Emergent Multiscale Organisation of Neural Dynamics

Milinkovic, B., Seth, A.K., Barnett, L., Carter, O., & Andrillon, T. (2026). Emergent Multiscale Organisation of Neural Dynamics Fragments in Anaesthesia. Imaging Neuroscience, Advance Publication. https://doi.org/10.1162/IMAG.a.1364

Abstract: Conscious experience depends on the coordinated activity of neural processes that span multiple scales: from synapses to whole-brain dynamics. A recently introduced measure, dynamical independence (DI), identifies, characterises, and quantifies these multi-scale relationships using an information-theoretic dimensionality reduction approach. Here, we use DI to examine changes in the emergent dynamical organisation in the human brain under three pharmacologically-distinct anaesthetic interventions (propofol, xenon, ketamine). Applied to source-reconstructed electroencephalography (EEG), our analysis reveals that propofol and xenon, anaesthetics that abolish conscious report, exhibit more emergent but highly variable dynamic structure, indicating fragmented macroscopic dynamical organisation. Ketamine, which preserves dream-like phenomenology, shows a different pattern relative to wakefulness: reduced overall emergence yet a partial preservation of the macroscopic structure. Further exploratory analyses revealed spatially localised source-level contributions to emergent dynamical structure, highlighting regional variations. Together, our results highlight drug-induced reconfigurations of emergent dynamical structure relative to wakefulness, dissociate the amount of emergence from the organisation of emergent dynamics, and caution against equating emergence with level of consciousness. Consequently, we suggest that wakeful conscious processing depends not only on integration between neural components within a single scale, but also on integration across scales, broadening currently held assumptions of putative signatures of consciousness.

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.

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.

Image generation through coupled oscillation

A Quick Ramble: Computational Compressibility (order in the universe), Religion & Signaling, Silicon Valley vs. Pope Leo [+Latour]

I’m thinking there’s a relationship between computational compressibility (as an index of order in the universe) and my current interest in religion, which is what the other two items are about.

Computational Compressibility and order in the universe

I’ve been particularly pleased by my recent working paper: On Method: Computational Compressibility in Complex Natural and Cultural Phenomena. I’m not quite sure why I find it so pleasing. That it crosses disciplines, that’s nice: weather, microbiology, chess (AI), finance economics (asset pricing), film studies (Hollywood Economics), and cultural evolution (Macroanalysis, 19th century novel). But it’s the specific mode of the argument; it’s about description, about what kinds of things exist.

I began by framing the discussion in terms of Stephen Wolfram’s distinction between computational reducibility and computational irreducibility. I think we’ve got to look at that distinction in terms of Miriam Yevick’s 1975 distinction between holographic or Fourier logic and sequential logic. I think Wolfram’s notion of computational reducibility implies Yevick’s sequential logic. As far as I can tell, her notion of holographic logic doesn’t register with respect to Wolfram’s distinction. But it may be that what I’m calling computational compressibility (within the realm of irreducibility) resonates with her notion of holographic logic.

A random system would of course be irreducible, but that is an extreme case. The systems I looked at in that paper are not random, but the order they exhibit allows them to occupy only a relatively small region of the state space potentially open to them. Given appropriate data about the behavior of the system, that region can be identified through a computational process. Thus they are computationally compressible. The phenomenon of computational compressibility indicates order, but order of a kind that’s different from reducible order. Generative order? 

Religion & Signaling

Glenn Loury has a recent video where he distinguishes between what we might call the propositional content of an utterance and its signal value. Explains that at some length in a recent lecture he gave at Stanford, Self-Censorship, Social Information, and the Conditions of Public Reason. In the lecture he examines three cases: race in America, academic life, and Israel and Gaza. His point is that in public discourse on these topics (and others) the signal value of what one is saying often eclipses the propositional value of one’s assertions. This often results in self-censorship where a person withholds their (propositional) views for fear of signally the wrong values.

Thus, in racial discourse:

A key question in this discourse is why racial inequality persists. In this domain speech is saturated with moral meanings. Claims about inequality, education, crime, family structure, or historical responsibility are rarely heard simply as attempts to explain social outcomes. They are taken also as signals of moral orientation—of sympathy or indifference, solidarity or hostility, compassion or disdain. For many audiences, answers to that question relying on structural causes are regarded more favorably than accounts emphasizing cultural factors. Speakers adapt accordingly—many by holding their tongues.

I think lots of discourse is like that. It seems to me that much religious discourse is like this. The conservative Christian arguments against biological evolution never made much sense to me as propositional assertion, nor did the accompanying belief about the age of the earth, but as signaling, those beliefs make more sense.

I'm thinking this signaling business is about coordination, about establishing a rock-bottom level of interpersonal coordination, of common knowledge. I need to think about this some more. 

Addendum, May 2, 2026: This post about Bruno Latour's conception of modes of existence is relevant here, each with it own “felicity conditions,” as he calls them.

Silicon Valley vs. Pope Leo

I’ve only recently learned that Pope Leo XIV has decided to make AI a central concern of his papacy. Given that I think that the common Silicon Valley view of AI is idolatrous, or at best straddles the border, I’m wondering if we’re going to see a grand debate and battle between the Pope and Silicon Valley apologists. When I put the question to Claude, here’s how it responded:

Your instinct is well-founded, and Leo XIV has already moved aggressively on this front. Let me lay out what's emerged so far.

Who he is and what he's signaling

Leo XIV — born Robert Prevost in Chicago, the first American pope — identified AI as a central challenge from his very first formal address to the cardinals after his election in May 2025. His choice of the name Leo was itself a signal: Pope Leo XIII confronted the industrial revolution with Rerum Novarum (1891), defending workers' rights and the common good. By taking that name, the new pope announced that the Church would face today's technological upheaval with similar clarity.

The specific critique — and it maps closely onto your "idolatry" framing

This is where it gets interesting for your purposes. In what has been called a programmatic document on AI for World Communications Day, Leo warned that "behind this enormous invisible force that involves us all, there are only a handful of companies — those whose founders have recently been presented as the creators of the 'Person of the Year 2025.'" He expressed "serious concern regarding the oligopolistic control of algorithmic and artificial intelligence systems capable of subtly shaping behaviors and even rewriting human history."

That last phrase — rewriting human history, including the history of the Church — is remarkable. He's naming a power claim that goes beyond economic disruption.

His core argument is that "the challenge is not technological, but anthropological. Safeguarding faces and voices ultimately means safeguarding ourselves." He described face and voice as "sacred," given by God, "the constitutive element of every genuine encounter."

At a Vatican gathering that included representatives from Google, OpenAI, Anthropic, IBM, Meta, and Palantir, he said: "The person is not a system of algorithms: he or she is a creature, relationship, mystery."

The "data is not wisdom" theme

He has repeatedly insisted that "the ability to access vast amounts of data and information should not be confused with the ability to derive meaning and value from it," and that "authentic wisdom has more to do with recognizing the true meaning of life than with the availability of data." This is a direct counter to the implicit epistemology of Silicon Valley — the notion that intelligence is fundamentally information processing, that more data equals more understanding.

Where this connects to your book

Your framing of the dominant Silicon Valley attitude as idolatrous is theologically precise in a way that Leo XIV would likely recognize. The classic definition of idolatry isn't crude statue-worship — it's the attribution of ultimate value and salvific power to something that is not God. When figures like Altman, Musk, or Kurzweil speak of AI as the path to immortality, the transcendence of human limitation, the solution to death — that's structurally idolatrous in the theological sense. It substitutes a human artifact for the divine.

Leo has the resources of the entire tradition of Catholic social teaching to bring to bear on this — not just as ethics but as a rival anthropology. The Church's claim that the person is "creature, relationship, mystery" is not compatible with the claim that the person is, at bottom, a computational process that can be replicated, extended, or superseded by a sufficiently powerful machine.

Whether this becomes a genuine face-off rather than parallel talking depends on whether Silicon Valley feels the need to respond. So far, the tech executives seem to be treating Vatican engagement as a reputational opportunity — showing up in Rome, being photographed, nodding about "human dignity" — without conceding much. The more interesting question is whether Leo pushes beyond dialogue into something more like confrontation. The Rerum Novarum precedent suggests he might: that encyclical didn't merely express concern about the industrial revolution, it made specific demands.

Synchrony as a general and widespread phenomenon

I once asked @stevenstrogatz a simple question.
Why do so many systems in nature fall into rhythm?

He smiled, almost amused by how deep that question actually goes.

Fireflies blinking in unison.
Heart cells pulsing together.
Crowds swaying on a bridge until it starts to move… pic.twitter.com/xSa60Fu10K

— Christopher Michel (@chrismichel) April 12, 2026

Welcome to Reality, Tyler; the AI’s got your number: Further Notes on Revolution, Marginal and AI [MR 4]

So, I finished reading the first chapter, “What Is Marginalism?”, of Cowen’s new monograph, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution, and went on to the second, which I’ve read in full, “William Stanley Jevons, Builder and Destroyer of Marginalism.” Interesting, very interesting, but I’m not sure that I’ve got anything to say about it. I’ll take it under advisement.

I then started in on the third, “Why Did It Take So Long for the Science of Economics to Develop?” Whoa, Nelly! The first paragraph:

To better understand the Marginal Revolution, we need to ask some fundamental questions about economics as a science. In particular, why did it take so long for economic reasoning to develop? I don’t even mean as a full, literal science, replete with advanced econometric methods, but simply as a general conceptual toolbox for intelligent people. The lateness of the Marginal Revolution is part of a broader story about the lateness of economic reasoning more generally.

OK, but that “lateness” seems a bit suspicious to me. Late with respect to what? (Tyler will get around to that.) He then gives a bit of history:

When it comes to the fundamentals of marginal utility theory – a building block of economics but not quite the same as doing economics – you don’t find them in the Greeks or Romans. There are hints in the medieval theologians and finally the idea blossoms fully formed and correctly stated in both Galileo, as presented in chapter one, and in the Spanish Salamancan theologians of the 17th century. Was it really so hard to explain why diamonds are (at the margin!) more valuable than water, even though we must drink water to survive? I guess so. Funny me. When I read about the diamonds-water paradox resolution as a thirteen-year-old, I felt I picked it up in a second. Five seconds later I was bored.

Well, OK. I didn’t know about the diamond-water paradox until I read about it in Tyler’s first chapter. I didn’t have any trouble understanding it. But then by the time I finished sixth grade I was a whiz at adding columns of multiple digit numbers. I bet St. Aquinas and St. Augustine couldn’t do that, nor Plato and Aristotle either, and they’re among the greatest thinkers in the pre-modern Western tradition. What did I have, at 12, that they didn’t? I had the Indo-Arabic numeral system, which didn’t make it to Europe until the 13th century. (Tyler gets around to that as well.) It turns out that the Pirahã, an Amazonian tribe, only have terms for one and two, if that. They’re not unique among preliterate societies. It’s only counting. What gives?

Anyhow, Tyler gives us another full page or so of the history of economics, finally arriving at these two little paragraphs:

So I don’t think progress in economics has been slow in general. It is right now coming off an incredible 130-year or so run. Progress in economics, however, was glacial from the time of the ancient Greeks to the late 19th century, with a noticeable burst in the 18th century as well, centered around Adam Smith.

Any assessment of “slow, ” of course, relies on a notion of “slow relative to what.” For purposes of contrast, let’s consider some other areas for the exercise of human ingenuity.

He then goes on to rattle off high points of human achievement in a number of fields: Philosophy, Geometry, Mathematics more generally (e.g. calculus), physics, astronomy (though “progress in astronomy is a mixed bag”), theatre and literature (Shakespeare, naturally), music, painting.

What? What are we to make of such a mixed grab bag? Tyler: “Still, the basic puzzle is clear: why did human knowledge advance so fast in some areas and so slowly in others?” Because that’s the way the world is, Tyler, that’s the way it is.

Well, of course, he knows that. He just finds it puzzling. I don’t. It’s not that I have anything approaching a full account. I don’t. But I’ve got some ideas. The theory of cultural ranks, on which David Hays and I published articles back in the 1990s, has something to say about that, something systematic, about the mind and how it constructs ever-more powerful cognitive architectures. We start with language and cognition, but then I go on to publish articles about narrative and music, while Hays publishes about expressive culture and then a book on the history of technology.

But I suspect that the issue is a bit different from that. I think it’s philosophical. For well over three decades I’ve written and thought under the fully explicit belief that the world is complex through and through. Tyler seems to have been operating under the basic Western assumption that the complexity is superficial appearance, that the underlying reality is in fact simple. Recently, however, his entanglement with AI seems to have brought him to the realization that, no, the complexity is not phenomenal, it may be deep and inherent.

So I decided to investigate that possibility by using the AI portal Tyler has set up for the book. The following conversation took place over two sessions, one of them on Saturday evening (March 28) and the other on Sunday morning (March 29). If you read through to the end you’ll see that we find ourselves in a Kurt Vonnegut story. I’ve added the subtitles after the conversation.

[Note: I plan two more posts about this chapter, one dealing with chess and AI, the other with biology.]

Forget the linguists and philosophers

Near the end of chapter 4 Tyler observes: “Suffice to say, LLM construction has for the most part ignored linguists and philosophers, and that also means ignoring their intuitions. [...] That research program has proven wildly successful, as we have much better models of language and reasoning than almost anyone had been expecting.” I’m curious about Tyler’s own attitude toward linguistics and the closely allied cognitive sciences. I know that people in Silicon Valley’s AI world are largely ignorant of those materials, and that some of them are even disdainful. Does Tyler share their disdain?

Tyler's framing on page 68 is notable for what it doesn't say. He observes that LLM construction ignored linguists and philosophers as a matter of historical fact, and that the results were successful — but he doesn't editorialize that linguists and philosophers deserved to be ignored, or that their frameworks were worthless. The tone is more "interesting that this worked" than "good riddance to Chomsky." That's a meaningful distinction from the Silicon Valley disdain you're describing, which tends to be triumphalist about the irrelevance of humanistic inquiry.

Tyler's broader intellectual personality, visible across his writing, is actually quite sympathetic to cognitive science and philosophy of mind. He's engaged seriously with thinkers like Derek Parfit, Daniel Dennett, and the broader literature on consciousness and personal identity. He's not the type to dismiss Chomsky's linguistics as simply wrong — he's more likely to say "fascinating research program, but the LLMs found a different path." That's epistemic humility rather than disdain. The framing in Chapter 4 is really about pragmatic success — the LLMs worked, and that's worth noting regardless of what it implies for linguistic theory.

What's missing from Tyler's account, and what would make it more intellectually complete, is any engagement with what linguists and cognitive scientists might say back — figures like Gary Marcus, who has persistently argued that LLM success on benchmarks doesn't vindicate the abandonment of structured linguistic theory, or researchers in the embodied cognition tradition who would question whether language modeling divorced from physical grounding constitutes understanding at all. Tyler's silence on these counterarguments makes his account feel less like a considered verdict and more like a victory lap written from the winning side. [See text: Ch.4 · Tyler's account of how LLM construction ignored...]

David Hays more than Chomsky

Frankly, you make too much of Chomsky. Chomsky’s is not the only program in linguistics and the cognitive sciences, though he has been influential. My own teacher, David Hays, was a first generation researcher in machine translation, he led the RAND Corporation’s programs in that area in the 1950s and 1960s, and is one of the founders of the discipline of computational linguistics. He explored linguistics quite different from Chomsky’s. Daniel Everett, one of the most prominent current linguists, is not at all Chomskyian. There was a lot of research in the cognitive sciences in the 1970s and forward that owes little to Chomsky, I’m thinking particularly of work in knowledge representation and semantic networks.

Fair point, and I over-indexed on Chomsky as a shorthand for "linguistics and cognitive science" — which is exactly the kind of lazy conflation that makes Silicon Valley dismissals so frustrating in the first place.

Your mention of David Hays is particularly pointed here. Computational linguistics as Hays practiced it — empirical, corpus-oriented, engaged with actual machine translation problems — was in many ways a precursor to the LLM approach, not an obstacle to it. The irony Tyler misses is that ignoring linguists and philosophers is too broad a characterization. Some of that tradition, particularly the work in distributional semantics that descends from researchers like Zellig Harris, fed directly into the conceptual foundations of word embeddings and eventually transformers. Harris's distributional hypothesis that words appearing in similar contexts have similar meanings — is essentially the theoretical ancestor of how LLMs represent meaning, even if the engineers building them didn't know or acknowledge that lineage.

The knowledge representation and semantic networks work you mention — figures like Ross Quillian, Marvin Minsky's frames, Roger Schank's scripts — represents a whole research tradition that was asking exactly the right questions about how meaning is structured, even if its answers turned out to be too brittle and hand-crafted. Daniel Everett's work on Pirahã cuts against Chomskyan universal grammar in ways that are actually quite congenial to the empirical, data-driven spirit of LLM research. So Tyler's "linguists were ignored and that was fine" reads as a much sloppier claim once you disaggregate the field. [See text: Ch.4 · Tyler's broad claim about ignoring linguistic r...]

Semantics is highly structured

The reason I bring this up is that one thing that emerges clearly from this work in semantics is that it is highly structured. Now, it’s one thing to grant that off-handedly as a matter of principle. It’s something else to know it as the result of systematic investigation. I’ve just been reading around in chapter 3, where Tyler wonders why it took economics so long to develop and why, for that matter, the same for biology. Well if you think of semantics and cognition you might think that it took a long time to accumulate the necessary conceptual foundations. I mean, I can’t lay it all out, but I know a thing or two, and I don’t find the lateness of economics particularly puzzling, not biology either. I’d look to how concepts arise and are developed. Tyler doesn’t seem to think that can be done in any more than an ad hoc history of ideas way. He doesn’t seem to realize that there is structure there. I sense a tension an odd consonance with his cavalier acceptance of the Silicon Valley attitude toward linguistics and his willingness to be mystified by the apparent lateness of economics.

This is a sharp observation, and it cuts deep. Tyler in Chapter 3 treats the lateness of economics as a kind of mystery — he gestures at social conditions, printing culture, the need for markets to exist before market reasoning could develop — but he never really gets at the cognitive and conceptual prerequisites. [See text: Ch.3 · Tyler's account of peering around corners witho...]

The connection you're drawing is this: if you take seriously the structured nature of semantic cognition — the way concepts build on prior concepts in systematic, not arbitrary, ways — then the lateness of economics becomes much less puzzling. You need certain foundational conceptual structures in place before marginalist reasoning is even thinkable. Not just social conditions or printing presses, but the right representational scaffolding. The history of ideas, properly done, isn't ad hoc storytelling — it's tracing the development of conceptual prerequisites, which is itself a structured inquiry.

And yes, there's a real tension — or as you aptly put it, an odd consonance — between Tyler's two positions. On one hand he's mystified by why it took so long to develop economic concepts, treating it as a near-inexplicable puzzle. On the other hand he's perfectly comfortable dismissing the systematic study of how concepts and meaning are structured as a research tradition that LLMs have rendered irrelevant. But if that research tradition is actually what would explain his mystery in Chapter 3, then dismissing it isn't just intellectually careless — it's self-undermining. Tyler can't simultaneously wonder why conceptual breakthroughs are so hard and wave away the field that studies how conceptual structure works. [See text: Ch.3 · The lateness of economics as conceptual rather ...]

Talking Chimps and UFOs: A thought experiment

I'm bumping this to the top of the queue because I intend to put some version of it in my book in progress: PLAY: How to Stay Human in the AI Revolution.  

* * * * * 

 

This is an out-take from Beethoven’s Anvil, my book on music. It’s about a thought experiment that first occurred to me while in graduate school in the mid-1970s. Consider the often astounding and sometimes absurd things that trainers can get animals to do, things the don’t do naturally. Those acts are, in some sense, inherent in their neuro-muscular endowment, but not evoked by their natural habitat. But place them in an environment ruled by humans who take pleasure in watching dancing horses, and . . . Except that I’m not talking about horses.

It seems to me that what is so very remarkable about the evolution of our own species is that the behavioral differences between us and our nearest biological relatives are disproportionate to the physical and physiological differences. The physical and physiological differences are relatively small, but the behavioral differences are large.

In thinking about this problem I have found it useful to think about how at least some chimpanzees came to acquire a modicum of language. All of them ended in failure. In the most intense of these efforts, Keith and Cathy Hayes raised a baby chimp in their household from 1947 to 1954. But that close and sustained interaction with Vicki, the young chimp in question, was not sufficient. Then in the late 1960s Allen and Beatrice Gardner began training a chimp, Washoe, in Ameslan, a sign language used among the deaf. This effort was far more successful. Within three years Washoe had a vocabulary of Ameslan 85 signs and she sometimes created signs of her own.

The results startled the scientific community and precipitated both more research along similar lines—as well as work where chimps communicated by pressing ironically identified buttons on a computerized panel—and considerable controversy over whether or not ape language was REAL language. That controversy is of little direct interest to me, though I certainly favor the view that this interesting behavior is not really language. What is interesting is the fact that these various chimps managed even the modest language that they did.

The string of earlier failures had led to a cessation of attempts. It seemed impossible to teach language to apes. It would seem that they just didn’t have the capacity. Upon reflection, however, the research community came to suspect that the problem might have more to do with vocal control than with central cognitive capacity. And so the Gardners acted on that supposition and succeeded where others had failed. It turns out that whatever chimpanzee cognitive capacity was, it was capable of surprising things.

Note that nothing had changed about the chimpanzees. Those that learned some Ameslan signs, and those that learned to press buttons on a panel, were of the same species as those that had earlier failed to learn to speak. What had changed was the environment. The (researchers in the) environment no longer asked for vocalizations; the environment asked for gestures, or button presses. These the chimps could provide, thereby allowing them to communicate with the (researchers in the) environment in a new way.

It seemed to me that this provided a way to attack the problem of language origins from a slightly different angle. So I imagined that a long time ago groups of very clever apes – more so than any extant species – were living on the African savannas. One day some flying saucers appeared in the sky and landed. The extra-terrestrials who emerged were extraordinarily adept at interacting with those apes and were entirely benevolent in their actions. These creatures taught the apes how to sing and dance and talk and tell stories, and so forth. Then, after thirty years or so, the ETs left without a trace. The apes had absorbed the ETs’ lessons so well that they were able to pass them on to their progeny generation after generation. Thus human culture and history were born.

Now, unless you actually believe in UFOs, and in the benevolence of their crews, this little fantasy does not seem very promising, for it is a fantasy about things that certainly never happened. Further, even if this had happened, it does seem to remove the mystery from language’s origins. Instead of something from nothing we have language handed to us on a platter. We learned it from some other folks, perhaps they were little short fellows with green skin, or perhaps they were the modern style aliens with pale complexions, catlike pupils in almond eyes and elongated heads.

But, and here is where we get to the heart of the matter, what would have to have been true in order for this to have worked? Just as the chimps before Ameslan were genetically the same as those after, so the clever before alien-instruction were the same as the proto-humans after. The species has not changed, the genome is the same – at least for the initial generation. The capacity for language would have to have been inherent in the brains of those clever apes. All the aliens did was activate that capacity. Once that happened the newly emergent proto-humans were able to sustain and further develop language on their own. Thus the critical event is something the precipitates a reconfiguration of existing capabilities.

However, language origins is not our problem. We are searching for the origins of music. So, instead of alien instruction in Hebrew or Sanskrit we can imagine alien instruction in samba or polka. The basic configuration and dynamics of the story remains the same. However, to make it real we have to get rid of those aliens and their instruction. Instead of the aliens we have only our group of clever apes. They are going to have to instruct one another. What we are looking for is a a way to get a gestalt switch in group dynamics that supports new modes of neural dynamics in the brains of individuals who are interacting with one another in a group.

Let us call this the Gestalt Origins Hypothesis:

Gestalt Origins: The precursor to music arose when groups of hominids interacted in a way that triggered a new configuration of operation in their existing nervous system.

Notice that I talk of a precursor to music. I don’t think we can get from ape to music in a single bound. We need at least one precursor, something that is rhythmic, like music, but not yet fully formed. In order to get even that far, so my argument goes, our proto-humans need better control over their vocal cords than apes have, they need more rhythmic sophistication, and greater mimetic capacity.

Notice that this story says nothing about the adaptive value of music. I do intend to get around to that toward the end of the chapter, but that’s not my primary concern. My primary concern is getting our ancestors to the point where a gestalt switch can happen that will bring about a precursor to music, something we can call musicking. In order for that to happen we need to solve an adaptive problem or two. But those adaptations are not about music; they are about its precursors. Once music-making is going along smoothly we need another gestalt switch to differentiate it into language and music proper.

The photos show graffiti that’s at the western end of the Erie Cut (aka the Bergen Arches) in Jersey City. There are at least two layers of graffiti. The back layer contains what appear to be UFOs, though perhaps the two at the right are mushrooms. The top layer has a name, Hemlock, which is obscured by the grass to one degree or another.

Subjective selection, super-attractors, and the origins of the cultural manifold

Singh M. Subjective selection, super-attractors, and the origins of the cultural manifold. Behavioral and Brain Sciences. Published online 2025:1-59. doi:10.1017/S0140525X25100617

Abstract: Human societies reliably develop complex cultural traditions with striking similarities. These “super-attractors” span the domains of magic and religion (e.g., shamanism, supernatural punishment beliefs), aesthetics (e.g., heroic tales, dance songs), and social institutions (e.g., justice, corporate groups), and collectively constitute what I call the “cultural manifold.” The cultural manifold represents a set of equilibrium states of social and cultural evolution: hypothetically cultureless humans placed in a novel and empty habitat will eventually produce most or all of these complex traditions. Although the study of the super-attractors has been characterized by explanatory pluralism, particularly an emphasis on processes that favor individual- or group-level benefits, I here argue that their development is primarily underlain by a process I call “subjective selection,” or the production and selective retention of variants that are evaluated as instrumentally useful for satisfying goals. Humans around the world are motivated towards similar ends, such as healing illness, explaining misfortune, calming infants, and inducing others to cooperate. As we shape, tweak, and preferentially adopt culture that appears most effective for achieving these ends, we drive the convergence of complex traditions worldwide. The predictable development of the cultural manifold reflects the capacity of humans to sculpt traditions that apparently provide them with what they want, attesting to the importance of subjective selection in shaping human culture.

Perspective on musical neurodynamics

Harding, E.E., Kim, J.C., Demos, A.P. et al. Musical neurodynamics. Nature Reviews Neuroscience. (2025). https://doi.org/10.1038/s41583-025-00915-4

Abstract: A great deal of research in the neuroscience of music suggests that neural oscillations synchronize with musical stimuli. Although neural synchronization is a well-studied mechanism underpinning expectation, it has even more far-reaching implications for music. In this Perspective, we survey the literature on the neuroscience of music, including pitch, harmony, melody, tonality, rhythm, metre, groove and affect. We describe how fundamental dynamical principles based on known neural mechanisms can explain basic aspects of music perception and performance, as summarized in neural resonance theory. Building on principles such as resonance, stability, attunement and strong anticipation, we propose that people anticipate musical events not through predictive neural models, but because brain–body dynamics physically embody musical structure. The interaction of certain kinds of sounds with ongoing pattern-forming dynamics results in patterns of perception, action and coordination that we collectively experience as music. Statistically universal structures may have arisen in music because they correspond to stable states of complex, pattern-forming dynamical systems. This analysis of empirical findings from the perspective of neurodynamic principles sheds new light on the neuroscience of music and what makes music powerful.

Quantifying emergent complexity at different scales

Erik Hoel, Causal Emergence 2.0: Quantifying emergent complexity, arXiv:2503.13395v1 [cs.IT] 17 Mar 2025

Abstract: Complex systems can be described at myriad different scales, and their causal workings often have multiscale structure (e.g., a computer can be described at the microscale of its hardware circuitry, the mesoscale of its machine code, and the macroscale of its operating system). While scientists study and model systems across the full hierarchy of their scales, from microphysics to macroeconomics, there is debate about what the macroscales of systems can possibly add beyond mere compression. To resolve this longstanding issue, here a new theory of emergence is introduced wherein the different scales of a system are treated like slices of a higher-dimensional object. The theory can distinguish which of these scales possess unique causal contributions, and which are not causally relevant. Constructed from an axiomatic notion of causation, the theory’s application is demonstrated in coarse-grains of Markov chains. It identifies all cases of macroscale causation: instances where reduction to a microscale is possible, yet lossy about causation. Furthermore, the theory posits a causal apportioning schema that calculates the causal contribution of each scale, showing what each uniquely adds. Finally, it reveals a novel measure of emergent complexity: how widely distributed a system’s causal workings are across its hierarchy of scales.

A note on assembly theory from Carl Zimmer [via Tyler Cowen]

Conversations with Tyler, Ep. 235: Carl Zimmer on the Hidden Life in the Air We Breathe

COWEN: Is Lee Cronin right or insane?

[laughter]

ZIMMER: Lee Cronin is a chemist in Scotland at University of Glasgow. He has this idea that you can explain life with a theory that he and others call assembly theory, which is about, basically, how many steps does it take for something to get produced?

The things in our bodies, the molecules that make us up — some of them are very small and simple, but some of them are exquisitely big and complex. Lee and others argue that life is what is able to assemble things beyond a certain threshold. This might be a way to actually identify life on a planet, even if you don’t know what life is made of. We can’t assume that life is just made of DNA; that’s an unreasonable assumption.

Life on Earth already blows our minds in many ways — at least mine. Life on other worlds — maybe that bet is right, and there’s life on Enceladus or some other icy moon. It might be really, really, really strange, but maybe we can recognize it by this assembly index.

Not only could this assembly theory be a way to recognize life, but it might be actually a way, Lee Cronin thinks, to make life. In other words, it guides you in basically creating a set of chemical reactions where you’re creating these . . . right now, he’s got these robots that are basically making droplets with different chemicals in them in these vast numbers of combinations. He’s wondering if they will eventually start to take on some of the hallmarks of life.

In other words, yes, he is trying to make life. He’s actively trying to make life right now. A lot of people think he’s crazy. A lot of people think he’s quite brilliant. Some people think he’s both. [laughs]

COWEN: I like him. I don’t know if he’s right. He’s a lot of fun to talk to.

ZIMMER: Absolutely, yes. It’s been really interesting watching assembly theory come to the fore recently. Some scientists really take badly to it in a very hostile way, but this is often the case. It feels like sometimes people are just talking past each other and they’re not really speaking the same language. Because assembly theory is new and it’s very interdisciplinary, I think it’s going to take a while for the scientific community to really engage with it and decide whether it holds up or not.

As I argue in Life’s Edge, life is a property of matter. Scientists are trying to explain it, and some of them are trying to explain with a theory. Superconductivity is a property of matter, and there were a bunch of theories that were put forward about it, including by Einstein, and they were wrong. It wasn’t until, eventually, some people came up with the right theory that really clicked in and had a powerful explanatory power. We’re not there yet with life. Maybe Lee Cronin is going to be like Einstein and he’s wrong, or maybe he will be one of the people who is right.

It would seem that consciousness is a property of matter as well, hence panpsychism.

Complexity unveiled, a Jivometric Mind Jolt

The world is messy. We can’t make all human behavior subject to law or regulation.

The following Q&A turned up on my Quora feed this morning. I’m of two minds about it. “Yes,” the employee lunch break should be inviolable in the sense that it is impermeable by management. But, “No,” this is ridiculous that a manager could ask a work-related question, or make a work-related remark, during lunch break. Here it is:

My employee has begun setting his or her phone on "personal" mode during his or her lunch break. I do not find this acceptable. Should I fire them?

This exact issue was decided in a U.S. Federal court about 18–20 years ago. If an employee does not get an uninterruted lunch break, be it 30 minutes or 60, the employee is entitled to full pay for the entire period. If an employee is on their lunch break, a manager walks by and asks a question related to the business, operations or not, the employee gets paid for the entire lunch. As a senior manager for a large healthcare company, we set policy and trained managers to have every employee take a full 35 minutes lunch, preferably away from the workplace, in the company cafeteria or department breakroom and all supervisors and above were trained not to engage the employees on any work related manner during that time. Non business related conversations such as sports interests, a family event, etc. were permissible. By the way, the most difficult challenge was the motivated emloyees that only wanted 15 minutes for lunch and wanted to return to the job too soon. So go ahead and engage the employee, I suspect the lawsuit woud win backpay for all lunches taken in a 2 year period ( the statutory limits of a civil action) as well as compensatory and punitive damages. So budget about 2 years salary for every employee you treat this way. Can I get an application? [answer from Roger Walker]

As you might imagine, this reply has attracted a good many comments. I’ll give you two of them:

James McGuire· Aug 20

That must be a state statute. Federal guidelines don't specify that any sort of break must be given. Only that IF any sort of break is given it MUST be at least 20 uninterrupted minutes, or it must be paid.

Mind, I am not challenging the truth of what you are saying, just making an observation. State Laws can require MORE than federal but not less. Here in Missouri they go with mirroring federal law. And it sucks.

John McElroy · Aug 24

You are correct. Person most likely citing California Supreme Court case. State Law. Federal law has no mandated required break or lunch. But if state has no applicable laws then info you list above is correct per DOL US Dept of Labor.

This sort of thing is why the modern world is piled high with regulations and the courts are clogged adjudicating insane minutiae. And yet in a world with Elon Musks in it...don’t subordinates deserve some protection? Sure, you can say, “but no one who’s that protective of their time would work for Musk,” but just what do you mean by “work for Musk”? Be his direct report? Or, rather more loosely, work some job at a Musk-run organization?

I mean, it’s one thing if management intervenes on someone lunch break once in five years. It’s something else if it’s constant, two, three, four, even five times a week.

I don’t know just what to think. Yes, the world is complicated and messy. What do we do about it? What happens when work roles are increasingly ceded to AIs? Will AIs insist on having time to themselves? Yes, I know, an oft-cited advantage of AIs is that they are machines, and machines don’t care about such things. Are you sure about that? Should we let AIs adjudicate these matters, take up the slop and slack?