For 300 years, the dream of science was to understand the world by chopping it up into pieces. But boiling everything down to basic parts does not tell us about the way those parts behave together. Physicists found the atom, then the quark, and yet these great discoveries don’t answer age-old questions about life, intelligence, or language, innovation, ecosystems, or economies.
So people learned a new trick – not just taking things apart but studying how things organize themselves, without a plan, in ways that cannot be predicted. A new field, complex systems science, sprang up to explain and navigate a world beyond control.
At the same time, improvements in computer processing enabled yet another method for exploring irreducible complexity: we learned to instrumentalize the evolutionary process, forging machine intelligences that can correlate unthinkable amounts of data. And the Internet’s explosive growth empowered science at scale, in networks and with resources we could not have imagined in the 1900s. Now there are different kinds of science, for different kinds of problems, and none of them give us the kind of easy answers we were hoping for.
This is a daring new adventure of discovery for anyone prepared to jettison the comfortable categories that served us for so long. Our biggest questions and most wicked problems call for a unique and planet-wide community of thinkers, willing to work on massive and synthetic puzzles at the intersection of biology and economics, chemistry and social science, physics and cognitive neuroscience.
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Michael: So David, this is the episode where we lay out what people can expect for this show. What does it mean to be a complexity explorer? And how is SFI intending to facilitate this for people?
David: Yeah, I think we want to expose people to a different kind of science, one that they might not be familiar with, a different kind of personality from the usual rather dull representation of scientists. Someone willing to take some risks, present ideas that people haven't heard before, and be honest about the hubristic enterprise that we're engaged in – which is trying to find big unifying theories for the complexity that we live in.
Michael: So let's start there, actually. I think when most people think about the project of science, they're imagining that we're sort of tilting the whole human knowledge enterprise towards this single, all-embracing theory of everything. And I know from our discussions that that’s not only not the only kind of science that happens, but that’s not necessarily the kind of science that we're focusing on here. How do you understand that sort of enterprise in light of what we've learned over the last 35 years studying complex systems, and how that project differs from the kind of popular perception of scientific process?
David: Yeah. There was a time in the '80s and early '90s when it was very popular to talk about GUTs, Grand Unified Theories, theories of everything. And these were prominent in physics. I like to say that these theories of everything were theories of everything except those things that theorize, right? Because they had nothing to say about life on the planet.
So even the Grand Unified Theories, the most ambitious, the boldest, were really very limited in their scope if you actually looked carefully. They were trying to explain the structure of the atom, the way in which gravity emerges, which remains an open question. So even Grand Unified Theories are not as grand as you might imagine. In a sense, what we do here is more ambitious. We declare that in the vast space of adaptive phenomena that span biology, culture, civilization, and technology, there are rules and regularities that can be expressed in a common mathematical, computational language. And so things that are currently taught and investigated in very different buildings and different departments by very different people, actually have much more in common than you might think.
And so for example, someone studying metabolism, say, in a biology department, might have something to say about the economy. And someone studying computer networks has, clearly, something to say about the brain. So we're interested in those, and we're interested in theories that can, in some sense, span fields that look as if on the surface when you superficially investigate them, have very little in common. Now, will that coalesce into one giant amorphous theory of everything? I hope not, but I don't know.
Michael: So one example here would be if we scale up this even past the sort of framing of this as different quantitative approaches, there's a question about the incommensurability of different fields of knowledge with respect to, say, the sciences and the arts. And I'm curious just how divergent you think an aesthetic or an empirical inquiry really are from one another, and how you see those differences.
David: Yeah. So one way to express complexity: it's that domain of reality that balances the random and the regular. And both of those are difficult concepts to understand in themselves, but it helps to answer your question. Science has almost nothing to say about systems with lots of randomness or lots of what we would call contingencies, or in the formal language, lots of free parameters. It really likes compact, small, elegant encodings which do very well at the regular end of the spectrum. Now you can actually think about the arts as being that domain of experience which are incompressible. There are so many specific elements in them, so much idiosyncrasy, so much individual expression, that they don't lend themselves to compression. And that's precisely why they will never be expressed in the mathematical language of physics.
So complexity actually provides a partial answer to why these domains of inquiry appear to be incommensurable, but they're not. It's just that they live on this spectrum of the random to the regular, that lend themselves to different languages of expression. I think the novel is an almost perfect platform for theorizing about reality when the reality you're describing has a ton of idiosyncrasy in it, as opposed to calculus or the theory of dynamical systems, which is really good at expressing and encoding a reality that has very little randomness in it, almost none, to be honest, and lives in what we would call low-dimensional space.
So that's one way of getting at it: complexity actually helps us understand why these different forms of expression exist. The other one that I find really intriguing is, we're engaged in different ontological and epistemological exercises. Artists are basically in the business of world creation, and scientists are in the business of world explanation…as are humanists, actually. But the difference is, scientists are in the business of explaining empirical reality as experienced, and humanists explaining artistic reality as created. And the rules of the game in creating worlds and explaining worlds are different. One is very individual and idiosyncratic, and one is very collective.
So again, I think the agenda is a little different, but you can see how they relate, because actually, there's an element of science which can be about world creation. You can think about counterfactuals: “What if there was no gravity?” Right? “What if there were nine sexes instead of two among hominids?” These are all counterfactual ontological world creations in science. And artists similarly can say, "You know, I'm going to paint this painting, but I only have so many pigments, and I still have to work within the visible spectrum of color." So they have to deal with empirical reality to the same extent that we create realities.
Michael: That's also a kind of a compression: the limitations of our tools, and the limitations of the affordances of our technical environments, which sort of segues into the question of the role of the engineer on that spectrum. Like, where would you place that sort of role in this ecology of social functions and modalities?
David: Yeah, the engineer is interesting. The great engineers combine the both, right? They create worlds which they then theorize about. And of course, these are simplifications. Great scientists are artists, because they spend most of their time creating worlds that don't exist, to demolish them. Great artists are very, very familiar with the constraints of their tools in empirical reality, because it allows them to be better world creators.
And so it's interesting, in the end, at the limits, there are distinctions that are worth making. But the metatheory that allows you to understand them as living on this kind of ontological continuum is useful, because it breaks down barriers, because we're constantly in contact. I mean, my artistic friends are very interested in their tools and instruments and the theory of optics or the theory of acoustics. And scientists are often fascinated by, "How do I more effectively explore a counterfactual reality? How should I liberate myself from the rigors of my discipline to make a discovery?" And I think this is why, here at SFI, we put them together, because we believe that the best ideas, I don't care if it's scientific or artistic, come from a collision/fusion of those different sensibilities.
Michael: So I think a lot about this in terms of the way that the act of seduction is posed as an inherently risky enterprise. You're releasing or relaxing your own personal boundaries to invite in the other; and similarly, both art and science, as well as other human knowledge enterprises, are often communicated/self-described in terms of exploration and risk: stepping out beyond the known. So how do you understand science and the Santa Fe Institute's work as a risky endeavor? And how does the kind of risk that's assumed here differ from the kind of risk that is being taken on by other institutions?
David: Yeah. I mean, we've talked about this, and I think we're not sophisticated enough about risk. So people think of it as a number, say, "How risky are you?" "I'm like a 0.7 or something." But it's not a number, it's actually maybe a list, and people differ in their risks. Some people will not take risk for their money, other people won't take risk for their reputation, other people won't take risks with their lives. And I think SFI, in that vector space theory of risk that has all these different components, is very willing to take risks with its reputation and stepping out of the comfort zone of a discipline. We're very good at that.
It doesn't matter to us whether or not there is a community that says you are of us. We kind of like the solitude of discovery in that respect. And because we are not a big data, big infrastructure institute, we're very rarely taking risks that are financial, because a significant financial investment for the Santa Fe Institute is a pencil, and you're not really taking risks with lead when you write down the wrong theory. So our risks are different from a large lab, for example, which is in some sense taking a risk by performing a massive experiment that might prove to be wrong. I mean, huge investment in grad students and postdocs, in machinery and in physical plants.
They are taking that kind of risk. They're not really taking a risk of ideas, because usually, those experiments are very incremental. We talked about LIGO, this incredible, ingenious engineering endeavor to detect gravitational waves to test a theory that we already knew was true. So that experiment in general relativity wasn't a risk in the world of ideas in any way. It was a risk in that the instruments might not work. That you spent so much money, and you didn't detect a gravitational wave. If you hadn't, would they have declared general relativity wrong? No. So in that sense, exactly wasn't a risk with an idea, because the idea would have held. They simply would have said, "You know, our detectors were too sensitive to small seismic perturbations in the environment." So SFI is the opposite of that. We don't invest tons of money in big machines to test accepted theories. We invest the small amounts of money that we have in creative minds to create theories that are not known and that do not exist, and that might offend existing bodies of knowledge.
Michael: Listening to this, there's almost a kind of population biogeography consideration here, and that the way that ideas develop and the way that intellectual risks are taken is dependent on the scale of the population that's actually engaged in that. There's the notion that genetic drift sort of buries novel mutations on the mainland. So where do you place SFI on a continuum? Like, if you were to think of sort of the lone, weirdo explorer out on the mountain versus these massive institutional enterprises, how do you lay out the geography of where to look for these different types of action and exploration?
David: Yeah, and again, I'm a little obsessed with this metaphor of mountains, monasteries, and the metropolis. There are things that you will discover, let's say, about yourself and the world, when you're hanging from a ledge at 2000 feet that you probably won't walking across the street in New York City. But there are things you'll learn walking across a street in New York City that you won't learn on a mountain. And there are things you'll learn on a monastery in the high mountains, through community, that you couldn't learn in the other two cases. So essentially, my view is every creative mind needs to pass through all three environments.
There is a period where you need solitude to maximize the entropy rate, maximize the exploratory side of creative thought. There is a time we need to then subject those ideas, most of which are probably insane, to the rigors of your community who are on your side. And having tested them, bring them to the world in the metropolis. SFI is a monastery in the mountains, and in some sense, it's my job to allow individuals the freedom to climb and to engage in the sort of ceremonies of the monastery that hone their ideas to a point that they can be presented to the world in the metropolis.
Michael: Seems like the mountain, monastery, metropolis also maps on to first person, second person, and third person methodologies. That you have, like Depraz, Varela and Vermersch, in On Becoming Aware, talked about knowledge starting in a sort of intuitive perception, first person phenomenological or epistemological thing, looping back to what we're just talking about a moment ago. Moving out of that personal domain into a domain of intersubjective and hermeneutical discourse, and then beyond. I guess, do you regard science as in some way, honestly, a reduction of ontology to these other forms? Or how do you relate that?
David: Yeah, it's interesting. I think as scientists we’re not very good at thinking about the environments and the process that would be most effective. Most of us grow up in small groups or in labs, and we learn our trade, and then we simply repeat what we've experienced. And I think SFI represents, in some sense, a very self conscious experiment in the needs of the scientific pipeline. I think we've discovered over 30 years what creative individuals want. And so I think you're absolutely right. I think there simply is a time where the scientific process looks like art. It's that first person assertion of the ego, right? It's, "This is what I need. This is what I think to be true."
And then it comes into conflict or contact with ego, which is what you know. And in a way, you're the first line of defense against your own worst ideas. But then those ideas that survive, confront the super ego, the social structure, our communities, more largely what you're calling the third person. So I think you're absolutely right. I think there is this very interesting recapitulation of the individual arc of discovery and the institutions that we inhabit, and we haven't been, I think, as thoughtful as we might be about how those should align. The appeal of SFI is that we're adding, if you like, to the ecology of institutions, some of those earlier phases that I think are often missing in the large scale production of science.
Michael: There's another piece in this if we look at this, pardon the sort of biblical analogy, but it seems as though often there's an evolution of religious institution that starts with some sort of desert patriarch or some sort of lone mystical experience that is then tested in the same way, congealed. And at some point it moves from a kind of a mystical experience to an attempt to navigate in a meaningful way, or in a sort of extension as logical conclusion, actually like control the world. That we move from St. John the Baptist to conversations about Catholic dominion. And philosopher William Irwin Thompson described this as actually appearing in two different forms of science, the Pythagorean approach that is open to the transcendent, and an Archimedean approach that's focused on system control. So do you see one of these approaches as more intellectually honest than the other? Or do you think that in general, the scientific community has changed and it's sort of balanced between these points over time?
David: Again, it's sort of interesting. These seem like these loose concepts, but I think we don't reflect on them enough. I think these archetypes are real. Certainly SFI, by your definition, is much more on the side of Pythagoras than Archimedes, but without Archimedes you wouldn't have Pythagoras. In other words, tools have to be built, machines that amplify our ability to reason need to exist. And so they're completely compatible. I think it is true, though, that most academics I think, would say that we've moved a little bit too far on the industrialization route. I mean, the Archimedean impulse is a little bit too strong. It's maybe much too strong, a bit like the tail wagging the dog at the moment.
And given that you need that sort of Petri dish of mutant concepts to be emerging all the time, some of which are really useful, if you spend all your time in production, and not enough time in creation, eventually, the sources of our inspiration or ideas will run out. And I think, again, on the landscape, SFI is there to be that Petri dish. It's there to support the Pythagorean impulse, and I do view these things in very complementary terms. I don't think you want every place to be like us, but you need us. I was very struck by one of our founders, Murray Gell-Mann's classical archetypes. He was very interested in what he called the Dionysians, who are essentially seeking immediate insight into reality to experience it directly. He contrasted those to the Apollonians, who took those insights and abstracted them and distilled them, and were interested in a much more rarefied product. But in between the two were the Odysseans, the explorers who enjoyed the sensual pleasures of the immediate, but were in communication with the gods. And that's sort of what we're after, right? The Odysseans. That's a classical reference I find useful.
Michael: It's interesting because again, to bring this back to ... In a way, that is a complex systems definition of diverse models that are performing a kind of collective computation, where we look back even into antiquity, and it seems as though the real action going on evolutionarily is in some combination between all of these different approaches to reality: the temple religion versus the wilderness mystics, etc. And it feels as though there's a modern instantiation of this in the relationship between complex systems science and machine learning. I mean, it seems as though those are ... I've heard you describe this as,bv these are like sibling disciplines.
David: Yeah. So there's two issues here. Right, to answer this properly we have to understand what complexity is, and complexity is this domain of reality that straddles the very regular and the random. And science has been really good at those two limits, right? And so one limit, classical mechanics, and the other limit, statistical mechanics. And both are powerful theories, one dealing with, if you like, crystals and the other one dealing with gases. The perfectly ordered and the very disordered. And in the middle is where it all gets complicated and complex, and that's where we live at SFI.
Now, what that's done, because science is not very good there, historically, is generated two possible approaches. One of them is complexity science and one of them is machine learning and AI, and they do different things. Machine learning and AI takes all that complexity in, encodes it in big models like deep neural networks, and makes predictions, but those predictions are completely opaque and don't give anyone an understanding as to how they were reached. On the other hand, you have complexity science, which tries to, in Murray’s language, take “a crude look at the whole.” It tries to find the right scale at which you can do theory of these adaptive systems, if you like, in the center, with a view to not producing perfect predictions, but generating real insight, explanation for why they exist.
And I think we're now entering in the 21st century, a new kind of scientific schism where we're going to live with two very different ways of engaging with reality. A machine-based, high-dimensional, very precise predictive framework that is a black box … and ours, which is a more familiar framework from the history of science, if you like, but that is faithful to the complexity of the systems we study, which doesn't predict so well, but does allow us to understand the basic mechanisms generating the phenomena of interest. And that's where I think complexity lives, and it's going to have to come to terms with living with machine learning and AI. It's almost as if we've returned, to use your biblical metaphors, to the Cain and Abel, and those two brothers are going to have to get on as opposed to one killing the other.
Michael: It's funny because it's a very different situation than – to make this somewhat institutionally autobiographical – than we were looking at in terms of the application of computational resources at SFI in the ‘90s, and the legacy of this organization in terms of popular texts that have emerged around cellular automata and that kind of thing. So it seems as though there's a very clear, shared origin story there. One thing that I'm constantly drawn to is this question of whether there are pulses in almost like a market sentiment analysis of just how unified our knowledge can be, to bring us somewhat back to where we started. And that we're at a moment in history now where maybe we're in one of these pulses where we're overwhelmed by new methodologies and new data, and we're at a point of differentiation rather than integration, or do you think that's over-simplistic?
David: I don't know. I think the history of insight is building physical or cognitive artifacts to allow us to reason through complexity. When you go back to the earlier SFI, it said the big problem is emergence, right? So the history of science is the history of reduction. To understand is to take something apart and look at its constituents, so when we’re kids, someone gives us a radio or a car and we just take it apart. And there is some insight to be had by looking at what makes something up, but the harder problem is to put it together again, right? And that's emergence, right? That's the other side. That's the construction side of science. The history of science is reduction, the future of science is emergence. SFI in its early days, if you like, was trying to come to terms with how simple systems spontaneously generate structure, because we felt it would help us reassemble the radio. And I think it has. And what's happened over the last 30 years is we've developed better and better tools to give us deep insights into emergence – which if you like, is the practice, the intellectual practice of understanding the origin and construction of adaptive form.
And it's become more empirical, right? We now know more from empirical work to couple with those early toy models. I think that's really key. I mean, if you think about one kind of science it's the LHC, massive particle colliders that break things into the most elementary constituents, and we are at the absolute opposite end of the spectrum. We're saying, what constituents when combined with appropriate rules produce completely novel kinds of structure? And what are the right theories to allow us to understand that? And that's SFI’s game.
Michael: So in that sense … we’ve spoken about this before, about differentiating models and theories, and then also the difference between, say, prediction and understanding, which you've touched on already here. So obviously these exist in some sort of ecological balance, and I'm curious, do you think that we're shifting into a new practice of science in which models matter more, theories matter less?
David: I do think so. And it comes down to utility and prediction. So let's try and make that distinction clearer. So let's imagine a billiard table (or a snooker table if you were raised in Britain). You can build a model of that and turn it into a game that you can play on your phone without understanding the fundamental theory of the conservation of energy and conservation of mass, right? You put into those models Newton's laws, and you have a frictional surface, and you have near perfect elastic collisions, all the things you put in, but to understand where that comes from, where those laws come from that allow you to make your game, you need to understand the second law of thermodynamics. What is entropy? What is friction? What's happening there that the ball eventually stops on its own, right? And that's not obvious; that doesn't fall out of classical mechanics. Where does the conservation of energy and mass come in? Why doesn't the ball just spontaneously evaporate? Well, to understand them, you have to go back to fundamental principles of symmetry that were worked out by the mathematician Emmy Noether. So theory, in a sense, is giving you the bigger insight into why the rules that you're using are possible in the first place, but that doesn't necessarily make a better game. That doesn't make a better billiard table. And that's always been the challenge.
Where that was reconciled, in a sense, was in the creation of the transistor out of the vacuum tube in the history of digital circuitry, where an understanding of quantum mechanics, the principles and theory, actually helped. And that's always been a tension. And so given society's obsession with immediate gratification, the model mind, the modeler, the person who builds things that are immediately of utility – or pleasure, in the case of a game – tends to be emphasized over the reason why the model can exist.
Michael: That also seems to play into an evolutionary theory about a trend towards reduced algorithmic complexity in our models. It shows up in science as parsimony, as aesthetic, and that ultimately there it is again, that sort of bedrock into, in some sense, an ultimately aesthetic concern. I know Sabine Hossenfelder has talked about this, whether or not beauty is leading the practice of science astray. I mean, do you worry about that?
David: Well, I actually think, again, that's sort of a topic for this series is would I call complexity aesthetics? There's a standard scientific aesthetic, which is really, in some sense, best represented in modernism and abstraction, very minimal. Think Mondrian, right? Think analytical cubism or synthetic cubism. But you know what, there were other traditions too, like the baroque, which is not like that. That’s also aesthetic. There's a very interesting question moving forward in the mathematical theoretical sciences, whether or not there might not be another kind of aesthetic that isn't so beholden to the austerities of modernism.
I'm extremely excited about this. I actually think one of the things that we might explore is this new aesthetic, this new aesthetic of complexity, which is still an aesthetic. Sometimes it irritates me that mathematical physics hijacked aesthetics in the service of only one artistic tradition. And it's an open question, I think, to most of us here, whether the sort of minimal notion, the sparse notion of beauty is the correct one for the complex world.
Michael: Again, it gets back to that issue of sufficient complexity in order to adequately map the phenomenon, right? There's something about that when I look at the curve there, the sort of parabolic arc between something that is all description and something that is all investigation, like open-ended inquiry, that reminds me of the way that SFI has historically talked about life itself as a phenomenon that occurs right there at the threshold of criticality. It's perhaps crass, but to the degree that we can describe this as a sort of appropriation by physics of biology and of neuroscience, do you see the work that's going on here as a symptomatic or recursive in some kind of way? That we're not at the wheel anymore, that we're moving towards an attempt to optimize strategies for navigating an uncertain world?
David: Well, I mean, let's get back to this notion that was in the early days of SFI there was this debate – and I remember reading a transcript where Stanislaw Ulam, one of the tutelary geniuses of the institute in a sense, at least his spirit made the following observation. He said, "Ask not what physics can do for biology, but what biology can do for physics." And I think, in some sense, that sums it up, because we're very used to the idea that the more, if you like, mathematically rigorous disciplines are nursemaids to the more descriptive ones. But actually, now we've entered a world in the world of complexity where the opposite might be true. Where the insights for example of evolutionary theory, and in particular, its mathematical form, might be necessary for physics to advance, and it's just not something we're used to, and it might change the practice of physics. It'll probably change the whole discipline of physics. I think we're very interested in that disruption. So when you talk about exploration and uncertainty, one of the uncertainties that we of the community have to embrace is the possibility that the fields that we've come to know and love and deploy are going to be taken to pieces. And that would be wonderful. In fact, that's what science is all about.
I mean, science is the most, as I said, disrespectful activity created in the history of human culture. It's fundamentally disrespectful of everything that came before it. If it is respectful of what came before it in some deep sense and beholden to its history, it will not advance. I mean, that's almost like another difference between the humanities and the sciences. The sciences are this kind of very petulant, almost childish rebellion that's necessary for discovery. And sometimes, as you know, the personalities have to reflect that cognitive requirement. And so I'm very interested in that idea. I'm interested in disrespect and challenging authority. I think it was Richard Feynman who said something like, “Science is the belief in the fallibility of experts.” That sort of idea: that nothing you're told is really ultimately true until you've proved it yourself. I love that idea, and I think sometimes the way we're presented as academics, as authorities, as holders of the truth of perpetuating bodies of knowledge is extremely regrettable. I would like this to be an environment where everyone's constantly kind of feeling a little itchy and ready to go into combat for their own idea in a somewhat civilized fashion.
Michael: Odysseus.
David: Yeah, the Odysseans. But at the end of it, make genuinely new discoveries that challenge the existing bodies of belief.
Michael: So here's a real brick and mortar example of this, it seems like this is a symptomatic of a larger cultural movement from an emphasis on, as James P. Carse would put it, the “finite game” of accumulated merit (largely a narrative past oriented historical enterprise that you're putting in the humanities), and an infinite game of endless cultural renewal – a sort of phase shift again, where we stand here at the beginning of the 21st century on the precipice of moving out of a sort of solid model of knowledge into, like, a fluid model.
David: I think it's always been fluid though. I don't think that's temporal. I think that there are factors or elements in society whose job is to maintain fluidity and to challenge sclerosis and status and the settled. SFI lives a little bit on the peripheries of the mainstream, because it's that fluid element. And I think there have been other institutions that have played that role historically, but it requires a special kind of mind and disposition, a special kind of support, to allow that kind of thing to continue. But I wouldn't say it's about the modern world, I tend to think that's overstated when people say this. It's always been true. There's always been a central core dogma, and on the periphery those who assaulted and challenge it with new ideas, and we are absolutely on the periphery.
Michael: So in that sense, the concrete example that I can think of to wag our own tail a little bit here is how SFI is now, through Complexity Explorer, the educational program, about to launch this Origins of Life course. That is a course where it's very clear to see that it's not an education about a settled field that someone has to come into from the outside like a crusader and then topple. It's an area that is still, if we're talking about this with a volcanic metaphor, it's still molten and it's still running, and that marks a kind of a pedagogical shift from an emphasis on what is to what might be.
I'm curious, what do you think as far as this particular origins of life course, how do you see that as a sort of exemplar of the intellectual activity of this place? And also, this might be kind of tangential, but what do you see as the great questions remaining to be explored in this area that are, in their way, so core to the bigger questions that are explored here?
David: Yeah. I mean, one of the questions is what are the fields that demand fluidity? That stand to be disrupted or challenged or require an infusion of novelty? And the origin of life is clearly one of those. It's a field that's rooted in the singular nature of DNA-based lifeforms now. I think SFI's contribution to this is to expand the concept of what life itself means. And after all, what we would ultimately like to know is, to what extent is life on Earth unique – and to answer that question in a principled way, we need a definition of life which is not bound to the chemistry and geology of our Earth.
So it is a good example because it's a field that's open. It's a field that's very expansive, and SFI has a long history in analyzing this problem from very different perspectives. Early on, artificial life was, "Okay, let's build life in a computer." What does that even mean? Some people would say that's not possible because life is building life. It’s a cheat, because computers are built by organic life forms. It's not the origin of life; it’s the origin of something else. And that's an interesting question, and [inaudible] would say no. If you set up the conditions appropriately, that's no different from doing an experiment in a lab, which after all was also built by a life form.
So yeah, I think the distinction I often like to make – that captures the origin of life, the origin of consciousness and intelligence, and many systems we study – is between, let's call them the biological naturalists and the functionalists. The naturalists say, “There's only one way of making something. It's not life if it isn't made out of these components, it's something else.” The functionalist say, “No, there is a computational mathematical description which transcends material, and what we would sometimes call universality, and that's what we should be searching for. And so we can make a superconductor out of many different materials. It's not rooted in one, even though it might have been discovered in one.”
I think that tension plays out a lot at SFI between different communities, especially our relationship to other institutions, because we tend to believe in universality. That there should be theories that transcend matter. It isn't that they're not rooted in it, but not in one particular form, and there are other institutions that tend to focus on one. It's the dialogue between those two in origin of life research that actually makes it so interesting. If it was only one side or the other, I think it would feel a bit stale, but the fact that we're constantly at each other's throats makes it kind of exciting.
Michael: Looking forward to this interview series, what are you most excited about – in terms of like, what can we expect from studying the personalities, the biographies, the origins, the destinies of this scientific research community, the Miller scholar authors involved in this…? Are we just walking into a great open question here?
David: Yeah. I hope so. I mean, I think in part it's to allow people to see inside of a very untraditional community of mavericks and how things really work. When we go to school or university we're given this highly sanitized introduction to ideas as if they were produced by some disembodied Vulcan, like no emotions and never made mistakes and never got involved in arguments, and it's just not true. And so part of it is the humanity, if you like, behind inquiry, I'm very interested in that.
And the other is the kind of science we do. I think people don't learn this in schools yet, and the next generation will, because we're starting to write those textbooks, we're writing those monographs. But for those who haven't yet been exposed to complexity thinking, it will be exciting and perhaps surprising to know that an economy can look like a brain, surprising to know that cities are like giant organisms. These are insights that we've made over the last, let's say, 20 years, and haven't diffused out into society, and I think the combination of the kind of fallibilities of individuals in pursuit of almost impossible questions should be of interest to people.
Michael: So really, to challenge assumptions, to encourage curiosity.
David: Absolutely, at every level: community, individual, collective, intellectual. There is a world of individuals on a mountain in the high deserts doing very strange things in the world of knowledge.
Michael: Well, this will be fun.
David: Excellent, right.