The systems theorist explains what’s wrong with standard models of intelligence.
One way to think about culture is as dominoes arrayed in a line, with one tipping over another, a sort of cascade of influence,” said David Krakauer. “When Newton and Leibniz were formulating infinitesimal calculus, they were borrowing from previous ideas in mathematics, which in turn borrowed from early ideas of geometry in Ancient Greece. That cumulative process is what allows for the civilization we now live in.”
The cascade of cultural influence was only one of many ideas that leaped from Krakauer, an intellectual livewire, in his conversation with Nautilus. He was ardent about the nature of intelligence and genius, ignorance and stupidity. Stupidity, he said, is “the greatest problem facing the world today.”
Krakauer is a provocateur who readily jumps across academic boundaries. As the first director of Wisconsin Institute for Discovery, at the University of Wisconsin, Madison, he’s cultivated an interdisciplinary environment that’s brought together geneticists, game theorists, computational scientists, and even a cartoonist. This summer he’ll leave the University of Wisconsin to become president of the Santa Fe Institute, another intellectual hotspot famous for its maverick scientists and interdisciplinary work on the science of complexity. Krakauer was chair of the faculty at SFI before he landed in Wisconsin in 2011.
An Oxford-trained polymath, Krakauer has a passion for systems theory, looking for connections between the way information is organized from cells to neural networks to world cultures. He’s also a film buff and avid fiction reader with a penchant for taking on idiosyncratic themes in his reading choices. This year it’s a personal study of the history of building bridges, mines, and other systems of infrastructure. As I said, our conversation jumped excitedly from one Krakauer enthusiasm to the next.
Interview Transcript
What is intelligence?
People will mention Isaac Newton and Gottfried Wilhelm von Leibniz, the invention of the calculus, the derivative that allowed individuals to calculate the motion of particles in space and time, the well-tempered scale, the 12-tone system, the periodic table of the elements, James Joyce’s Ulysses. And so it’s very difficult to answer the question, “What is intelligence?” if all of those seemingly disparate examples are instances of what we think of as intelligence. So that’s the challenge, I think, that we face.
The tendency has been to try to reduce it to one number, psychometrically: the IQ test. And that would be like saying that you can know everything about the value of a thing from its price, right? So if I said, “I went to a wonderful art show and I saw Pablo Picasso’s Les Demoiselles d’Avignon,”—tell me about it. Well, 6.6 million [dollars] or whatever, presumably considerably more; it tells you nothing. And so this is the challenge. On the one hand, it’s complicated and diverse, and the tendency has been to excessively simplify.
Now let’s try and get at what it might be. Let’s take a very simple example, and the example I’d like to choose is the Rubik’s Cube. If I give you a Rubik’s Cube and you try and solve it just randomly, it’ll take many, many lifetimes. There are a billion, billion solutions to the Rubik’s Cube; that’s several lifetimes. That would be ignorance. That would be where you just don’t know what to do, and so you perform essentially at the random level. Stupidity—for the Rubik’s Cube—is if you just consistently moved and manipulated one face. Maybe if I just rotate this face forever, eventually the cube will be solved. And it will never be solved, in infinite time—unlike the random case, which will. Intelligence is a series of rules, manipulations, that will guarantee that you reach a solution in n steps or less.
Is stupidity the opposite of intelligence?
No. There’s actually a whole taxonomy of concepts that are worth thinking about. So there’s intelligence, there’s genius, there’s stupidity, there’s ignorance, and there’s being wrong. And it’s worth unpacking what each of these might mean.
Ignorance is insufficient data. So it doesn’t matter how smart you are; if you don’t have enough data to solve a problem, you’ll never solve it.
Intelligence is finding very simple solutions to complex problems. For example, if you say to someone, “You made that look effortless!” you’re saying to them, “You’re smart.” If you say to someone, “You made that look really hard,” you’re saying, “That’s stupid.” So stupidity is using a rule where adding more data doesn’t improve your chances of getting it right; in fact, it makes it more likely that you get it wrong.
So being wrong has in common with ignorance the fact that you’re more likely to get it right when you get extra data. So stupidity is a very interesting class of phenomena in history and it has to do with rule systems that have made it harder for us to arrive at the truth, and we could talk about it. It’s an interesting fact that while there are numerous individuals who study intelligence—there are whole departments that are interested in it—if you were to ask yourself what’s the greatest problem facing the world today, I would say it would be stupidity. So we should have professors of Stupidity; it would just be embarrassing to be called the Stupid Professor!
What are some examples of stupidity in science?
I’ll give you two examples that I think are rather intriguing. One of them is the theory of continental drift and plate tectonics. It was realized at the beginning of the 20th century that if you look at the shape of the continents, it looks a bit like a jigsaw puzzle. Alfred Wegener in 1912 suggested that actually, maybe they all fit together into a giant continent called Pangaea, and somehow, over the course of time they’d been separated. Anyone, [even] a child, can see this if they look at a map. He showed this to his colleagues and they said, absolutely not.
So what is that? Well, it started with ignorance because they hadn’t looked properly, but now they had the data and they still resisted the theory. Is that stupidity? Or, just being wrong? Well, their argument was [that] there’s no mechanism that could possibly explain how it is that those jigsaw puzzles drifted apart. And then in the 1930s, Arthur Holmes came along and said, oh, plate tectonics: There’s convective forces that cause them to be drifting apart. But still they wouldn’t accept it! So now you had the empirical regularity—the jigsaw; you had the mechanism—convection; and it was still denied. We’re in the domain of the stupid. And it’s because the rules that they were applying, which came from the world before Wegener, were inapplicable to the new empirical reality. That’s one.
My favorite version though, is Mars. In the 19th century, Giovanni Schiaparelli looks at Mars and says, “oh, there are canals on the surface of Mars!” And that was sort of ignorant because he looked through the telescope and he saw scarification on the surface of the planet. But where it became stupid is when subsequently, Percival Lowell came along and said, “Oh no, they’re not; that’s a vast irrigation system that’s been put on the surface of the planet by an extraterrestrial civilization.” Now, people said, “Well Percy, what makes you believe that?” He said, “well look, the more I look, the more of them I see!” So this was a case where he was genuinely being stupid, because somehow his system for interpreting the regularity was completely at odds with the empirical details themselves, and massively convoluted—given what had to be accounted for. So those are beautiful examples of stupidity at the system level.
What is genius?
I have my own favorite explanation for what genius is. If intelligence is making hard problems easy, genius is making problems go away! Let me give you some examples.
So let’s take physics. Prior to the so-called scientific revolution of the 17th century, we had extraordinary theories for how the planets pursue their orbits, the Ptolemaic system, Tychonic system, and so on. Every time someone made a new observation of a new planet, they had to add more and more complexity to their models—epicycles and deferents and so forth. Johannes Kepler came along and said, you know, I can replace all of that with the ellipse. So take that whole structure—he didn’t make that work better, he didn’t oil that; he just threw it away! And he replaced it with Kepler’s laws. It was an extraordinary simplification of the problem.
Now, Kepler has his laws but he doesn’t understand where they come from. Why is there motion in the ellipse? Along comes Isaac Newton. He’s a young man, he’s living with his mum because there’s a plague in London; doesn’t have any colleagues, doesn’t have any collaborators; he just is a really angry young man. He says, you know, the general theory of gravity, the inverse square law—that the force of gravity falls off non-linearly in the distance. That’s why they’re elliptical. Solves it, boom! Replaces Kepler’s laws with a more fundamental theory of gravity.
Problem with Newton’s theory is that it doesn’t work very well with very large mass or very large velocities. Albert Einstein comes along, [and] introduces space-time in his general theory of relativity. Out with Newton. Now, it doesn’t mean that those other theories don’t remain in some sense useful; but what genius does is it just changes the rules of the game. It doesn’t just make it better, or easier, or more efficient. And one of the very interesting characteristics of genius, as opposed to intelligence, is it looks a little crazy. Because an intelligent solution is almost always—and I gave some examples of stupidity—clear to most people that that is a better way of doing things. Yes, that is a better way of doing things. But when you change the rules, you make a lot of people uncomfortable, and it looks a little crazy. So in some sense, my diagnostic, my litmus test for genius as opposed to extreme intelligence is it made everything simpler, but the people, when they first saw it thought it was lunatic; because formally, it’s changing the basis set. It’s just changing the nature of the representation of the problem so completely that you get the kind of vertigo of unfamiliarity. So that for me would be genius.
Speaking of geniuses, why are you so fascinated by Charles Darwin?
Darwin is a very interesting case. My interest in Darwin is part intellectual and part biographical, as is often the case. So intellectually, a little bit like Newton. He wasn’t at university; he was on a boat! He was sailing around the coast of South America with a very unstable captain. And on that boat, without funds, without qualifications, without colleagues, he formulates, essentially, in his red notebook, the theory of natural selection—based on empirical observations of an extraordinary kind. So that level of empirical synthesis, somewhat in isolation, is of great interest to me.
The other thing about Darwin that makes him an ambiguous figure, in fact, in discussions of genius relates to what attributes of mind he had, for example, in distinction to a Henri Poincaré or a great mathematician, and it’s because he combined what Howard Gardner would call, “multiple intelligences.” Darwin was both narrative and linguistic; he was visual; and he was analytical. So what makes Darwin somewhat unique in the history of science—and I think explains in part why he’s such a key figure—is that he had many attributes of intelligence—not just the analytical-mathematical or the linguistic-narrative. He had them all! And the taxonomic. So intellectually, my colleague at the Santa Fe Institute, Murray Gell-Mann, one of the great physicists of the 20th century, is like that. What makes him so special is [that] he has the analytical-mathematical and the taxonomic-narrative. He called this fundamental of fundamental particles the “quark” because he was interested in James Joyce. So that’s the intellectual.
The other side of Darwin that interests me a lot—and I speak now in my role as a sort of director of institutes or a president of institutes—is his courage. So when he proposed the theory, he immediately recognized [that it would] challenge a lot of received beliefs about where we came from and the nature of reality and moral codes; and yet he persisted in arguing in favor of what he thought the right theory was. His wife was horrified. His family were horrified. Society was horrified! The extraordinary bravery it took to stick to that position; of course he had allies and it helped a great deal. So it’s that combination of … It’s very romantic, right? Solitary explorer in the ocean, integrating observations that other people hadn’t made; the production of one long argument, which is what he called, On the Origin of Species, in the face of extraordinary opposition where he was called crazy, make Darwin just a very captivating figure in the history of science.
Is it a mistake to look to brain science as the best way to understand intelligence?
I don’t think studying the brain through neuroscience and psychology is a mistake, but I think it’s very limited. I think there’s a whole series of reasons why that’s true. One of them is the huge anthropomorphic bias that we have, which is, “What’s intelligence? Well, it’s just what we do.” In other words, it’s language. And so by definition, since as far as we are aware Homo sapiens is the only species on Earth with a sophisticated grammar, it’s the only species that’s intelligent. Of course, the ludicrous examples of that are: [Those] that use oil painting; or have a printing press; or, so pick your favorite attribute of the particular demographic that you’re interested in and make that the definition of intelligence and exclude everybody else. So that’s the history of thinking about intelligence in fact, excluding others—other human beings or other species. So that’s one huge problem and it’s manifested in the fact that most studies of intelligence are studies of intelligence in one species.
That immediately suggests the other problem, which is [that] it’s fundamentally non-Darwinian; it’s non-evolutionary. If you do believe that all species on Earth are related, as I do; and you do believe in some naturalistic account of where intelligence came from, then it would profit us to look at other species and their attributes, to try and find some path that might lead to all of this variability. And those fields—neuroscience and psychology—tend to be quite anti-evolutionary. It’s very rare that you see comparative work in those departments.
Another problem is dualism, but not mind-brain dualism that everyone knows, that somehow mind is emergent from brain; but brain-body dualism—that if you’re studying intelligence, you just study properties of the brain and not properties of the body. So for example, we have a whole series of terms that make fine distinctions, which are in fact bogus. So mathematicians are intelligent; potters and painters are talented; and sports people are skilled; or what have you. These terms, what do they really mean? They mean that I’m biased, that I want to shore up my domain and ensure that intelligence is only a property of deduction, of higher cortical function.
So the final problem, I think, with the standard model for studying intelligence is the theory of computation that comes to us from Alonzo Church and Alan Turing and John von Neumann and others. So if you ask a psychologist or a neuroscientist, “What is a brain? Why is a brain different from a liver or a kidney or a lung?” They would say, “Well, because it’s a computing device.” Well, what’s a computing device? Well, we know that from Alan Turing. And, that theory of computation works because Turing very ingeniously separated memory and processing and allowed himself infinite memory and infinite time, and that allowed him to solve very important theorems on the question of undecidability. But that’s the wrong model of computation for evolved structure, and I think a lot of the framework that we used to reason about intelligence unconsciously or tacitly borrows from a theory of computation that’s appropriate for engineered devices, but might not be appropriate for evolved devices.
Why do you use the word “intelligence” to describe gifted athletes?
It gets back to my definition in terms of making hard problems easy. So I gave the example of “high jump,” and that’s exactly what athletes do. In other words, something that we’d find tremendously difficult—skiing downhill at a very high velocity or getting a small ball into a basket or getting a ball over a net at over 70 miles an hour, things that we struggle with—I assume you do, [these are things] I certainly struggle with—they make look effortless. And that’s not really that different from a mathematician effortlessly solving a theorem, or a musician remembering a symphony. The difference [exists in] the part of the brain that stores the relevant information, and for some reason when we’re talking about the motor system, it’s not intelligence. I think part of the reason for that is because it’s not exclusively human, because marine mammals make swimming look effortless. Birds make flying look effortless—we can’t do that. And surely that can’t be intelligence because we can’t do it.
If you reduce the theory to intelligence to, on the one hand, this notion of efficient solutions to hard problems, and simultaneously think about it in terms of the energy and resources that neurons require to solve the problem, then in fact, the motor system is arguably more intelligent than the frontal cortex.
Are chimpanzees much less intelligent than humans?
This is a very hard problem, so here are the facts. Chimpanzees are less than 1 percent different from us. In other words, they have 99.6 percent genetic similarity. So just think about that by analogy. Imagine I gave you Hamlet, and I changed less than 1 percent of the text and I said, “What’s the play?” and you said, “It’s Hamlet.” And I said, “What have I changed?” “I’m not quite sure. I did see a few typos.” But the full story would be there, essentially—a few infelicities. That’s the problem.
Why is it that less than 1 percent difference seems, by our perceptions on observations and measurements, to result in such a huge difference in terms of capability? The second point is that humans and chimps diverged about 5 million years ago—we have a common ancestor about 5 million years ago—whereas, humans and chimps diverged from other great apes like the gorillas longer ago, about 8 million years ago, and from the monkeys about 25 million years ago. So we are more closely related to chimpanzees than chimpanzees are to gorillas; that’s the second problem.
The third fact is the following. If I went back in a time machine about 250,000 years and you compared a human to a chimp, while we might look like us today, we wouldn’t have any of the trappings of culture that we associate with intelligence. In fact, we’d be probably less impressive than many chimpanzees! So here are all the weird paradoxes. The sequence similarity is tiny. The biological divergence seems to be a long time ago, but the cultural divergence is very recent. So what on earth happened? No one knows, okay? So we can’t have a theory that says that the structure is very different, that accounts for the big difference in function, because by definition we’re genetically almost identical. In fact, we’re physiologically almost identical; we’re anatomically almost identical.
So it has to be something like a phase transition. It has to be something along the following lines: Imagine that I gave you an ink that was very volatile, such that when you wrote down text you could only transmit it to at most one other individual, and once those people had it and tried to propagate it in turn, the message will have disappeared. If we lived in such a world there’d be no culture; because we couldn’t transmit across many generations what we had learned in our lifetime. So just by changing the chemical constituents of the ink—one little chemical change—you make the difference between no culture and a culture. And I think that’s what we have to look for. There’s something about the capability of humans to integrate over time what they have acquired and incrementally, and collectively, add to culture. That thing is what I call the “exbodiment”—that most of what happens cognitively doesn’t happen in here, but out there; and is stored in the world—in books, in folklore, in symphonies and so forth. How that happens seems to me the key to understanding the difference between chimpanzees and humans.
Do you worry, as some people do, that super smart machines will come to dominate the human species?
Yes, there are the doom and gloom types who say that eventually we’ll build machines so intelligent that they’ll realize that they’re in competition with us for energy, so they’ll turn us into batteries and spend the rest of their existence meditating on their own futures! A super Zen machine. That might be true, but it’s so far away that it’s not that interesting to me.
Here’s something that is true about machine intelligence that’s an immediate crisis and a question for us; and it’s not what people call AI—artificial intelligence. I call it App I, you know, app intelligence. Let me just give you some examples of this, and it has to do fundamentally with the abdication of free will. It is already the case that for many of us when we make a decision about what book to read or what film to see or what restaurant to eat at, we don’t make a reasoned decision based on personal experience, but on the recommendations of an app! So that little bit of free will that we got to exercise in our daily lives has evaporated. So the lake of free will has become a pond.
Let’s just extrapolate a little bit further forward. Imagine, Voter App. Well, I have a certain income. I live in a town. I have certain preferences. I have certain ideals. Let me just enter those into my app and it’ll tell me who to vote for. Now, the reality is [that] it’s amazing we don’t already have one, because it’s clearly better than what we do now because for most people you say, well, I love their hairdo, you know! Or, I like the way they speak or they amuse me, or whatever proxy for decision making they employ. So there’s Voter App.
How about Eater App? Well you know, we are busy people, we work long days. I get home and there’s just so many times I can eat Cheerios or a fried egg. Why don’t I just enter my state of health? I’m fat—I don’t have to because my phone is already recording exactly how many steps I’ve taken today and it knows better than I do what I should be having for dinner. It also knows my taste profile, [and] it understands my pre-existing conditions, which it does not want to exacerbate. So there’s Eater App and Voter App and Reader App and Listener App. So before, long the domain in which we get to exercise our intelligence will be a point. That’s a reality. The post-singularity “doom and gloom” is an interesting speculation, but of not immediate great consequence; and I think the other one is serious and I see it already. I’m not a technology doom and gloom type; I love the stuff. But I am aware that with all of these increments in capability are coming decrements in humanity.
Isn’t it depressing to see how technology is turning us into perfect consumers?
Well, yes! And that’s why I say it’ll be an interesting time for us to reengage seriously with the humanities and the social sciences because this is probably a property of all technological innovations—that they force a crisis. The atom bomb, for example, forced a crisis. We had an extraordinary power and we didn’t really have the moral probity or sophistication to deal with it. We still do not. And that’s not making a judgment about whether our actions were right or wrong; it’s just that I think thinking reasonably about how to deploy power on that scale is beyond us.
I think the same thing will happen here. When we talk about the crisis in the humanities, the role of theological debate—or you pick your preferred form of reasoning about the world—I think that’s where it will re-emerge, because science can’t provide answers to the question of value. And perhaps what’s worrying people now is that the divergence between those worlds has allowed the technology to run its own course on its own terms, which are in fact by and large economic.
How does the domino principle contribute to our understanding of intelligence?
One way to think about culture is as dominoes arrayed in a line with one tipping over another, tipping over another, a sort of cascade of influence. And culture builds on culture. When Newton and Leibniz were formulating the infinitesimal calculus, they were borrowing from previous ideas in mathematics, which in turn borrowed from early ideas of geometry in Ancient Greece. That cumulative process is what allows for the civilization that we now live in, but it raises a very interesting paradox and it’s interesting that it’s very rarely commented upon.
Here’s the thought experiment: Take an old computer. I don’t know, a trash-80—a TRS-80—an Apple II, I mean you pick your favorite old computer. The 1984 Apple Macintosh, any; a UNIVAC going back further. Now imagine I showed you an Apple II running World of Warcraft. If you knew anything about computers you’d say, “Impossible. Impossible!” The Apple II was based on an 8-bit 6502 chipset, had about 16KB of RAM, and it didn’t have a graphics processor, and it was using I think an 8KB videocard to address the screen, if that—probably less. World of Warcraft takes gigabytes of data and incredible computational power to render a scene, so it would be impossible essentially to run modern software on hardware that’s just about 20 years old.
Now, let’s think about human beings. Human beings are hardware that’s about 100,000 years old, but we run string theory, Lie algebra. We’re running 21st-century software! How is it possible that old, antiquated hardware can continue to run ever newer and more complex cultural software? Now, the metaphor might be the problem, right? But it’s an interesting one and that raises a very interesting question about the limits to the cascade, because I happen to be one who believes that the cultural becomes so complicated at a certain point that it won’t run on our brains. And in fact, you could argue that the reason why we’ve generated computational devices is consciously or unconsciously, we’ve come to recognize that our endogenous, organic computing power is not up to the task and we have to recruit machines to represent culture, because we cannot. I think there’s good evidence for that.
So if you think about search algorithms, there’s absolutely no way that the smartest person on the planet could do what Google does; just doesn’t have the memory capacity to do it. And I think that that ultimately might be what bounds us; that we’ll reach a point where our memory capacity and inferential power simply cannot accommodate the latest cultural artifact. At that point what happens? Does it become independent of us, or does it just stop? It’s like evolution coming to an end.
Why do we need the science of complexity to tackle the most difficult questions?
One quite useful distinction that one can make is between the merely complicated and the complex. So the universe is complicated in many parts; the sun is complicated, but in fact I can represent in a few pages of formula how the sun works. We understand plasma physics; we understand nuclear fusion; we understand star formation.
Now, take an object that’s vastly smaller. A virus, Ebola virus. Got a few genes. What do we know about it? Nothing. So how can it be that an object that we’ll never get anywhere close to, that’s vast, that powers the Earth, that is responsible in some indirect way for the origin of life, is so well understood, but something tiny and inconsequential and relatively new, in terms of Earth years, is totally not understood? And it’s because it’s complex, not just complicated. And what does that mean?
So one way of thinking about complexity is adaptive, many body systems. The sun is not an adaptive system; the sun doesn’t really learn. These do; these are learning systems. And we’ve never really successfully had a theory for many body learning systems. So just to make that a little clearer, the brain would be an example. There are many neurons interacting adaptively to form a representation, for example, of a visual scene; in economy, there are many individual agents deciding on the price of a good, and so forth; a political system voting for the next president. All of these systems have individual entities that are heterogeneous and acquire information according to a unique history about the world in which they live. That is not a world that Newton could deal with. There’s a very famous quote where he says something like, I have been able to understand the motion of the planets, but I will never understand the madness of men. What Newton was saying is, I don’t understand complexity.
So complexity science essentially is the attempt to come up with a mathematical theory of the everyday, of the experiential, of the touchable, of the things that we see, smell and touch, and that’s the goal. Over the last 10, 20 years, a series of mathematical frameworks—a little bit like the calculus or graph theory or combinatorics in mathematics that prove so important in physics—have been emerging for us to understand the complex system, network theory, agent-based modeling, scaling theory, the theory of neutral networks, non-equilibrium statistical mechanics, non-linear dynamics. These are new, and relatively, I mean on the order of decades instead of centuries; and so we’re at a very exciting time where I think we’re starting to build up our inventory of ideas and principles and tools. We’re starting to see common principles of organization that span things that appear to be very different—the economy, the brain, and so on. So complexity science ultimately seeks unification—what are the common principles shared—but also provides us with tools for understanding adaptive, many body systems. And intelligence for me is in some sense, the prototypical example of an adaptive, many body system.
What are the big science questions that you’re trying to understand?
So I think most researchers, in the humanities and the sciences, have the very big questions that they are pursuing that they very rarely dare announce, and then the sort of detailed problems that they’re working on day to day. So for me, the very big question is the origin of intelligence in the universe, and in that sense I think rather like a physicist. A physicist would say, why is there something rather than nothing? Where did gravity come from? Why are there planets? Why are there solar systems? And I’m interested in exactly that question: Why is it that on some of those planets, in some of those solar systems, you find life that we call intelligent? That’s the big question.
But you can’t really sort of go to work in the morning and say, you know, how does one work on that? So you work on facets of it. How is information encoded in organic matter, reliably? How do evolved structures like brains and societies and genomes compute? What is the right theory of computation for evolved matter? And how is information reliably transmitted from one generation to another? Are there laws? Are there limits? How much noise can be sustained? And how can that noise be reduced? So those are, if you like, the microscopic research projects that are oriented toward the big question that for me, I’m more than happy to articulate, but many of my colleagues tend to be nervous about articulating.
How does your love of literature inform your creative life?
I’m somewhat obsessed with it. I mean, here’s an example: I write very elaborate projects and schemes for my literary reading in any given year, which are more or less instantaneously violated! So this year my scheme was the history of projects, bridge building, mines, political systems, systems of knowledge, cities, from Petersburg to Middlemarch, and so forth. But of course, then a book comes along like, The Buried Giant by Kazuo Ishiguro and I have to read it; or Helen McDonald’s H is for Hawk and I have to read it!
So okay, why do these things matter so much to me? I’ll tell you why, very seriously. Literature and film are two of the domains in human inquiry where we get to explore the conjunction of the real and the possible, and the unreal and the impossible. Literature is ontological experimentation. It’s experiments in the nature of reality; and what makes it special is that it’s experiments in the nature of the reality through the perspective of individual suffering and joy and so forth. That range is not felt anywhere else. It’s not a part of science. There are theories about this in philosophy. One of my favorite philosophers who very few people know is Alexius Meinong who had an elaborate theory of the ontology of imaginary objects: the existing world—things we see like cups and glasses, cats and dogs; the subsisting world—mathematical theorems—they kind of exist but they’re not like apples and oranges—he called that subsistence; and the absisting world which is the possible impossible objects, for example a square-circle. Now, a square circle is possible in language but it’s impossible in reality; it’s a contradiction. Yet, if you say that to people, they immediately imagine something like a donut as rendered by Piet Mondrian. So the ontology of literature interests me, and I believe that enlightenment, prana, wisdom, is found in literature because it represents the conjunction of the real and the unreal through the subjective experience. I can’t think of a domain that comes closer to fulfilling what for other people they seek in religion, I guess, or meditative practices, than what I find in literature.
Why do you also like less “serious” literature, such as genre fiction?
Serious literature is an invention of the 19th century, meaning realist literature in other words. Ishiguro comes up here in The Buried Giant, you know, why would this serious writer who wrote The Remains of the Day, A Pale View of Hills and so on, be dabbling with ogres and dragons? Seems infantile. But if you start thinking about it in terms of how the real touches the unreal, possible the impossible, so-called genre—I don’t believe in genres! You know, what was Dante? What was Milton? What’s William Blake? Where shall we find them in the literature store? In science fiction? This is economics, not the pursuit of knowledge—those forms for me are brave in daring to explore these boundaries. That’s what I find interesting about them. What makes J. R. R. Tolkien so enduringly fascinating to people in The Lord of the Rings is the fact that he was a philologist that was willing to explore an incarnated form of his discipline. What does it mean to really consider the genealogy of language, when it’s embedded in culture, and connect it to a mythic history of Britain? That kind of exploration, it’s hard to imagine where else it would take place! And so I think every genre, every form admits of excellence. There’s trashy science-fiction and there’s trashy romantic fiction and there’s trashy documentaries and there’s trashy pop music and there’s genius rock music. That’s somewhat independent. The question is how well they, in some sense, abide to the intelligent rules of their form: How felicitous it is, how efficient it is, how they play with combinations in genius ways, and how they surprise us.
What would you be if you weren’t a scientist?
I struggle with this question. I think, at this point in my life … I don’t know, when I was younger, probably you know, a fireman or something, so it depends when you ask the question what one will be. But at this point, I think I’m interested in these kinds of forms. I suspect I would make movies. I think that if I had to give up science I’d start making films, or a weird kind of video game that would allow me to play precisely at these interfaces I’ve been talking about. I like things that have rich structure at multi-temporal scales—that go from the individual scale to the social and larger—the galactic! Standard academic life doesn’t particularly admire that, and so it’s somewhat paradoxical that I find myself in the position I have. Complexity science does, to some degree. But I think some other form of endeavor where I get to play at being an alchemist.
Steve Paulson is the executive producer of Wisconsin Public Radio’s nationally syndicated show, “To the Best of Our Knowledge.” He’s the author of Atoms and Eden: Conversations on Religion and Science. You can subscribe to TTBOOK’s podcast here.
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