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.

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.

Ramble: Marginalism, AI & Play, God 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.

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?

* * * * * *

You can download a PDF:

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

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

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

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

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

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

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

* * * * *

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

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

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

Asset Pricing: From Marginalism to High-Dimensional Valuation

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

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

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

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

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

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

Thick Objects: Stocks, Brands, and Movies

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

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

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

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

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

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

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

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

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.