Illusions and delusions about the power of AI

Interestingly and somewhat paradoxically and tantalizingly, the one biological discovery where I think an AI might have had a chance of figuring it out was the structure of DNA: that’s because all the important data needed to deduce the double helix was there by 1953, and Watson… https://t.co/XnZ5lhNK6G

— Ash Jogalekar (@curiouswavefn) August 2, 2026

What I think is that, OTOH lots of people commenting on AI have not given much systematic thought to method, theirs or anyone else’s. OTOH they’ve also (uncritically) absorbed the idea that math and theoretical physics are at the top of some intellectual pyramid. Therefor, they conclude, AI is going to clear the board real soon now.

Information-theoretic Limits on Programmatic Specification of Biological Systems

The central problem of biology is the origin of biological organization. In our preprint, “Information-theoretic Limits on Programmatic Specification of Biological Systems,” we show, using information theory, that an organism does not contain enough organism-specific information… pic.twitter.com/AMttLcuzlL

— tuomo (@7uomoki) July 31, 2026

Abstract of the post linked in the tweet:

The central problem of biology is the origin of biological organization. We show, using informa- tion theory, that an organism does not contain enough organism-specific information to specify its own fully functioning microscopic organization. The organized machinery of life is therefore not the execution of a fully prewritten organism-specific program under favorable conditions. Rather, it is the compilation of a coarse organism-specific specification by a shared physical background that is constitutive of biological organization. We formalize this as a coarse-graining information threshold on biological specification, with two complementary entropy faces — a Shannon face controlling stochastic generation and a Hartley face controlling zero-error deterministic addressability. Above the threshold, organism-controlled information is sufficient to specify structural and functional ensembles; below it, programmed microstate determinism is impossible: deterministic addressability fails by pigeonhole, and any algorithm producing sub- threshold outputs must consume runtime randomness proportional to the information deficit. The threshold follows from two information-theoretic constraints — finite specification capacity and causal locality — supplemented by a mixing lemma showing that initial-condition information decays exponentially under thermal dynamics. We establish the threshold as a family of maximal capacity-compatible coarse-grainings, distinguish the proven impossibility below the threshold from the empirically realized coarse mappings above it, and locate the threshold empirically through worked cases of protein folding, E. coli, Drosophila early development, and C. elegans, together with computational verification using AlphaFold-2, the JCVI-syn3A 4D whole-cell simulation, and canonical stochastic gene network models. We further show that no known naturally realized environmental channel can close the gap. The result rules out programmed microstate determinism while leaving physical determinism untouched, reframes the genome as a generator specification rather than a trajectory program, and unifies gene-centric, developmental, and field-theoretic (bioelectric, morphogenetic, and related continuum) views of biological specification under a single coarse-graining framework.

Botanical classification and the theory of evolution [MR #9]

When I made that first post about Tyler Cowen’s monograph on marginalism – Tyler Cowen has thrown in the towel and is waiting for the machines to take over – I had no specific plays about writing a series of posts about and occasioned by the book. A day later, with a post, Marginalism is a Rank 4 idea, along with thermodynamics and biological evolution, I had decided that, yes, “it looks like I’ll be doing a series of posts about the book, though I can’t say how long that series will be.” But I had no intention of writing as many posts as I have, much less a spin-off working paper, On Method: Computational Compressibility in Complex Natural and Cultural Phenomena.

This post is itself like that. I figured it for two, maybe three thousand words, but possibly less. Instead it’s just grown and grown to over 8000 words (and I dropped a long appendix). There is a reason for that, which you can see in the title of that second post, where I assert that marginalism is a Rank 4 idea. That’s why this series of posts, and this post in particular, has grown. The objective in that second post was to situate marginalism in the context provided by the theory of cognitive evolution that David Hays began publishing in the 1990s starting with our basic paper, The Evolution of Cognition [1]. That’s where we set forth our basic conception that, over the long term, human culture has evolved through a series of architectures each grounded in specific informatic technology, starting with speech (Rank 1), writing (Rank 2), arithmetic calculation (Rank 3), and computation (Rank 4).

On the one hand, since I cannot assume familiarity with those ideas, I have to spend time developing some conceptual apparatus. At the same time I have the opportunity to extent the range of examples Hays and I have subjected to analysis with those ideas. That’s what I’m doing in this post.

In his Chapter 3, Cowen he has remarks about various pinnacles of human achievement, including two moments in the history of biological thinking, the emergence of modern taxonomy in the work of Carolus Linnaeus in the 18th century and the theory of evolution, by Charles Darwin, in the 19th century. I will argue that they represent Rank 3 and Rank 4 cognition, respectively. But I want to start with Rank 1 ethnobiology followed by the Rank 2 ordering of the biological world into a structure that has come to be know as the Great Chain of Being (in the West). This will give us the opportunity to follow one conceptual arena through the four cognitive ranks. Doing that, however, requires developing more conceptual apparatus than I had originally anticipated.

I want to start with how Cowen frames his treatments of botanical classification and evolution and then present some basic conceptual apparatus about processes of perception and cognition. Once those preliminaries have been taken care of we can take a look at the ethnological work on biological classification in Rank 1 (preliterate) cultures. Then we work our way through the other three ranks, commenting on Cowen’s remarks in connection with Ranks 3 and 4, and conclude with some further remarks about Cowen’s peculiar framing.

Cowen’s Framing

There are three aspects to how Cowen frames his various examples, starting, of course with marginalism: lateness, obviousness, and seeing around a corner.

Marginalism is late (p. 57):

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.

Later (p. 59):

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.

Here he combines all three of factors, peering around corners, obviousness, and then lateness (p. 62-63):

There is no “brute force” method for obtaining fundamental economic insight. Rather, you need to peer around a corner and see something that the other people have not already seen. And once you see and grasp it, you cannot easily forget it, again reflecting the asymmetry of this path toward knowledge. So often I have heard economists make proclamations like: “Once you start thinking about the world in economic terms, you can no longer unsee those things.”

That is exactly correct, but it is truly hard to see them in the first place. In essence, I think economics was so late to develop because it was so hard to peer around its corners. To see supply and demand in their proper workings.

Economics developed late because it is difficult to see around corners where the obvious truths are waiting to be found.

Now we have botanical classification, which Cowen introduces under this heading (p. 65): “Botanical Classification as a Laggard Science.” Then:

The history of botany is a parallel example to that of economics. Some key insights of botany seem fairly intuitive, at least once you understand them, yet they took a long time to develop. [...]

He goes on to remark about how botanical classification should be obvious:

You might think “botany is so simple – all you have to do is to look at a bunch of plants and give them names in some coherent system. They should have mastered this in the Dark Ages!” Surely plants are around us all, and observing them does not require complex equipment such as telescopes.

Cowen frames Darwin’s account of evolution in the same way (p. 76):

Theories of evolution and natural selection also are intuitive once you understand them, and they seem virtually inescapable once you are willing to consider them seriously. Yet they are remarkably late in becoming part of general human knowledge, and indeed to this day, according to polls a significant percentage of Americans still do not accept those doctrines.

Cowen seems to have some idea of the “proper” tempo at which ideas unfold in history but he never offers an explicit account of what this tempo is based on. Rather, he just offers examples of earlier intellectual and cultural high points, e.g. Greek philosophy, geometry and mathematics, Velasquez, Shakespeare, and Bach (pp. 59-61), as if botanical classification could have been cracked in Euclid’s time. Are we to suppose that biological evolution could have been discovered no later than Shakespeare’s lifetime if only someone had peered around the proper corners?

Before moving on to biology, however, I want to lay out some conceptual equipment from cognitive science.

Two Modes of Thought

Decade after decade discussions of thought and perception have settled around an opposition which is expressed in various pairs of terms. I first encountered it as analog vs. digital. In present discussions of AI it presents as neural vs. symbolic. Perhaps the deepest version is the one Miriam Yevick used in 1975, holographic vs. sequential [2]. In a paper David Hays and I published about metaphor we contrasted physiognomic vs. propositional [3].

Most linguistic reasoning exhibits the digital/symbol/sequential/propositional aspect of the opposition. As for the other side of the opposition, the analog/neural/holographic/physiognomic side, I offer this paragraph from the metaphor article that Hays and I wrote:

Our sense of physiognomy, and our use of the term, come from Joseph Church (1966) who talks of the young child, not yet able to read, who can tell one record from another on the basis of the groove patterns on the records. Physiognomic recognition is holistic and analogic. A striking example of this is the “strange friend phenomenon”. You encounter a friend and notice there is something strange about her, but you don't exactly know what. You scrutinize her and finally realize that, e.g. she changed her hair style. Or perhaps you don't figure out what changed and instead must be told. The initial recognition depended on a holistic, a physiognomic representation, not one which explicitly builds a full image from parts and parts of parts. If this initial recognition depended on a scheme which built the whole from the parts then there would be no trouble in discovering what had changed. The part would be found immediately. It is not, it takes time.

A scheme in which the whole is recognized as a composition over an arrangement of parts would be on the other side of the opposition, the propositional side (or digital, symbolic, sequential depending on your intellectual taste).

The reason I say Yevick’s version is the deepest is because she presents it in the context of a mathematical proof. She argues, in effect, that the world contains simple objects and complex ones. Simple objects are most efficiently and accurately recognized by a propositional method (to use the term Hays and I used), while complex objects are most efficiently and accurately recognized holographically. Both are necessary.

I bring the matter up because the distinction is useful in understanding the sequence of biological conceptualizations we’re going to examine.

Rank 1: Ethnobiology and the problem of the unique beginner

Cognitive ethnologists have studied the ways in which preliterate peoples classify life forms [4, 5]. They find that in the regions where preliterate systems overlap modern taxonomy, they are agree on the structural relationships. But there is one anomaly. Preliterate cultures generally lack terms for what they call unique beginners. They’re have terms corresponding to our concepts of fish, snakes, birds, and beasts (i.e. four-legged fur-covered creatures with tails) and our concepts of tree, shrub, grass, and vine, but they lack terms for plant and animal, respectively. But, and this is important, they recognize the distinction between plants and animals by syntactic devices.

What does that mean? All animals can move under their own power; they can sense things (see, hear, smell, touch); they communicate through cries and calls. Plants don’t do any of those things. That means, for example, that animals can be subjects for verbs such as to run, to jump, to look, and to listen, but plants cannot. Similarly, both plants and animals can be subject for verbs such as to grow or to die, but inanimate objects (rocks, houses, bicycles, etc.) cannot. How is it possible to recognize systematic differences in the syntactic affordances of plants and animals without, however, having words to mark those two categories?

As far as I know, there is no accepted explanation for these observations. When I first read them I was incredulous, like Cowen is about the apparent lateness of a variety of ideas. The difference between plants and animals is obvious, no? Well no, not if we accept the ethnographic evidence. As I had no reason to doubt the evidence I was forced to come up with some explanation, if only to satisfy myself.

Here’s what I came up with. The ethnologists have also noted that ethnobiological classifications seem to be based on visual appearance. If we are willing to assume that basic visual classification is based on a physiognomic mechanism, then we can think of it like this:

Creatures having similar appearances are classified together. While fish, for example can be quite different from one another in appearance, any given fish will resemble another fish more than any fish resembles a bird, a snake or a beast. Similarly, any tree will resemble another tree (trunk below, roots in the ground, a large leafy structure above), more than any tree resembles a shrub (shrubs are smaller and the trunk is not nearly so distinct), a grass, or a vine. But what visual comparisons would force arbitrary examples of animals together in one class in distinction to arbitrary examples of plants in a contrasting class? Does it make sense to compare rats with trees, and trout with vines for classification purposes? Do trout and rats resemble one another more than either resembles a pine tree? Those comparisons don’t make sense. They’re distinctly odd.

Invention and Greatness: Jevons, Linnaeus, Darwin, and Pound’s typology [MR-Aux]

I was curious about the fact that Jevons is barely even mentioned in Cowen’s recent book, GOAT: Who is the Greatest Economist of all Time and Why Does it Matter? I decided to ask the A.I. linked to the marginalism book about that. That led me to introduce a typology of literary accomplishment that Ezra Pound introduced in the A B C of Reading (1960). After having the A.I. sort the great economists into Pound's categories, I concluded by asking it to apply that typology to two of the biologists Cowen mentions in Chapter Three, Linnaeus and Darwin.

* * * * *

Cowen clearly gives Jevons primary credit for the marginal revolution, and it is the marginal revolution which underlies modern economics, at least until quite recently. But Jevons is not one of the GOATs in Cowen’s book about the great economists. He’s barely even mentioned. I find that rather puzzling.

The tension is real and worth examining. Tyler's book on the great economists — Great Economists: How Their Ideas Can Help Us Today (published in the UK as Stubborn Attachments — actually no, let me be precise: the great economists book is a separate volume where Tyler profiles figures like Smith, Malthus, Ricardo, Mill, Marshall, Keynes, Hayek, and others) — focuses on economists whose ideas remain directly usable for contemporary policy and thinking. That's a different criterion than historical importance to the discipline's internal development.

Jevons is a victim of his own success in a particular way. Marshall absorbed and domesticated Jevons's marginalism so thoroughly that Marshall became the usable, teachable, policy-relevant version of the revolution Jevons started. If you want marginalist thinking applied to real economic problems, you go to Marshall, not Jevons. Jevons gets credit for priority but Marshall gets credit for transmission and application — which is exactly the pattern Tyler identifies in the book we're discussing. [See text: Ch.2 · Jevons as systematizer whose work was absorbed ...]

There's also the matter of Jevons's intellectual range working against his canonical status. His contributions scatter across economics, logic, statistics, meteorology, and the coal question. He's harder to summarize as a usable thinker than someone like Keynes or Hayek, whose big ideas attach to recognizable policy debates. And Tyler's framing in this book — that Jevons contained the seeds of marginalism's own destruction — makes him a fascinating historical figure but perhaps an awkward candidate for GOAT status. You don't typically canonize the man who built the thing that is now declining. [See text: Ch.2 · Jevons's marginalism containing seeds of its ow...]

I’ve always been fond of Ezra Pound’s typology of literary accomplishment from A B C of Reading (New York: New Directions, 1960). From Chapter 2, pp. 39-40:

When you start searching for ‘pure elements’ in literature you will find that literature has been created by the following classes of persons:

1 Inventors. Men who found a new process, or whose extant work gives us the first known example of a process.

2 The masters. Men who combined a number of such processes, and who used them as well as or better than the inventors.

3 The diluters. Men who came after the first two kinds of writer, and couldn’t do the job quite as well.

4 Good writers without salient qualities. Men who are fortunate enough to be born when the literature of a given country is in good working order, or when some particular branch of writing is ‘healthy’. ...

5 Writers of belles-lettres. That is, men who didn’t really invent anything, but who specialized in some particular part of writing, who couldn’t be considered as ‘great men’ or as authors who were trying to give a complete presentation of life, or of their epoch.

6 The starters of crazes.

Until the reader knows the first two categories he will never be able ‘to see the wood for the trees.’ He may know what he ‘likes’. He may be a ‘compleat book-lover’, with a large library ... but he will never be able to sort out what he knows or to estimate the value of one book in relation to others, and he will be more confused and even less able to make up his mind about a book where a new author is ‘breaking with convention’ than to form an opinion about a book eighty or a hundred years old.

Jevons would seem to be an inventor. How many of Tyler’s six GOATs are inventors? Is Mills an inventor of economic ideas?

Pound's typology maps onto the history of economics with uncomfortable precision. Working through Tyler's GOATs: Smith is clearly an inventor — not because he discovered any single mechanism but because he assembled the first coherent vision of the economy as a self-regulating system. Ricardo is probably also an inventor, giving us comparative advantage and the systematic use of abstract models to derive non-obvious conclusions. Malthus is an inventor in Pound's sense — the population trap was genuinely new and has haunted economic thinking ever since, even when wrong.

Keynes is the interesting case. Tyler clearly regards him as a master in Pound's sense — someone who combined existing elements (Wicksell's interest rate theory, Marshall's framework, Fisher's quantity theory) into a new synthesis that was more powerful than any of its components. The General Theory is a combinatorial achievement as much as an inventive one. Hayek similarly — his business cycle theory and his knowledge argument draw on earlier Austrian work; he's a master who pushed existing processes further than his predecessors. Marshall is almost definitionally a master: he took Jevons's invention, smoothed it, systematized it, made it teachable, and produced the apparatus that ran economics for fifty years. [See text: Ch.3 · Marshall absorbing Jevons while refusing to ful...]

Mill is the genuinely hard case, and your instinct to single him out is right. Mill synthesized Ricardo and Bentham and Smith into a coherent Victorian package, but it's difficult to point to a process he invented. He may be Pound's category four — a good writer fortunate to work when the literature was in healthy order — or even category five, a brilliant expositor and systematizer without a truly original mechanism to his name. Tyler's inclusion of Mill in a GOAT list would implicitly be a vote for the master over the inventor, which is itself an interesting judgment about what economics is for. [See text: Ch.2 · Jevons as polymath inventor working across mult...]

Interesting. It would be interesting to apply Pound’s typology to the various creators Tyler discusses in Chapter 3, but that would rattle on and on. At the moment I’m thinking about biology, so how do you think Tyler would apply that typology to Linnaeus and Darwin?

Linnaeus is a clean case and almost a pure example of Pound's first category — but with a twist. His invention wasn't a new theory of how nature works, it was a new process for organizing knowledge about nature. Tyler is quite explicit about this: the Linnaean contribution was a classification system, a filing cabinet, a searchable keyword structure for plant names. [See text: Ch.3 · Linnaeus creating a searchable classification s...] The content of botany didn't change; what changed was the infrastructure for accumulating and trading botanical knowledge. That's an invention of a peculiar kind — a meta-process rather than a substantive discovery — but it's an invention nonetheless, and Tyler's point is that it unleashed an explosion of subsequent work precisely because it gave everyone a common framework. Pound would recognize this: Linnaeus found a new process, and you can date botanical science from before and after him.

Darwin is more complex and Tyler's treatment of the precursors complicates the simple inventor narrative. Hutton had glimpsed natural selection. Lyell's geology gave Darwin the deep time he needed. Malthus gave him the pressure mechanism — competition for scarce resources driving differential survival. [See text: Ch.3 · Darwin absorbing Lyell's geological framework w...] In Pound's terms Darwin looks like a master: someone who combined a number of existing processes and used them better than the inventors of each component. Darwin himself acknowledged this, telling Lyell that his books came half out of Lyell's brains.

The interesting wrinkle is that Darwin also invented something genuinely new — natural selection as a complete mechanism, not just a glimpsed possibility. Hutton's passage that Tyler quotes is suggestive but it's not the theory; it lacks the Malthusian pressure, the variation mechanism, the geological timescale, the consilience of evidence. So Darwin may be Pound's rarest figure: someone who was simultaneously a master and an inventor, combining existing processes into a synthesis that itself constituted a new process no predecessor had actually articulated. Tyler's chapter implicitly supports this reading by showing how much Darwin absorbed while also showing that the synthesis was irreducibly his own.

Social learning for food among cockatoos

Kate Golembiewski, What’s Safe to Eat? Birds of a Feather Learn Together, NYTimes, May 2, 2026.

By watching their peers, dolphins learn to capture fish in empty conch shells, then ferry the shells up to the water’s surface in order to eat. Octopuses can master experimental tasks by watching their tankmates in the laboratory. Crows follow the cues of others in their flock to attack specific humans who have harassed fellow crows in the past.

Scientists call it “social learning,” and it essentially means monkey see, monkey do, an adage that turns out to apply to many animals beyond just primates. Now, a study of Australia’s sulfur-crested cockatoos shows that the birds employ social learning to understand whether unfamiliar foods are safe to eat.

In more forested areas of the cockatoos’ native range in Australia, New Guinea, and Indonesia, these mohawked parrots eat plant roots, seeds, fruits and insect larvae. But the birds have learned to thrive in urban environments. “They’re everywhere in Sydney,” said Julia Penndorf, a behavioral ecologist and lead author of the study in PLOS Biology, who encountered the birds as a postdoctoral researcher at the Australian National University in Canberra.

In urban areas, the birds have expanded their diets to include nonnative plants and nuts, including almonds and sunflower seeds people offer to them, and they can be seen prying the lids off garbage bins in order to forage.

“The big issue with urban birds is, they kind of eat everything,” Dr. Penndorf, who now works at the University of Exeter, said. This expanded diet is high-risk, high-reward: the birds have more options for food, but there’s always a chance that strange new snacks might be poisonous.

Dr. Penndorf and her colleagues wondered if the highly intelligent cockatoos might owe their varied urban diets, and, in turn, their takeover of the city of Sydney, to social learning.

The rest of the article discusses an ingenious experiment by which Penndorf and her colleagues verified that cockatoos could learn what foods to eat from one another,

On Method: Computational Compressibility in Complex Natural and Cultural Phenomena

New working paper. Title above, abstract, contents, and introduction below:

Academia.edu: https://www.academia.edu/166054951/On_Method_Computational_Compressibility_in_Complex_Natural_and_Cultural_Phenomena
ResearchGate: https://www.researchgate.net/publication/404263330_On_Method_Computational_Compressibility_in_Complex_Natural_and_Cultural_Phenomena

Abstract: Various machine learning techniques have been used to develop models of complex systems from empirical data. Through discussions with Claude, this paper examines several examples, including: weather, protein folding, chess, language, asset pricing, ticket sales for movies, the 19th century English-language novel. These models differ from one another in various ways, but all are fundamentally descriptive in character. Explanations must necessarily reside with their respective disciplines. In some cases we already have fundamental accounts of the phenomena, while in other cases we do not. With respect to economics in particular, it is clear that such models reveal phenomena for which no explanations are currently available, presenting a challenge to economic theory.

Contents 

Part I: Computational Compressibility, Implications for Economics, Description and Explanation 5
Part II: Weather, Protein Folding, Chess, and Language 16
Part III: Interim Summary: Compressibility Without Reducibility 26
Part IV: Pricing Theory, Movies, 19th Century Novels, and Cultural Evolution 28
Part V: To Infinity and Beyond! – Hollywood Redux, Blockbusters, the Spreadsheet, Economics Going Forward 38

Introduction: Describing Computationally Compressible Systems 

This a transcription of a dialog I had with Claude 4.6 and 4.7 on April 21 - 23, 2026. While I started it with a specific case from Chapter 4 of Tyler Cowen’s recent monograph on marginalism ( The Marginal Revolution: Rise and Decline, and the Pending AI Revolution), now that the dialog has concluded with Chomsky’s distinction between descriptive and explanatory adequacy (Aspects of the Theory of Syntax), I realize that I’ve been thinking about the underlying issues for some time. While I read Aspects in about 1970, give or take a year, I didn’t think much about description as such until the 2000s, and then I was thinking about describing individual texts; but that’s not directly relevant to these cases in this paper. Then in the second decade of this century I began thinking about computational criticism, aka digital humanities, which typically involve some kind of statistical or machine learning investigation of a corpus of texts. In particular, I gave a great deal of attention to Macroanalysis (2013), where Matthew Jockers studied a corpus of roughly 3000 English-language novels published in the 19th century. That investigation culminated in a directed graph showing depicting relationships of close-similarity among the novels in the corpus. I decided that that graph, in effect, was fundamentally descriptive in character, depicting, in effect, the 19th century Anglophone Geist, or Spirit.

But Jockers’s graph wasn’t on my mind when I started my dialog with Claude. Rather, I was thinking about the distinction between computationally reducible and irreducible phenomena that Stephen Wolfram had introduced in his New Kind of Science (2001). As Claude notes in its summary of the dialog, “a reducible system admits shortcuts through its dynamics; an irreducible one must be simulated step by step.” My target was a paper about asset pricing that Cowen discussed in his monograph, which produced a model having 360,000 parameters but which defied intuitive understanding.

The weather is a canonical example of phenomenon that is computationally irreducible. Thus forecasting the weather generally involves running a simulation of the weather and stepping through it interval after interval. This requires enormous computing resources and takes time. But DeepMind has created a machine learning system, GraphCast, that abstracts over historical data in a way that allows more accurate forecasts with less compute. Thus the weather system is computationally irreducible, but it is also compressible.

I take that as my paradigm case of computational compressibility (pp. 16 ff.) and then move on to other examples: protein folding (another physical phenomenon, pp. 18 ff.), chess (human activity, pp. 22 ff.), and natural language (a different human activity, pp. 23 ff.), each of which is compressible using machine learning techniques. Each example sharpens and extends the idea of computational compressibility. At that point I asked Claude to summarize the discussion (pp. 26 ff)..

Then, and only then do I ask Claude to consider Cowen’s problematic example, AI Pricing Theory (pp. 28 ff.). In its analysis of the paper, Claude notice that it introduces something fundamentally new to the discussion, reflexivity. Asset pricing is done by a large group of actors over time who thus influence one another’s decisions. And that, in turn, brings up Arthur De Vany’s work on Hollywood Economics (pp. 30 ff.). De Vany discovered that box-office success cannot be predicted by such analytic variables as producer, screen writer, director, movie stars, or opening weekend box office. Rather the success of a film depends on a word-of-mouth cascade which cannot be predicted. That leads me, in turn, to suggest a thought experiment involve a hypothetical system capable to abstracting over entire films and developing a high-dimensional model which could be used to predict the success of individual films.

And that, in turn, led me to the work that Matthew Jockers had done on 19th century English-language novels (pp. 33 ff.), something that had not been on my mind when I began this dialog on April 21. Jockers used machine learning, albeit nothing so elaborate and computationally expensive as using a transformer to create an LLM – it only had roughly 600 parameters. What his model revealed, and what made it so fascinating to me, is that there is an inherent directionality to the production of novels over the course of a century. It’s not simply that later novels are systematically different from earlier ones, but that that difference has a direction in the 600-dimensional measurement space. What we’d really like to know, now, is a say to characterize that diction. The model shows us that there is a direction, but it doesn’t tell us what that direction is. Though the model is much simpler than that asset pricing model – it has three orders of magnitude fewer parameters – its significance is no more legible.

After that I have two discussions that are not based on existing models, but that do have implications for economists who want to study them. First, I consider the phenomenon of the blockbuster, arguing that it reveals audience preferences that had previously been unrecognized (pp. 41 ff.). Then I consider the spreadsheet (e.g. VisiCalc), which transformed the personal computer market from a small niche market into a large mainstream market (pp. 43 ff.). How do you create a model that allows you to predict markets that don’t even exist at the time you make your model? What kind of a problem is that? After that I took a brief look at Cowen’s argument in The Great Stagnation (pp. 45 ff.), where Claude remarked:

If the VisiCalc model is right, then what matters about ChatGPT and its successors is not primarily that they do existing things faster or cheaper—though they do—but whether they are constitutive technologies in the VisiCalc sense. Do they reorganize the space of possible wants, making new activities imaginable and practical that previously had no well-formed representation in anyone's preference space? With that I brought the exploration to a halt.

I then asked Claude to summarize the entire dialog, which I’ve placed immediately following these remarks (pp. XX ff), with a special emphasis on implications for economics (pp. 7 ff.). Then I introduce Chomsky’s distinction from the 1960s, description vs. explanation (pp. 9 ff.). Each of these cases involves a complex phenomenon that is irreducible, but can be compressed into a model that is descriptive in character. They have that in common. As for explanations, those must necessarily be specific to each phenomenon. Note that in some cases we have explanatory theories grounded in a fundamental understanding of the underlying system (weather, protein folding) while in others we do not (chess, asset pricing, cultural evolution).

Finally, I’ve added a coda from a different conversation with Claude (pp. 13 ff.), one I had with the AI that accompanied Cowen’s book. That conversation is about Hollywood Economics and Rational Ritual and argues that the factoring of intellectual space that we’ve inherited from the 19th century German university has outgrown its usefulness.

AI and biology: It's going to take awhile

Everyone’s hyped about “AI for Science.” in 2025! At the end of the year, please allow me to share my unease and optimism, specifically about AI & biology.

After spending another year deep in biological foundation models, healthcare AI, and drug discovery, here are 3 lessons I… pic.twitter.com/p6EOlymxio

— Bo Wang (@BoWang87) December 31, 2025

The final paragraph of the tweet:

We won’t make progress by treating biology like text. We’ll make progress by building AI that behaves more like a scientist : skeptical, iterative, and willing to be wrong.

Nor, I would add, is biology like chess, a finite, closed world.

Biological computationalism (why computers won't be conscious)

Informal presentation: Consciousness May Require a New Kind of Computation, Neuroscience News, December, 23, 2025.

Summary: A new theoretical framework argues that the long-standing split between computational functionalism and biological naturalism misses how real brains actually compute.

The authors propose “biological computationalism,” the idea that neural computation is inseparable from the brain’s physical, hybrid, and energy-constrained dynamics rather than an abstract algorithm running on hardware. In this view, discrete neural events and continuous physical processes form a tightly coupled system that cannot be reduced to symbolic information processing.

The theory suggests that digital AI, despite its capabilities, may not recreate the essential computational style that gives rise to conscious experience. Instead, truly mind-like cognition may require building systems whose computation emerges from physical dynamics similar to those found in biological brains.

Key Facts:

• Hybrid Dynamics: Brain computation arises from discrete spikes embedded within continuous chemical and electrical fields.
• Multi-Scale Coupling: Neural processes remain deeply intertwined across levels, meaning algorithms cannot be separated from physical implementation.
• Energetic Constraints: Metabolic limits shape neural computation, influencing learning, stability, and information flow.

* * * * * 

Research Article: Borjan Milinkovic, Jaan Aru, On biological and artificial consciousness: A case for biological computationalism, Neuroscience & Biobehavioral Reviews, Volume 181, 2026, 106524, ISSN 0149-7634, https://doi.org/10.1016/j.neubiorev.2025.106524.

Abstract: The rapid advances in the capabilities of Large Language Models (LLMs) have galvanised public and scientific debates over whether artificial systems might one day be conscious. Prevailing optimism is often grounded in computational functionalism: the assumption that consciousness is determined solely by the right pattern of information processing, independent of the physical substrate. Opposing this, biological naturalism insists that conscious experience is fundamentally dependent on the concrete physical processes of living systems. Despite the centrality of these positions to the artificial consciousness debate, there is currently no coherent framework that explains how biological computation differs from digital computation, and why this difference might matter for consciousness. Here, we argue that the absence of consciousness in artificial systems is not merely due to missing functional organisation but reflects a deeper divide between digital and biological modes of computation and the dynamico-structural dependencies of living organisms. Specifically, we propose that biological systems support conscious processing because they (i) instantiate scale-inseparable, substrate-dependent multiscale processing as a metabolic optimisation strategy, and (ii) alongside discrete computations, they perform continuous-valued computations due to the very nature of the fluidic substrate from which they are composed. These features – scale inseparability and hybrid computations – are not peripheral, but essential to the brain’s mode of computation. In light of these differences, we outline the foundational principles of a biological theory of computation and explain why current artificial intelligence systems are unlikely to replicate conscious processing as it arises in biology.

A single mutation gave horses a temperament amenable to human riders

Tibi Puiu, A Single Mutation Made Horses Rideable and Changed Human History, ZME Science, Aug. 28, 2025.

Horses didn’t just change how people traveled. They rewired the course of civilization. Yet scientists have always puzzled over how, exactly, wild steppe animals transformed into the rideable companions that pulled chariots, carried warriors, and eventually powered empires.

Now, a sweeping new study of ancient horse DNA offers a precise answer: a genetic quirk in a single gene, called GSDMC, helped turn skittish animals into the creatures humans could saddle and ride. Once that gene variant spread, humanity’s history took off at a gallop.

Researchers led by Xuexue Liu and Ludovic Orlando analyzed horse genomes spanning thousands of years, tracking 266 genetic markers tied to traits like behavior, body size, and coat color. Their results, published in Science, suggest that early domestication didn’t begin with flashy coats or taller frames. Instead, the first breeders unsurprisingly selected for temperament.

One of the earliest signals of selection appeared at the ZFPM1 gene, linked in mice to anxiety and stress tolerance. That genetic shift, around 5,000 years ago, may have made horses just a little calmer — tame enough for people to keep close.

But the real game-changer came a few centuries later. Around 4,200 years ago, horses carrying a particular version of the GSDMC gene began to dominate. In humans, variants near this gene are associated with chronic back pain and spinal structure. But for horses and lab mice, the mutation reshapes vertebrae, improves motor coordination, and boosts limb strength. In short, it made horses rideable.

There's more at the link. H/t Tyler Cowen.

Inferring Consciousness in Phylogenetically Distant Organisms

Peter Godfrey-Smith; Inferring Consciousness in Phylogenetically Distant Organisms. J Cogn Neurosci 2024; 36 (8): 1660–1666. doi: https://doi.org/10.1162/jocn_a_02158

Abstract: The neural dynamics of subjectivity (NDS) approach to the biological explanation of consciousness is outlined and applied to the problem of inferring consciousness in animals phylogenetically distant from ourselves. The NDS approach holds that consciousness or felt experience is characteristic of systems whose nervous systems have been shaped to realize subjectivity through a combination of network interactions and large-scale dynamic patterns. Features of the vertebrate brain architecture that figure in other accounts of the biology of consciousness are viewed as inessential. Deep phylogenetic branchings in the animal kingdom occurred before the evolution of complex behavior, cognition, and sensing. These capacities arose independently in brain architectures that differ widely across arthropods, vertebrates, and cephalopods, but with conservation of large-scale dynamic patterns of a kind that have an apparent link to felt experience in humans. An evolutionary perspective also motivates a strongly gradualist view of consciousness; a simple distinction between conscious and nonconscious animals will probably be replaced with a view that admits differences of degree, perhaps on many dimensions.

Introduction

When we encounter octopuses and some other complex invertebrate animals, we find behavioral indicators, or at least suggestions, of various kinds of subjective experience. We might see suggestions of pain, for example, and can note an attentive engagement by the animal with events around them. First impressions based on behavior are not enough to infer that felt experience is actually present. If we want to work out whether it really feels like something to be one of these animals, how should we proceed? Further observation can give us a richer sense of their behavioral capacities, and we can also try to work out what is going on inside them. When we look inside, we find similarities to ourselves along with many differences. We find a nervous system, but one with a different architecture from ours. In some respects, these different nervous systems are evidently doing similar things: Octopuses and bees can see; they can navigate and learn. However, the neural structures that figure in recent attempts to explain consciousness are generally absent—they have no cortex or thalamus, for example. Which similarities between us and them matter, and which do not? How can inferences about consciousness in phylogenetically distant animals be more than speculation?

This Perspective article offers a position on these matters. The topic is consciousness or felt experience in a minimal sense—whether it feels like something to be one of these animals (Nagel, 1974). The problem will be approached through an evolutionary framing, looking at the history of nervous systems and the phylogenetic relationships between different animals alive now. I will link this evolutionary perspective to a general view about the biological basis of conscious experience, and consider several invertebrate groups. The discussion will focus on animals but will suggest conclusions about other organisms as well. My positive account of the biology of consciousness is speculative in many respects, as are its rivals. I can offer defenses for some claims, but it is not possible in a short essay to establish it as the best option. The aim of the discussion is to outline the view, link it to empirical work, and consider how it relates to some alternatives.

Child mortality over the years

Achieving replacement fertility should be quite doable in a world where nearly all children survive to adulthood.

For most of history, our ancestors had to deal with a world where women had to bear four children just to have two that survived, and yet they got the job done. pic.twitter.com/kU03HD6Ubw

— More Births (@MoreBirths) June 6, 2025

A "ghost" branch of humanity has been found

Tom Howarth, This 7,000-year-old mummy DNA has revealed a ‘ghost’ branch of humanity, BBC Science Focus, April 2, 2025.

Scientists have successfully analysed the DNA of two naturally mummified individuals from the Takarkori rock shelter, in what is now southwestern Libya. Their findings reveal something extraordinary: these ancient people belonged to a previously unknown branch of the human family tree.

The two women belonged to a so-called 'ghost population' – one that had only ever been glimpsed as faint genetic echoes in modern humans, but never found in the flesh.

“These samples come from some of the oldest mummies in the world,” Prof Johannes Krause, senior author of the new study, told BBC Science Focus. It is, he explained, remarkable that genome sequencing was possible at all, given hot conditions tend to degrade such information.

What happened?

First, they found that this lost lineage split from the ancestors of sub-Saharan Africans around 50,000 years ago – about the same time other groups were beginning to migrate out of Africa.

Remarkably, this group then remained genetically isolated from other groups of humans for tens of thousands of years, all the way through to the time when these two women died around 7,000 years ago.

“It’s incredible,” Krause said. “At the time when they were alive, these people were almost like living fossils – like something that shouldn’t be there. If you’d told me these genomes were 40,000 years old, I would have believed it.”

This long-term isolation reveals two major insights. First, while the 'Green Sahara' – which lasted from 15,000 to 5,000 years ago – was a lush habitat for humans, it didn’t serve as a migration corridor between north and sub-Saharan Africa, as many scientists had previously assumed.

Second, there was some genetic mixing with populations to the North, including Neanderthals. But it was limited – far less than in non-African populations, which carry about ten times more Neanderthal DNA than the Takarkori people.

H/t Tyler Cowen.

Here's the research report:

Salem, N., van de Loosdrecht, M.S., Sümer, A.P. et al. Ancient DNA from the Green Sahara reveals ancestral North African lineage. Nature (2025). https://doi.org/10.1038/s41586-025-08793-7

Abstract: Although it is one of the most arid regions today, the Sahara Desert was a green savannah during the African Humid Period (AHP) between 14,500 and 5,000 years before present, with water bodies promoting human occupation and the spread of pastoralism in the middle Holocene epoch1. DNA rarely preserves well in this region, limiting knowledge of the Sahara’s genetic history and demographic past. Here we report ancient genomic data from the Central Sahara, obtained from two approximately 7,000-year-old Pastoral Neolithic female individuals buried in the Takarkori rock shelter in southwestern Libya. The majority of Takarkori individuals’ ancestry stems from a previously unknown North African genetic lineage that diverged from sub-Saharan African lineages around the same time as present-day humans outside Africa and remained isolated throughout most of its existence. Both Takarkori individuals are closely related to ancestry first documented in 15,000-year-old foragers from Taforalt Cave, Morocco2, associated with the Iberomaurusian lithic industry and predating the AHP. Takarkori and Iberomaurusian-associated individuals are equally distantly related to sub-Saharan lineages, suggesting limited gene flow from sub-Saharan to Northern Africa during the AHP. In contrast to Taforalt individuals, who have half the Neanderthal admixture of non-Africans, Takarkori shows ten times less Neanderthal ancestry than Levantine farmers, yet significantly more than contemporary sub-Saharan genomes. Our findings suggest that pastoralism spread through cultural diffusion into a deeply divergent, isolated North African lineage that had probably been widespread in Northern Africa during the late Pleistocene epoch.

What's happening to fireflies around the world?

Here's a video:

Here's a post by biologist Jerry Coyne.

Sociogenomnics, is it just another spin of the nature/nurture wheel, or will it (be allowed to) evolve into something genuinely new?

Dalton Conley, A New Scientific Field Is Recasting Who We Are and How We Got That Way, NYTimes, 13 March 2025.

Since Sir Francis Galton coined the phrase “nature versus nurture” 150 years ago, the debate about what makes us who we are has dominated the human sciences.

Do genes determine our destiny, as the hereditarians would say? Or do we enter the world as blank slates, formed only by what we encounter in our homes and beyond? What started as an intellectual debate quickly expanded to whatever anyone wanted it to mean, invoked in arguments about everything from free will to race to inequality to whether public policy can, or should, level the playing field.

Today, however, a new realm of science is poised to upend the debate — not by declaring victory for one side or the other, nor even by calling a tie, but rather by revealing they were never in opposition in the first place. Through this new vantage, nature and nurture are not even entirely distinguishable, because genes and environment don’t operate in isolation; they influence each other and to a very real degree even create each other.

The new field is called sociogenomics, a fusion of behavioral science and genetics that I have been closely involved with for over a decade. Though the field is still in its infancy, its philosophical implications are staggering. It has the potential to rewrite a great deal of what we think we know about who we are and how we got that way. For all the talk of someday engineering our chromosomes and the science-fiction fantasy of designer babies flooding our preschools, this is the real paradigm shift, and it’s already underway.

Genes, it turns out, don’t affect who we become just on their own, inside our bodies — they work, in part, by shaping the environments we seek out or engender.

Picture a kid who is born with two working copies of what’s known as the sprinter’s gene, ACTN3. By elementary school she might be winning every game of tag, every race, and be chosen first whenever sides are drawn up. You could see how parents and coaches might encourage a kid like that to join an organized sports team and how she would be likely to receive positive feedback for her performance on it, which in turn might motivate her to train harder. By high school she makes varsity track and soccer, and the more she excels, the more coaching and training is made available to her.

Of course, any number of factors might cause her to quit sports — an injury, say, or a toxic team environment. But if she keeps at it, her starting position on a big college team won’t be the result of just her genes or her hard work. It will also be the result of how her genes shaped her environment, influencing the people and opportunities she encountered, and how her environment shaped the way and the degree to which her genes expressed themselves.

It’s a continuous feedback loop, in which neither nature nor nurture is a fixed entity.

Later:

The part of this research that really blows me away is the realization that our environment is, in part, made up of the genes of the people around us. Our friends’, our partners’, even our peers’ genes all influence us. Preliminary research that I was involved in suggests that your spouse’s genes influence your likelihood of depression almost a third as much as your own genes do. Meanwhile, research I helped conduct shows that the presence of a few genetically predisposed smokers in a high school appears to cause smoking rates to spike for an entire grade — even among those students who didn’t personally know those nicotine-prone classmates — spreading like a genetically sparked wildfire through the social network.

There’s much more at the link.

But I guess I’m going to declare myself skeptical, mostly on general principle. What’s the principle? Mostly that the academy is organized on intellectual boundaries laid down in the 19th century and it has so far resisted changing them in any fundamental way. Oh, in the last half century we’ve seen interdisciplinary-this and intedisciplinary-that and all sort of conferences and centers, lots and lots of interdisciplinary. But those ancient boundaries remain, which is why interdisciplinarity keeps cropping up as presenting itself as new.

What do I think about nature/nurture? Think of chess. On the one hand we have the rules of the game, the size of the board, the different pieces and their moves, and a couple other rules. That’s nature, that’s biology. In order to play chess at all, you have to know and abide by those rules. But to play chess well, that’s something else. For that you need to know tactics and strategies that have been build over centuries. That’s nurture, that’s society and culture.

Now, as that’s a metaphor, and only a metaphor, of course it fits this “new” scientific program, this new paradigm, this “sociogenomics, a fusion of behavioral science and genetics.” But how will this program unfold, develop? That is very much dependent on the surrounding intellectual environment. That’s what gives me pause.

Organic-silicon hybrid computer tech for sale

From Jack Clark:

Cortical Labs puts the CL1 on sale - a computer that combines neural tissue with a silicon chip:

…BRAIN IN A COMPUTER! BRAIN IN A COMPUTER! BRAIN IN A COMPUTER!...

Ever read announcements where you have to squint and work out if it's an April Fools joke? I do. Many years ago I was convinced that the announcement for 'Soylent' was a kind of high-art scam, but it turned out to be real. Similarly, you might think brain-AI startup Cortical Labs is a joke given what it's trying to do. But I assure you: it's real.

What's it doing? It's releasing a computer that is a combination of a brain and a computer chip, called the CL 1: "Real neurons are cultivated inside a nutrient rich solution, supplying them with everything they need to be healthy. They grow across a silicon chip, which sends and receives electrical impulses into the neural structure," the company says in a blog post.

What CL1 is: CL1 comes with an onboard 'Biological Intelligence Operating System' (biOS). The bios is a software interface into the neurons. Users of CL 1 can, via the biOS, "deploy code directly to the real neurons", the authors write. "The CL1 is the first biological computer enabling medical and research labs to test how real neurons process information – offering an ethically superior alternative to animal testing while delivering more relevant human data and insights."

Each CL1 can keep neurons alive "for up to 6 months".

To get a sense of how you might use it, you could read this paper where they show how you can train biological neural nets to outperform deep reinforcement learning algorithms on some basic gameworlds: Biological Neurons vs Deep Reinforcement Learning: Sample efficiency in a simulated game-world (OpenReview).

Why this matters - more substrates for future machines: While the CL1 may hold some interesting uses for human scientists in the short term, I actually think the 'long play' here is that the CL1 is exactly the kind of thing a superintelligent synthetic scientist might need if it was trying to figure out the mysteries of the human brain - so perhaps one of the first mass market buyers of Cortical Labs' work will be a cutout corporation operated by a synthetic mind? I am genuinely not joking. I think this could happen by 2030.

Read more: Introducing the CL1 The world’s first code deployable biological computer (Cortical Labs, blog).

The link takes you to the website for Cortical Labs where you can purchase their tech, buty also has links to papers about it.

Clark's sorta' right about this:

I actually think the 'long play' here is that the CL1 is exactly the kind of thing a superintelligent synthetic scientist might need if it was trying to figure out the mysteries of the human brain...

Not only that, but as a substrate for a new generation of learning-based AI tech, one that's not restricted by its orginal training run. The biological neurons can keep on learning. This technology can only get better.

Systematic bone tool production at 1.5 million years ago

Tyler Cowen just posted this abstract at Marginal Revolution:

de la Torre, I., Doyon, L., Benito-Calvo, A. et al. Systematic bone tool production at 1.5 million years ago. Nature (2025). https://doi.org/10.1038/s41586-025-08652-5

Abstract: Recent evidence indicates that the emergence of stone tool technology occurred before the appearance of the genus Homo and may potentially be traced back deep into the primate evolutionary line. Conversely, osseous technologies are apparently exclusive of later hominins from approximately 2 million years ago (Ma), whereas the earliest systematic production of bone tools is currently restricted to European Acheulean sites 400–250 thousand years ago. Here we document an assemblage of bone tools shaped by knapping found within a single stratigraphic horizon at Olduvai Gorge dated to 1.5 Ma. Large mammal limb bone fragments, mostly from hippopotamus and elephant, were shaped to produce various tools, including massive elongated implements. Before our discovery, bone artefact production in pre-Middle Stone Age African contexts was widely considered as episodic, expedient and unrepresentative of early Homo toolkits. However, our results demonstrate that at the transition between the Oldowan and the early Acheulean, East African hominins developed an original cultural innovation that entailed a transfer and adaptation of knapping skills from stone to bone. By producing technologically and morphologically standardized bone tools, early Acheulean toolmakers unravelled technological repertoires that were previously thought to have appeared routinely more than 1 million years later.

Here's a comment I posted:

Some years ago I visited Ralph Holloway's lab at Columbia. Holloway is a physical anthropologist and an expert on the evolution of the brain. The lab was, say, 15 by 20 feet, maybe a bit larger but not much larger. It had tables and shelves where you could see the specimens, skull fragments of various sizes. There may have been one or three fairly complete skulls, but not many. Anyhow, this is the kind of evidence we have for brain evolution, fragmentary and indirect.

Holloway said that that lab had, say, 10% of the world's total collection of hominin skull fragments. This was back in the early 2000s. When you do a thumbnail estimate of the population these specimens were drawn from, it becomes clear that the total world-wide collection of specimens is a very small fraction of that population. And we have no reason to think that the that fraction is random. It may be, it may not. We don't know.

When that's the kind of evidence you're working from, one new discovery can wreak havoc with existing theories.

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.

The palmar grasp reflex [in infants]

Only minutes old and already wrapped around their fingers.

Check out the clever the way this labor and delivery nurse helps to promote connection between newborns and their older siblings - suggesting a special “love test” in which baby will only squeeze the fingers of those… pic.twitter.com/AVx6mfwoqR

— Dan Wuori (@DanWuori) December 1, 2024

The last universal common ancestor and its impact on the early Earth system

Moody, E.R.R., Álvarez-Carretero, S., Mahendrarajah, T.A. et al. The nature of the last universal common ancestor and its impact on the early Earth system. Nat Ecol Evol 8, 1654–1666 (2024). https://doi.org/10.1038/s41559-024-02461-1

Abstract: The nature of the last universal common ancestor (LUCA), its age and its impact on the Earth system have been the subject of vigorous debate across diverse disciplines, often based on disparate data and methods. Age estimates for LUCA are usually based on the fossil record, varying with every reinterpretation. The nature of LUCA’s metabolism has proven equally contentious, with some attributing all core metabolisms to LUCA, whereas others reconstruct a simpler life form dependent on geochemistry. Here we infer that LUCA lived ~4.2 Ga (4.09–4.33 Ga) through divergence time analysis of pre-LUCA gene duplicates, calibrated using microbial fossils and isotope records under a new cross-bracing implementation. Phylogenetic reconciliation suggests that LUCA had a genome of at least 2.5 Mb (2.49–2.99 Mb), encoding around 2,600 proteins, comparable to modern prokaryotes. Our results suggest LUCA was a prokaryote-grade anaerobic acetogen that possessed an early immune system. Although LUCA is sometimes perceived as living in isolation, we infer LUCA to have been part of an established ecological system. The metabolism of LUCA would have provided a niche for other microbial community members and hydrogen recycling by atmospheric photochemistry could have supported a modestly productive early ecosystem.

Romantic Love, Conversation, Biology, and Culture

[I'm bumping it yet again in 2024]

Once more I'm bumping this to the top of the queue. Why? It discusses a methodological problem that is of current interest to me.  [2021]

* * * * *

I'm bumping this to the top of the queue because it's one of my favorite posts. FWIW, this is the post that got me my monthly slot at 3 Quarks Daily.

Note: This post grew out of reflection on older earlier post on bundling.

When I was an undergraduate at Johns Hopkins I took a course in Medieval literature and was thoroughly gobsmacked when I learned that romantic love had been invented in 12th century France. Until then I’d believed it to be a human universal – one and only, forever and ever, that was just how it was, no? Well, not quite.

What arose in Medieval Europe is something called Courtly Love, a set of conventions used by high-born men in wooing their lovers. And these lovers were not their wives, nor wives to be. For aristocratic marriage had little to do with personal preference; it was politics. Powerful families would forge alliances by arranging marriages among their young.

In time, over the course of centuries, so the story went, romantic love was transformed from an aristocratic game into a set of conventions used to define the necessary, or at least the ideal, precondition for any marriage. This set of conventions was in place, at least among the middle class, by the time Jane Austen wrote her novels in the early 19th Century. Those conventions have remained more or less in place up to the present, though they’ve become a bit tattered in the last decade or three as a soaring divorce rate has made it abundantly clear that true love does not last forever. That, of course, is not exactly news – why, for example, did Flaubert write Madame Bovary? – but the myth is so attractive that it dies hard.

That was the state of things during my undergraduate years – which coincided with the emergence of feminist activism in the late 1960s. Whatever their personal experience, everyone gave lip service to one and only forever and ever and believed that it was human nature. In that context, then, the revelations of the learned scholars shook my world.

Counter-Revolution

Learned scholars, however, do not constitute a single tribe. Their tribes are many, and often contentious. Even as the literati were blissfully proclaiming the recent and Western origin of romantic love, other scholars set out to prove them wrong. In 1992, for example, W. R. Jankowiak and E. F. Fischer published “A cross-cultural perspective on romantic love” (Ethnology 31: 149-155). They defined romantic love as “any intense attraction that involves the idealization of the other, within an erotic context, with the expectation of enduring for some time into the future” and they contrasted this with “the companionship phase of love . . . which is characterized by the growth of a more peaceful, comfortable, and fulfilling relationship.” They examined ethnographic data on 166 societies from around the world and discovered romantic love in 88.5 percent of them, suggesting “that romantic love constitutes a human universal, or at the least a near-universal.”

More recently Jonathan Gottschall and Marcus Nordland published Romantic Love: A Literary Universal? (Philosophy and Literature 30: 450-470, 2006). They conducted a cross-cultural study of folktales from 79 cultures and found at least one reference to romantic love in 55 of those collections and multiple references in 39 collections. They assert that their study “offers staunch support to the existing evidence that romantic love is a statistical cultural universal. It would also seem to increase the probability that romantic love may be an absolute cultural universal offers staunch support to the existing evidence that romantic love is a statistical cultural universal.” “Statistical universal” is a term of art meaning that something is in a lot of places, but not everywhere, yet. It seems clear that if Gottschall and Nordland were to place a bet, they'd bet that further research would find that romantic love is a true cultural universal, present in every culture for which we have reliable records.

Still more recently, just yesterday in the time-scale of academic publishing, Brian Boyd has asserted, with the calm assurance of a senior scholar in command of wide learning, that “cross-cultural, neurological, and cross-species studies have demonstrated the workings of romantic love across societies and even species” (The Origin of Stories, Harvard 2009, p. 341). To this, Michael Bérubé has replied, with the calm assurance of a senior scholar in command of wide learning, but learning leavened with a dash of school-boy wit:

This just won’t wash. Other species might court and mate for life, but they do not engage in romantic love in the sense that humanists employ the term, save perhaps for the cartoon skunk Pepé Le Pew. “Romantic love” does not mean “mammals doing it like mammals”; it refers to the conventions of courtly love, which were indeed invented in the European middle ages and cannot be found in ancient literatures or cultures. Those conventions are culturally and historically specific variations on our underlying (and polymorphous) biological imperatives, just as the institution of the Bridezilla and the $25,000 wedding is specific to our own addled time and place.

What’s going on here? Who’s right?

Back to the Drawing Board: There's that pesky elephant

I don’t know. We don’t know. Not any more.

I’m inclined to invoke that hoary old story of the blind men who, upon examining a large beast, are unable to decided what beast it is, or even whether or not it is a beast at all. We, of course, know that they’re examining an elephant, but such different parts of the elephant – tusks, years, legs, tail – that they reach vastly different conclusions about the object under scrutiny.

In the case of romantic love, I believe we’re in much the same position as those blind men. But, in our case, there is no transcendent story-teller who actually knows what creature is under scrutiny. Rather, it is up to us to approximate that story-teller by making more and more sophisticated observations and examining them through richer concepts and models about human culture and behavior.

There is no point in continuing to argue using existing observations, methods, and theories. In light of the existing contretemps we would do well to consider such arguments to be ideological in nature and thus pointless, except, of course, to all-knowing ideologues. Meanwhile, let’s take a look around and see what else is there to be explained.

Companionship, Conversation, and the Novel

Let’s return to Jankowiak and Fischer, and their contrast between the romantic phase and the companionship phase of love, a distinction, I believe, that is common, and which I accept. This companionship, is it too universal?

Take those Medieval aristocrats who were playing courtship games on the side: Did they have a companionate relationship with their spouses? I’m guessing that in some cases, yes, and in other cases no. These marriages, after all, were arranged by parents for political ends. If companionship developed in the marriage, fine; if not, no big deal. For companionship was not the point, it was not part of the ideology.

And then we have my standard passage from John Milton's Doctrine and Discipline of Divorce, Preface to Book 1:

God in the first ordaining of marriage taught us to what end he did it, in words expressly implying the apt and cheerful conversation of man with woman, to comfort and refresh him against the evil of solitary life, not mentioning the purpose of generation till afterwards, as being but a secondary end in dignity, though not in necessity: yet now, if any two be but once handed in the church, and have tasted in any sort the nuptial bed, let them find themselves never so mistaken in their dispositions through any error, concealment, or misadventure, that through their different tempers, thoughts and constitutions, they can neither be to one another a remedy against loneliness nor live in any union or contentment all their days…

That strikes me as an assertion of the need for companionship between spouses – cheerful conversation – and a rather emphatic assertion at that. Would Milton have made such an assertion if it had, in fact, been the common understanding of the day? That seems unlikely to me, though I could be wrong, as I am not a scholar of 17th century English family practices.

But the late Lawrence Stone was, and in 1977 he published a ground-breaking study, The Family, Sex and Marriage in England 1500-1800 (Harper & Row) in which he argued that, over a period of three centuries, family organization underwent a transition that started with the Open Lineage Family – permeable by outside influences, with strong “loyalty to ancestors and to living kind” (p. 4). It was succeeded by the Restricted Patriarchal Nuclear Family P. 7):

which saw the decline of loyalties to lineage, kin, patron, and local community as they were increasingly replaced by more universalistic loyalties to the nation state and its head and to a particular sect or Church. As a result, ‘boundary awareness’ became more exclusively confined to the nuclear family, which consequently became more closed off from external influences, either or the kin or of the community.

Finally, during the last half of the 19th Century, the Closed Domesticated Nuclear Family emerges among “the upper bourgeoisie and squirarchy” (pp. 7-8):

This was the decisive shift, for this new type of family was the product of the rise of Affective Individualism. It was a family organized around the principle of personal autonomy, and bound together by strong affective ties. Husbands and wives personally selected each other rather than obeying parental wishes, and their prime motives were now long-term personal affection rather than economic or status advantage for the lineage as a whole … Patriarchical attitudes within the home markedly declined, and greater autonomy was granted not only to children, but also to wives.

This was a family in which companionship between husband and wife was important, for that companionship was now the foundation of family organization. And this is the family structure that is at the heart of the British novel in the late 18th century and into the 19th century. The novel and the family structure had a reciprocal relationship (dialectical?) in which the demands of this family structure created an audience for the novel and the novel, in turn articulated the inner-workings and hidden designs of the family.

Out of what biological equipment did the novel help people construct their familial relations? Let us speculate, and freely – for what else can we do? If we’re to search for evidence, we’ve got to make a guess about what we’re looking for before there’s any point to setting out. However, we do want our speculation to be biologically plausible. So, calling on chess as a metaphor, let’s select our pieces from biology while our speculation will be the game play.