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.

What I did last week: aesthetics, economics, Rorschach analogy for AI, default images, and “leveling”

I did some satisfying work last week. Here’s a quick rundown. I’m listing the posts in the order I wrote them.

Visual Aesthetics

A case of visual aesthetics: Why is the monochrome image superior to the color image?

The issue, black & white vs. color, has been and I suppose remains central to photography, and I deal with it there, a bit. But that’s not what I’m doing here. This is about the conversion of a particular ChatGPT image from color to black & white. It was a fun post to assemble and to think about. I like the suite of images.

Rank 5 Economics?

Beyond Marginalism: What’s Next? [MR #12]

This is my last word – save for an introduction I’ll write in a week or three, who knows? – on the fourth and final chapter of Cowen’s monograph on marginalism. This is where he tosses up some examples of leading edge work in economics, noting that it’s drifting away from marginalism into complex high-dimensional models created through machine learning. His examples come from finance. The new models yield better predictions.

I focus on one model that has 360,000 parameters and end up making (speculative) sense out of what’s going on. I suggest that those parameters are picking up the effects of Keynes’ “animal spirits” as expressed in the gossip and stories of Schiller’s narrative economics. I further suggest that we can test this by comparing the output of a classical model with that from a high-parameter machine learning model. The divergence should be highest with those stocks otherwise identified as meme stocks.

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

Here I take my speculations about how to test these high parameter models and present them to Marge, the AI associated with Cowen’s book. Marge approves.

Rorschach test for AI

More on how I’m approaching The God Test – Rorschach! [GT-2]

I came up with the Rorschach blot as analogy for the kind of challenge AI presents to us, to our understanding of AI and of the future. The idea is that the blot does have a form, albeit a complex one that’s not very legible. Hence our commentary on it (that is, on AI) tells as much about us as about AI. I’ll be developing this further in a later post.

Prototype Image in ChatGPT

This is a new working paper that opens up a whole new line of investigation. This was a fun piece of work. Writing it up took way longer than actually generating the images.

A prototypical image in ChatGPT 5.6: An informal pilot study

Abstract: Previous work has found strong default preferences in stories generated by large language models from minimally specified prompts. To determine whether a similar effect appears in image generation, I asked ChatGPT 5.6 to create 17 images in separate chats using four prompts that specified either no subject matter or only a rendering medium: “Create an image,” “Create a drawing,” “Create a painting,” and “Create a water color painting.” Sixteen of the 17 outputs depicted closely related landscapes containing mountains, trees, sky, and water; the remaining image depicted a lighthouse. The images also shared a calm, picturesque mood and contained no human figures, although several included signs of human habitation. Because the internal prompt passed to the image generator was not available, the study cannot determine whether these defaults arise primarily in the language model, the image generator, or their interaction. The results are exploratory but suggest that severely underspecified image prompts may reveal stable default preferences in the integrated ChatGPT image-generation system.

The “leveling” of knowledge in the compressed form of LLMs

NYTimes: AI needs human supervision in order to complete an entire job.

This is something I’ve been thinking about off and on for a while, but this is my first explicit framing of the issue. The idea is that once ideas or set of ideas has been expressed in writing and those documents then consumed into an LLM, all ideas function the same within/through/for the model. In that post I’m comparing a study of using AI to perform routine office processes (from NYTimes) with the use of AI to perform a complex set of tasks in drug development, in effect, high-school level capability with Ph.D. level capability. They’re the same to the LLM.

I need to think about this some more. It seems to me what’s nowhere present in the LLM is the kind of procedural knowledge necessary to learn tasks at whatever level. That simply isn’t presented in the written products of that knowledge (not even in written procedures).

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...]

Beyond Marginalism: What’s Next? [MR #12]

It is time to conclude my series of posts on Tyler Cowen’s monograph, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution (2026). Let’s look at the fourth and final chapter, “Why Marginalism Will Dwindle, and What Will Replace It?” Here’s how Cowen opens it (p. 85):

The underappreciated news is that marginalism is on the way out. Furthermore, this is old news, though the trend is accelerating.

Most of all it is underdiscussed news. As economics continues to evolve, marginalist insights – probably of all different kinds – will lie ever further from the frontiers of research and knowledge.

I find it easy to imagine that – less than 20 years from now – marginalism will be viewed as a historical curiosity rather than a central analytical engine of economics. No one will quite come out and say that, nor will they present marginalism as false or destructive. Rather it will be seen as of limited relevance, much as we might view parts of the earlier classical economists, such as their expositions of the quantity theory of money. New and different analytical frameworks will replace the ones that have dominated neoclassical economics to date.

Think about that, think about it very carefully. When thinking about it remind yourself that Cowen named his blog, his virtual home base for the last two decades, after marginalism.

For a professional academic to say that the world in which they were trained, the structure of ideas within which they have worked, which they have nurtured in students, which they have communicated to the public at large, which they have come to love, to say that that world is slipping away into the past, man, that’s rough. And rare. Not many have been able to do it.

Back in 1946 the great physicist, Max Planck, remarked, “A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.” Thomas Kuhn referenced that remark in The Structure of Scientific Revolution, and the economist Paul Samuelson gave a compressed version in a 1975 article in Newsweek. It would appear that Cowen has gotten the message and decided that, rather than dropping dead, he’d give the new ideas a boost.

After that sobering opening, Cowen reviews what happened between the late 19th century and now. He lands on price theory. Price theory? – “the view that the basic intuitive economic concepts, as would be taught in intermediate microeconomics, are highly useful and for advanced problems too” (p. 91). There’s that word, “intuitive.” Cowen explains:

Your hypothesis should be intelligible in terms of microeconomic concepts that you can hold in your mind and understand. In most (maybe not all?) cases, you should be able to explain some version of those principles to a well-educated, non-economist onlooker.

A couple pages later we arrive at something called “Topkis’s Theorem” which is very mathy (p. 94). Two pages after that: “Economic intuition, RIP. And marginalism with it.” Whoops! “I am seeing the traditional, intuitive approach to economic reasoning retreating from one field after another. To give one vivid and also important example, machine learning and neural nets are overturning the world of finance.”

Modeling collective action with 360,000 factors

A couple of pages later Cowen gives us a striking example. It’s from something called Arbitrage Pricing Theory (APT) (pp. 99-100). It’s a model that uses machine learning to develop 360,000 factors and does a better job of predicting than traditional models have only five or six factors. However, the factors in the traditional models are derived from marginalist assumptions and make intuitive sense while none of those 360,000 factors are legible. It’s clear to Cowen that, in the current intellectual marketplace for economics, the unintelligible models with superior performance are out-competing the traditional marginalist models. Bye, bye, marginalism!

I see no need to comment extensively on this particular model as I’ve already given it a great deal of attention, generating two different working papers from it. The first, On Method: Computational Compressibility in Complex Natural and Cultural Phenomena, places it in the context of a half-dozen other investigations in a half-dozen fields in the social and natural sciences. The second, Notes on the Collective Valuation of "Thick" Objects: Financial Assets, Movies, and Novels, compares it with work that Arthur De Vany published in 2004, Hollywood Economics, and a more recent study by Matthew Jockers, Macroanalysis (2013), in which he investigated a corpus of 6000 19th century Anglophone novels. I’ve also written a blog post that complements that second paper: Thick Objects, High-Dimensional Models, and the New Intuitions [MR #11].

In that second paper and in the blog post I argue that those three cases are about a collective process where a population of human actors – traders and analysts in one case, movie goers in another, and novel readers in the third case – make judgements about “thick” objects. Even before I made an explicit argument, I had an intuition, an intuition that, despite the obvious differences, what De Vany was up to with movies was somehow like what Didisheim et al. were up to with stocks. Just where those intuitions came from, I can’t say, but I’ve been thinking about complex systems for a long time. [As an aside, for what it’s worth, Robert De Vany’s work on movies is perhaps where my interests in culture and cultural evolution come into closest contact with Cowen’s interests in economics and, in particular, in the economics of culture.]

As for the idea of thick objects, the term was suggested to me by either ChatGPT or Claude to characterizes complex objects whose characteristics cannot be fully enumerated because of that complexity. Moreover they are under constant scrutiny by a population of people who are interested in them and constantly evaluating them back and forth among themselves and, in that process, revealing further characteristics. It is not difficult to see that movies and novels are the same kind of thing, each is a mode of storytelling, and that they are complex objects. But what do they have to do with stocks? A remark by the pundit, Scott Galloway, made the connection for me in a podcast with Kara Swisher, “Stocks are like brands and that is they’re part promise and part performance.” Performance is assessed by a wide variety of metrics, metrics which go into the models such as the one by Didisheim et al., while promise is subject to endless speculation, some of which inevitably precipitates into those metrics.

Animal spirits, narrative economics, memes, and a Squid Game market

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.

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.

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.

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.

We need new economic indicators

We have the stock market figures everyday. Maybe also we could have figures on the number of homeless people too.

And the media can report daily on those homeless figures. pic.twitter.com/Wgc2w9Bglb

— David Graeber Institute (@Graeber_social) June 24, 2026

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.]

* * * * *

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