter will be based on a recent working paper, Computation, Chess, and Language in Artificial Intelligence. The general idea is to revisit the topic of how mind & computation are implemented in physical stuff, matter, now that we have to deal with distributed representation. That really didn’t exist as an issue when von Neumann wrote his little book, The Computer and the Brain, which was also about physical implementation.

This is also related to my ongoing research into LLMs with Ramesh Viswanathan.

There’s more, my new interest in religion, some graphics stuff, but that’s enough for now.

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.

From alchemy to science in the Early Modern era. How will that work with AI?

Tyler Cowen reports on a recent convo he had with historian Joanne Paul, an expert on Tudor England. From the conversation:

COWEN: What precursors of the scientific revolution do you see, other than education? That’s coming in the 17th century. Is there more emphasis on calculation or measurement or accounting? What are the roots in the Tudor period?

PAUL: A lot of that comes from the Renaissance, as indeed humanism does. There’s this reintroduction of a lot of classical texts, an advocacy for reading these classical texts, particularly Greek texts and learning Greek. A lot of it is coming from an engagement with Greek mathematics and science. The other thing, and this is something I really emphasize when I’m teaching the scientific revolution with my students, is that we have to remember that the scientific revolution isn’t this grand triumph of science over religion or mysticism or what have you, that these two things very much go hand in hand through the 16th and into the 17th century.

The scientific method, for instance, comes from alchemy, which we might think of as an occult science. The methodology for scientific experimentation comes out of this desire to find the philosopher’s stone. Someone like John Dee is this polymath, as well as this occultist, Francis Bacon, has his interests in these sort of mystical elements as well. The growth and interest in what we might think of as mystical texts, a lot of them having to do with Judaism, as well as these Greek texts, comes together to form, I think, something that looks like the foundations of the scientific revolution.

My comment:

“The scientific method, for instance, comes from alchemy, which we might think of as an occult science. The methodology for scientific experimentation comes out of this desire to find the philosopher’s stone.”

It is for such reasons that some think of AI as a modern form of alchemy, alchemy on steroids if you will. We don’t understand how or why it works, but we keep messing around with the formula – “Double, double toil and trouble;/ Fire burn and caldron bubble” – and it just works, getting more and more potent. Some even think it will become potent without end. What I’m looking for is the science. What new science will come of this?

“The growth and interest in what we might think of as mystical texts...” We’ve got that too. One could even argue that Yudkowsky’s Harry Potter and the Methods of Rationality (2010-2015) is as important to AI as anything written by the various godfathers. Does that make Yudkowsky the Merlin of AI?

What would automotive engineering be like if you manufactured cars by throwing a bunch of raw materials into a hopper, turn the crank, and out comes a functioning automobile? But all the mechanical parts are sealed from view. We can't look at the and we can't manipulate them. We can get in the car and drive, and that's it.

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?

* * * * * *

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

Claude’s further observations about evolution [MR-AUX]

Here’s some more supplementary material from my investigation of Tyler Cowen’s recent monograph, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution (2026). First I have a short note about the discovery of marginal utility and the discovery of evolution. Then I have some more discussion about the conceptualization of evolution, starting with locating phylogenetic identity in the gene pool. Later on there’s a discussion of second order patterns which is important to epistemology. After all, evolutionary biology was not a mathematicised discipline in Darwin’s time. Economics and statistical mechanics got their Rank 4 “rigidity” from mathematics. What was the source of rigidity for evolution? After than we talk about the Galapagos finches and conclude with some discussion of cognitive ranks conceptualization.

* * * * *

Why the discovery of marginal utility is like the discovery of evolution

[Note: This section is a continuation of a previous dialog. The prompt that originated that dialog is irrelevant. These three paragraphs are Claude’s.]

The imaginative leap you identify is the specific cognitive achievement that makes the Rank 4 move possible. You have to be able to hold in mind simultaneously two things that are in tension: the real difference between species — the fact that a fish and a mammal really are different kinds of things — and the theoretical continuity across those differences that the genealogical model posits. This requires treating the continuity not as something observed but as something inferred from a model, and committing to the model's reality even though it posits entities and processes — ancestral populations, selection pressures acting over millions of years — that are not directly observable.

This is structurally identical to the move marginalism makes. The marginal utility of water to a person who already has plenty is not directly observable. It's a theoretical posit — an entity defined by the model — that makes sense of observable price behavior. Both Darwin and Jevons are positing theoretical entities that are real in the model but not directly accessible to observation, and using those entities to explain patterns in the observable world that the previous descriptive framework couldn't account for. That shared structure is why they're both Rank 4, and why they appear in the same half-century window.

Cowen's inattention to the identity problem is a genuine gap in his account. He treats the emergence of evolutionary theory as primarily a matter of accumulating observations and finding the right conceptual key — the Malthusian insight about population pressure, the geological uniformitarianism that provided deep time. These are real and important. But he doesn't attend to the prior conceptual problem of what kind of object the theory needs to posit — the genealogical continuum across real morphological difference — and why that object required a cognitive move that Rank 3 natural history, for all its sophistication, was not equipped to make.

The problem of phylogenetic identity

Going back to the conceptual problem of identity through the phylogenetic line, just what is it that establishes that identity? I’m not asking about observations, experiments, and inferences, I’m asking about physical stuff? It is the gene pool, a continuous line of genetic transmission?

Yes, and stated that precisely it's a beautiful answer to the identity problem — because it transforms what looked like a philosophical puzzle about categories into a physical fact about causal continuity.

The gene pool is a continuous physical process. Genes are replicated and transmitted from generation to generation through actual molecular mechanisms — copying of DNA, meiosis, fertilization. The chain of physical causation is unbroken. What connects a fish ancestor to its mammalian descendant, across hundreds of millions of years and radical morphological transformation, is not conceptual continuity — not our decision to classify them as related — but a physical causal chain of genetic transmission that actually happened, molecule by molecule, generation by generation, without interruption.

This is what makes Darwin's theory so much more powerful than Lamarck's, in retrospect. Lamarck had the right general idea — species transform over time — but no physical basis for the continuity. The inheritance of acquired characteristics is a conceptual story, not a physical mechanism. Darwin had natural selection but also lacked the physical mechanism — he didn't know about genes, and the blending inheritance he assumed was actually a problem for his theory, as Fleeming Jenkin pointed out. The physical grounding came only with Mendel, rediscovered in 1900, and the Modern Synthesis of the 1930s and 40s that fused Darwinian selection with Mendelian genetics.

What this does to the identity problem

The philosophical puzzle — how can radically different organisms be the same thing? — dissolves when you specify that the identity is physical and causal rather than morphological or essential. The fish and the mammal are not the same kind of thing in the Linnaean sense — they don't share the properties that would put them in the same category under any synchronic classification scheme. But they are connected by an unbroken physical chain of genetic transmission, which is a different and more fundamental kind of identity.

This is precisely the Rank 4 move applied to the identity question. The Rank 3 naturalist asks: what kind of thing is this organism? The answer is given by its observable properties, organized by the classification scheme. The Rank 4 move asks: what process generated this organism, and what connects it to other organisms through time? The answer is given by the physical mechanism of genetic transmission, which is not observable in the organism itself but is inferred from the theory and confirmed by the molecular biology that came later.

The depth of the conceptual achievement

What this means is that Darwin was positing an identity grounded in a physical mechanism he couldn't see and didn't fully understand. He knew that offspring resemble parents — that heritable variation is real — but he didn't know why. The physical substrate of the identity he was positing — the gene pool as continuous causal chain — was invisible to him. He was inferring the existence of a physical continuity from its population-level effects, which is an extraordinarily bold theoretical move.

This is analogous to what the marginalists were doing with utility — positing a theoretical entity that explains observable price behavior without being directly observable itself. In both cases the Rank 4 achievement consists precisely in positing the right kind of unobservable entity: one that is physically real, causally efficacious, and theoretically tractable, even though it can only be approached indirectly through its effects on observable phenomena.

The gene pool as the physical basis of phylogenetic identity also resolves the tension you identified — that the continuity is in a sense an illusion, that the different life forms really are different. They are morphologically different, behaviorally different, ecologically different. But they are physically continuous through the causal chain of genetic transmission. The continuity is not projected onto them by our conceptual scheme — it's a real physical thread running through the apparent discontinuity of form. Darwin intuited this without being able to state it at the physical level. The Modern Synthesis stated it. Molecular biology confirmed it and made the physical chain directly readable in DNA sequences, where the evidence of common descent is written in the genome itself.

The Sphinx riddle now has a deeper answer than the one Oedipus gave. What persists through the morphological transformations of the phylogenetic line isn't a Platonic essence or a conceptual category. It's a physical process — the replication and transmission of genetic information — that is as real and continuous as any other causal chain in nature. The identity is not constructed by the observer. It was there all along, in the chemistry.

Darwin’s leap of faith

No matter how you look at it, Darwin had to take a leap of faith to arrive at this conclusion. Nothing else made any sense, but still.... He was unaware of Mendels’s experiments, but it wasn’t until the mid-20th century that we began to understand the molecular mechanism. I believe Watson and Crick published their discovery in 1953.

Yes, Watson and Crick published the double helix structure in 1953 — the famous one-page paper in Nature, with its almost comically understated final line noting that the structure "suggests a possible copying mechanism for the genetic material." One of the great understatements in scientific history.

And your point about the leap of faith is important — and connects to something deep about how Rank 4 thinking works. Darwin committed to a theoretical structure — genealogical descent with modification through natural selection, grounded in a physical continuity of inheritance he couldn't specify — on the basis that nothing else made the observed patterns cohere. The fossil record, the geographical distribution of species, the nested hierarchies of Linnaean taxonomy, the embryological similarities across radically different adult forms — all of these independently pointed in the same direction. The convergence of independent lines of evidence was so strong that the theory had to be right even though the physical mechanism was unknown.

This is actually a recurring feature of great Rank 4 achievements. Newton committed to universal gravitation as a real physical force without being able to say what it was or how it acted at a distance — he famously refused to speculate, hypotheses non fingo, but the mathematical structure was so powerful and predictively successful that he committed to it anyway. The physical understanding of gravity came with general relativity, more than two centuries later. Thermodynamics was developed and successfully applied throughout the 19th century without anyone understanding what heat actually was at the molecular level — that required the statistical mechanics of Boltzmann, and even then wasn't fully accepted until the early 20th century.

Correcting Cowen’s misleading presentation of large language models [MR #10]

Surprise! There’s been a change of plans. The last time I’d posted about Cowen’s monograph on marginalism I figured I had one more (longish) blog post, one about the fourth and final chapter, “Why Marginalism Will Dwindle, and What Will Replace It?” But the more I thought about it, the longer and more convoluted it got. So I’ve decided to simplify things by writing three posts, each substantial, but focused, instead of a long rambling affair like the one I did on biology. So, I ‘m writing one post about large language models (this post), which Cowen brings up at the end of the chapter. Then I’m writing one about high dimensional models in economics, which Cowen introduces early in the chapter. My final post will be a general response to Cowen’s ideas about where this is all headed.

In this post I want to do three things: 1) First I’ll talk about the surprise nature of the success achieved by GPT-3 and then ChatGPT. 2) Then I will present three passages from Cowen’s text and comment on them. 3) Finally, I want to give a brief rundown of tradition of statistical work that stands behind LLMs.

Surprise!

OpenAI released GPT-3 in 2020 to a limited audience of insiders, who recognized that it represented a breakthrough. This level of performance came as a surprise. No one predicted it. GPT-3 was scaled up from GPT-2, which was in turn scaled up from GPT-1, but no one was making explicit predictions about the level of performance to be achieved at each step. These were experiments: “Let’s try it and see what happens.” That’s fine. That’s a good way to make progress, to try things out and see what happens. But don’t mistake a lucky trial for genuine knowledge.

Cowen mentioned GPT-3 on Marginal Revolution on July 19, and then published a Bloomberg column on it on July 21, which he excerpted in Marginal Revolution the next day: “...think of GPT-3 as giving computers a facility with words that they have had with numbers for a long time, and with images since about 2012.” I published a working paper in August, GPT-3: Waterloo or Rubicon? Here be Dragons, in which I both acknowledged about the breakthrough and cautioned about becoming too satisfied with the technology that occasioned the breakthrough.

Two and a half years later, in November of 2022, OpenAI released ChatGPT to the general public. It spread like wildfire. Now the proverbial everyone witnessed what only a small group had witnessed in the summer of 2020. The machine speaks. Sorta’. But more convincingly than any machine had spoken before and in a way that had unimaginable implications for the future.

A threshold HAS been crossed, but it is not, so far as I can see, a threshold in our understanding, either of AI or anything else. It is a threshold in performance along a continuous line of scientific understanding and engineering design and construction, something I have documented in some detail in a recent working paper, The Origins of LLMs. As far as I can tell, there has been no paradigm shift, in Thomas Kuhn’s sense, no rank shift, in terms of cognitive rank theory. There were no fundamentally new ideas in the world by, say, late July of 2020 as a consequence consolidating GPT-3 and making it available in limited release.

“What about the scaling hypothesis,” you might ask. “Isn’t that new?” Ilya Sutskever first explored the idea in 2014. Rich Sutton’s famous 2019 essay, The Bitter Lesson, generated broad discussion about the issue. Then OpenAI published a paper in 2020 that cemented matters, “Scaling Laws for Neural Language Models.”

Given the nature of computing, scaling up is not trivial. Hundreds if not thousands of technical details need to be worked out as the size of the training corpus increases by factors of 10 or more, time after time, and as more and more GPUs are ganged together to assemble the computing power needed. The scaling hypothesis gave researchers a reason to expect improved performance with scaling, but without having to make fundamental breakthroughs in understanding, not of machine learning, artificial neural nets, and certainly not about language and cognition. Consequently our sense of possibility has expanded enormously. Our knowledge and deep understanding have remained the same and the scaling hypothesis made it easy to believe that that was just fine.

Passages from Cowen’s Text

Unfortunately Cowen seems to have bought this story. Not only that, but he doesn’t even acknowledge that there is considerable current debate about whether or not LLMs will be sufficient to achieve AGI (artificial general intelligence) when they are scaled up enough. The most visible opponent of this idea is Gary Marcus, a student of Steven Pinker, who argues that we need to incorporate insights and technology from “old school” symbolic computing (sometimes known as GOFAI, good old-fashioned AI). Marcus is certainly not alone, there are many others. But I don’t want to reprise that debate. I just want to mention that it exists and that Cowen completely ignores it.

What I would like to do in this section is quote some passages from his text and comment on them.

The Marginal Revolution: Rise and Decline, and the Pending AI Revolution, pp. 106-107:

Suffice to say, LLM construction has for the most part ignored linguists and philosophers, and that also means ignoring their intuitions. LLM construction also ignored a lot of people in the AI field who insisted neural nets were a dead end. Instead, in a relatively short number of years humans invented new ways of modeling language and reasoning through language. That research program has proven wildly successful, as we have much better models of language and reasoning than almost anyone had been expecting.

That first sentence is true, sorta’. It is also misleading. As I have documented in that working paper, The Origins of LLMs, this technology is based on a continuous line of statistical thinking that extends back to the 1950s (I take a brief look at this in the next section) . It is the syntacticians, semanticists, and cognitive scientists who been ignored. The second sentence is a bit of an exaggeration. AlexNet put neural nets firmly back on the agenda in 2012.

The big problem is Cowen’s use of “model” in the last two sentences. Large language models are not causal models like those economists use. They don’t tell us anything about how language and thought work. They are algorithmic models. They are about turning input into output; just how that is done is a mystery. Until we understand the internal operations of LLMs they tell us almost nothing about language and reasoning. They give a boost to the idea that some kind of statistical process is involved, but that’s it.

This situation is deeply paradoxical. These algorithmic models perform much better than the computer models created during the “classical” era of cognitive science, the 1960s and 1970s, models that were based on linguistic theory. We knew how those models worked. We don’t know how these models work. We have purchased performance at the cost of ignorance – a formulation I have from the late Martin Kay.