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 through 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 2017 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.
Through LLMs to Rank 5 cognition
With that, it’s time to return (briefly) to Cowen’s text. When he’s concluded his remarks about high-factor machine learning models Cowen moves outward from economics though this, that, and the other and finally lands on the large language models (LLMs) that power chatbots such as Claude, ChatGPT, Gemini, and Grok (pp. 106).
I think he’s too credulous about the standard industry story of LLMs being a sharp break from the past, and I think he should have addressed the kinds of criticism offered by Gary Marcus and others. However, I’ve already addressed these issues in an earlier post, Correcting Cowen’s misleading presentation of large language models, and in a working paper, The Origins of LLMs – A long tectonic subduction event finally producing a visible volcanic eruption in November 2022. I see no reason to summarize much less repeat those arguments here. I agree with his general point. We’re moving to a whole new world in which AI will play a central role. Just what that role, or roles, will be, though, is open to question. But I’m sure that I think about that differently than Cowen does.
At various points in these notes made reference to the theory of cognitive ranks that David Hays and I developed in the 1990s, particularly in the posts, Botanical classification and the theory of evolution, and LLMs, the nature of language as a computational object, and arithmetic as a specialized language. In our original article, The Evolution of Cognition (1990), we argued for four cognitive architectures, each enabled by a specific capacity as follows:
Rank 1: speech
Rank 2: writing
Rank 3: calculation
Rank 4: computation
Note that while speech belongs to humankind’s native endowment, the other three require external support supplied by cultural inventions: 2) writing implements and surfaces, 3) notation conventions and devices for calculation (e.g. the abacus, slide rule), and 4) digital computers (mostly, though there are analog machines).
When we were developing those ideas we thought in terms of five ranks, but decided to drop the fifth rank when it became time to publish. We didn’t know how to conceptualize it; we had intuitions, if you will, but no way to isolate it as a specific conceptual entity. That is changing.
Consider the argument I’ve been making in this series of notes. Early in this series I argued that marginalism is a Rank 4 idea, along with thermodynamics and biological evolution. If that is so, and if marginalism is now slipping into the past to be replaced by something else, which Cowen has argued in this chapter, then it follows that it must be heading for some variety of Rank 5 cognition. I have decided that we might as well say that Rank 5 cognition is supported by technology for intelligence. Which is to say, I do not think that intelligence, human or artificial, is reducible to (the principles of) computation. I’ve got the beginnings of an argument on that point, but this is not the place to reprise it. I’ve done that in a recent post, Three Principles of Intelligence (That Aren't Principles of Computation) [Rank 5 cognition].
Thus it is not only economics that’s in need of and in search of fundamentally new ideas (and intuitions). Every discipline is in that situation. Let a thousand flowers bloom.
Chaos and order
Here is how Cowen finishes the monograph:
Whither human intuition, and also economic intuition?
Contemporary economics was born from the Marginal Revolution, but if we are forecasting its future, right now it is hard to see even one move ahead, much less the correct one.
There is however a slightly scarier version of this story yet. Maybe our intuitions about the world, including the economic world, were never so strong in the first place. Maybe we put so much value on “intuitive” results, in 20th century microeconomics, as a kind of cope and also security blanket, to make up for this deficiency. But our intuitions, even assuming them to be largely correct, always were just a small corner of understanding, swimming in a larger froth of epistemic chaos. And now the illusion has been stripped bare, and the true complexities of economic reasoning are being revealed.
As Arnold Kling would say, “Have a nice day.”
In that context I want to circle back to a post I made at the end of March, Welcome to Reality, Tyler; the AI’s got your number: Further Notes on Revolution, Marginal and AI, where I discussed the ideas of Stephen Wolfram and Ilya Prigogine.
Back in 1977 Ilya Prigogine won the Nobel Prize for chemistry for his work on dissipative structures and non-equilibrium thermodynamics, processes where structure can emerge from chaos – chaos in the technical mathematical sense, not the common sense notion of plain old disorder. He went on to collaborate with Isabelle Stengers in a more philosophical work, Order Out of Chaos (1984), suggesting that the possibility of local regimes of order depends on a large matrix of chaos. Then, at the turn of the millennium Stephen Wolfram published A New Kind of Science (2002) in which he argued, in effect, that the world consists of islands of computationally reducible phenomenon swimming in a larger froth of computationally irreducible phenomena, a theme he reprised in 2024 in a long blog post, Can AI Solve Science? There he remarks:
So what should we expect for AI in science going forward? We’ve got in a sense a new—and rather human-like—way of leveraging computational reducibility. It’s a new tool for doing science, destined to have many practical uses. In terms of fundamental potential for discovery, though, it pales in comparison to what we can build from the computational paradigm, and from irreducible computations that we do. But probably what will give us the greatest opportunity to move science forward is to combine the strengths of AI and of the formal computational paradigm.
I like that. I can live with it. Perhaps Cowen can as well.
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