 Hello, everyone, and welcome to the Circular Metabolism Podcast, the bi-weekly meeting where we have in-depth discussions with researchers, policymakers, and practitioners to better understand the metabolism of our cities, or in other words, their resource use and pollution emissions, and how to reduce them in a systemic, socially just, and context-specific way. I'm your host, Aristide from Metabolism of Cities, and in today's episode we'll talk about complex adaptive systems, Kleiber's Law, and how it is possible to predict, based on the size of cities, how many patents it's produced, how long are all of its roads, or how many violent crimes have been committed. I can imagine that you might be lifting an eyebrow right now, but rest assured, all of this is possible, and we have an expert to explain all of this. I have distinguished Professor Geoffrey West from the Santa Fe Institute, and Geoffrey is a physicist that has worked on particle theory at the beginning of his career, and then shifted on focusing on power laws and scaling laws in biology, and later on in urban systems, and we'll see how that is possible. He has written all of this, and you can find all of these information in his book, Scale, the Universal Laws of Life, Growth, and Death in Organism, Cities, and Companies. Just before we kick off, I'd like to make a small request from you. I'd like for you to share this podcast with everyone that you think would find this interesting, so you can do that by reviewing it on Apple Podcast, on Spotify, or even on YouTube if you're watching it on YouTube. With all that being said, hi Geoffrey, and welcome to this podcast. Could you please introduce yourself? Yes. Thank you, Aristide. Thank you so much for inviting me. I'm looking forward to our conversation. My name, as you said, is Geoffrey West, and I'm a professor at the Santa Fe Institute, and I am a physicist by training. I mean, as you said, it's been much of my career thinking about quarks and gluons and string theory, and dark matter, and in more recent years, biological questions have now become very seriously interested in cities and companies and sustainability, and some of the big questions we're facing on the planet. I'm wondering how one shifts from, you know, particle theory to biology. I mean, you have some explaining here saying that before going to biology, you were interested in life and death in general, some philosophical questions, and that perhaps led you to biology or your shift to biology? Yes. Well, I'll try to keep it brief. But yes, you know, I've always been interested in sort of some of the big questions, I mean, sort of, you know, high school questions, you know, what is life, the meaning of life, you know, what is it, et cetera. And, you know, that's in a way why I got originally into high energy, fundamental physics, you know, that is the most fundamental questions in a way in the physical world, but as much as I enjoyed it and so on, it of course doesn't answer some of the even bigger questions. I mean, the origin of the universe is a big question, but, you know, it doesn't somehow our place in relationship to that, maybe. And so some of those kinds of questions. So that's always been there. And but the shift took place mainly in the 90s, the 1990s, and I was in my 50s and beginning to be conscious of my own age. And as you sort of intimated, I've always had a sort of morbid interest in death, you know, it's always fascinated me. And I became interested in my own aging and mortality, partially because I happened to come from a family of short-lived male. You know, most of the males in my family died in their 50s and early 60s. My father died when he was 61, my grandfather when he was 57, and so on. So I grew up with this idea that I was expected to die in my best, in my early 60s. And here I was in my 50s, and it didn't look very far over the horizon. And I became very interested in that question. And, you know, it turned out it was a time when there was a lot of turmoil in high-energy physics to do with the building of the superconductor, superglider. And one of the things that was sort of being quoted a lot at that time was sort of an attack on physics, that, you know, this statement that I'm sure you've heard, and many of you listeners also, that physics was the science of the 19th and 20th century, and biology will be the science of the 21st century. Well, I heard that, but I also heard the corollary that was, therefore, there's no need to do any more physics. And so I reacted very strongly to that, knowing nothing about biology, by the way, saying that, well, biology, yes, obviously biology is going to be a major, if not the major, major science of the 21st century. But it isn't going to be a serious science unless it starts to somehow absorb some of the paradigm methodology and ways of thinking of physics. So this was very arrogant. Can't imagine it was what it received, yeah. So, you know, it was typical, by the way, of the physics community, unfortunately. But it, so I would, I reacted this in this way, and then sort of one day I thought, you know, if I believe that, maybe I should sort of put money where my mouth is, and start thinking about biology, you know, is it really true? I mean, I don't know. And since I had this interest in mortality, I thought that would be an interesting place to start. And I learned very quickly, something that surprised me, two things I learned. One thing was that this, despite death being probably the second most important event in an organism's life after its birth, if you looked in biology books, textbooks, there was always no mention of mortality or, you know, why you died. I mean, it was just sort of, there was a lot about, you know, reproduction and birth and life history and so on. But essentially nothing, I could find nothing. And I even went to the literature, spent time in libraries in those days, still real libraries. It would be much easier today when you do Google. But I went, this is the 90s. And I discovered looking at the literature, but it was pretty poor. It was sort of a backwater of biology. And then I also learned something that was a question I had asked myself, not only why do we age and die, but what sets the scale of lifespan? Why a hundred years? Why not, you know, a thousand or a million? Or, you know, why are we all dead by 10 years? What is it? And so that was one thing I realized that biologists don't think in those terms. And secondly, and by the way, we're going to talk about cities. When I got into cities, I discovered people doing, I don't know, urban, I don't know what you want to call it, but thinking about cities and everything, also don't think about or haven't been thinking in those terms, you know, asking those more quantitative questions. But maybe we'll come to that in a little bit. But so the other thing I learned was that I realized that if you want to understand why something dies, you've got to understand what's keeping it alive in the first place. And therefore, what's gone wrong, you know, what's happened, that the mechanism for sustenance is somehow degraded. And so, you know, I realized that that was to do with metabolism. And then I learned that there were these, by just reading books, there were these extraordinary scaling laws in biology, in principle, mostly, I mean, the most famous thing to do in metabolism, we'll talk about that, and that relates to metabolism of a city ultimately, but metabolism for an organism. And there were these extraordinary scaling laws, which I found amazing because they were quantitative and there was data. And they were amazing because who would believe that the most complex phenomenon in the universe, which is evolving and adapting, where everything about the system, we believe, is historically contingent. I mean, every organ in your body, every cell type, every genome depends on its history. I mean, that's natural selection and evolution. So if you believe that and you plotted anything, you see these points sort of, you know, not necessarily randomly, but spread all over the graph, because that would reflect the fact that it's been historically contingent, accidents happen. It's sort of a random process out there. So they would be, and here they were, these lines, these points lying beautifully on one line. I thought, my God, that's, there must be, this must be a major area of biology. Well, it had been, it had been in the, before the war, before the Second World War, but the molecular evolution, the focus on what the elementary particles, so to speak, of biology and the discovery of genetic code and blah, blah, blah, had shifted attention completely away from it. And I learned there was no serious explanation of these scaling laws that have been forgotten. And I thought, oh, this is great. Here's something quantitative. Here's something that a physicist can think about, and no one's thinking about it. So I can in the, you know, when I'm not doing quarks and gluons, I can think about this. And that's what got me going in understanding. And the first problem that I worked on was indeed to understand the origin of the scaling of metabolic rate. And once I got into that, and I met up with some wonderful biologists and I collaborated with eventually, my, I didn't realize at the time it was not my intent, my whole trajectory of my scientific career gradually took a complete change of direction. And, and I totally serendipitous, totally, you know, unexpected. Like an evolution somehow, yeah. You know, I have, I mean, I'm very lucky because I enjoyed it immensely as much as I love thinking about string theory and fundamental questions in physics, where, you know, you work extraordinarily hard and you have to be extremely clever. And if you're lucky, you make epsilon progress, which is usually not appreciated by your colleagues even. And suddenly there was this, all this biology was sort of, I suppose in the language, almost low hanging fruit for a physicist, I felt, which somehow had been neglected. So that, that was, that was great. Actually, it took a long time, by the way. I mean, I say it as if it was easy, but you know, the transition and actually doing some serious work took of the order of two years. So, I think along a long story, I'm sorry, but that's no, no, no, I think I want to go back to this. So what existed already, which you said back in the before the Second World War, I think you referred to Claibor's law about exactly. Could you a bit elaborate what were these axes where he was looking at this perfect line? And what does it really mean? Yes, exactly. So I'll talk about Claibor's law in the second. But the other thing I wanted to say was that many scaling laws were discovered during the thirties and forties into the fifties. And they have been, and I was going into the sixties. And I was extremely fortunate that three books had been written in the late seventies, early eighties, has summaries by biologists of all these scaling laws without any explanation, but just, you know, talking about them. And with wonderful compendium, it's like a compendium. And without that, I probably couldn't have done anything just having all those. Anyway, so the original law that started all this was discovered, I think it was 1932 by a man named Max Claibor, who was a biologist at the, what's now the University of California, Davis. And he was a, he was Swiss originally, but he was interested in metabolism. And he was one of the first people to plot logarithm, you have to plot them logarithmically, whether you like it or not. Because if you want to put a mouse and an elephant on the same graph, you can't do it with linear scales. So you have to plot them logarithmically going up by factors of 10. And he plotted them on a graph, he did some of his own measurements. And he found that they fit perfectly on a straight line. And essentially perfect. I mean, it's amazing actually. And he also discovered that the slope of that line was very close to three quarters. And he just said, look, you know, the best fit is, you know, I think it was 0.745, but three quarters. And he pronounced, okay, three quarters. And there was actually a little argument at the time between biologists who think, well, you shouldn't say that, you know, when he got it right, as far as I'm concerned, he said three quarters. And so, and that's again, just to repeat what I said, it's sort of amazing because each one of those organisms and each subsystem, each cell type, each genome, of course, has this historical contingency. And yet they line up on this line. That was the first thing. So that this curious number three quarters, not two thirds, which would be the most naive, well, the most naive, the one is linear, that you double the size of an organism, twice as many cells, it goes linearly. Well, it was clearly not linear. And then you'd say, well, maybe two thirds sort of Galileo and argument, heat loss, but you know, that that's so simplistic. And why would why would natural selection sort of do something geometric? You know, what's the, what's the, why would you suppose that's its fundamental premises, natural selection and survival of the fittest? Why would some geometric thing like that evolve? So, and he realized that, and he showed on his graph, his original graph, a linear, two thirds and a three quarters. And that the three quarters was the one that was the best fit. So the other thing about that three quarters is that it's less than one sublinear. And that therefore, I know he did not emphasize this as I as I recall. But that means that the bigger you are, the less energy is needed to support a unit mass of a cell, a gram of bio tissue, or at a cellular level, the bigger the organism systematically, the less energy is needed per cell. In fact, scaling, you know, so put it in terms of the metabolic rate of a cell within the organism that is in vivo decreases as mass to the one quarter power. It seems to the three quarters means that the mass is increased, the metabolic rate is increasing as mass to the three quarters. So there was this systematic economy of scale, which meant that, you know, our cells are working less hard than your dogs, but harder than your horses, I mean, so to speak, in a systematic predictable way. So he did that. And then following that, as I said, during the following years, people measured all kinds of things. In fact, what they discovered was kind of remarkable that any physiological variable you could think of from heart rates to length of your aorta, you know, diffusion rates of things in your body, all kinds of things through life history events, you know, how long you live, which is what I become interested in, but growth rates, number of offspring, all the scale in a in this simple way that is as a power law and the exponent, the unlock to the three quarters was always a simple multiple of one quarter, always. So there was this kind of extraordinary universality in biology across all scales. And by the way, it wasn't just mammals, it was across all of life from, you know, insects, fish, birds, plants and trees, I mean, everything. And then eventually down at the cellular level. Was that measured as well at the cellular level? The cellular level, the cells, you know, it was like, this is kind of remarkable. And so this was all summarized, as I said, in a two or three books in the 1980, I would say. And so I came on the scene in the mid 90s, basically. And I had these books that, you know, was really nice, actually, because not only do they have all the data, but they were written, they were all pretty well written for in a non technical way, even though they were technical books, actually, they were written for biology community, but they were, you know, they didn't go into much technicalities. So going back to my beginnings, I started thinking about mortality and recognizing that you need to understand metabolism. And here I needed to understand that. Oh, by the way, the other thing was that one of the scaling laws that among all those many life history events was the scaling law for lifespan, which scaled approximately as mass to the one quarter. But with lots of variants, by the way, I mean, lots of variant because partly because data is hard to get, and you don't have control data, you know, there's zoo animals and animals in the wild who get killed, because of predator prey dynamics, and so on. So, you know, it's hard to have controlled experiments on that, so to speak. Nevertheless, you know, one quarter wasn't bad as a fit. And there's just a lot of lot of variants, as I say. So that was very interesting. That was really intriguing that all this somehow fit together. And so, but the first thing I wanted to do was to understand the scaling law for metabolic rate. And the thing that may be coming out of physics, the thing that intrigued me was that that's pretty weird that metabolic rate for mammals should scale the same as fish that should scale the same as trees. That can, you know, I mean, okay, they're all life, but they're very different. But there's one obvious thing about all of them. Two obvious things I would say that are connected. One is they're all struggling with the same problem. They're all made of cells. There's an enormous number of cells. We have, you know, 100 trillion cells in our body, about 10 to the 14 cells. And they all have to be fed in some democratic way, in an efficient way. And we know how that, what natural selection involved. It did something obvious. It created these networks that do it. But that's true of all of these systems. They all have these networks. They all have been struggling with the same issue. And so I thought, obviously, the one commonality across all of these and all scales is that it's something to do with these networks. So it must be that it's something to do with the mathematics and physics and the generic properties of these networks in terms of their structure and their dynamics. So I said, okay, let's just create a model network. Let's think of the, for example, our circuitry system. And what do we learn? Because one of the things that is in terms of metabolic rate. So metabolic rate is, as you mentioned, when you're talking about cities in your introduction, is roughly speaking for an organism, how much food do you need per day to stay alive? Basically, it's not quite that because you have to metabolize that food, but effectively it is. So it's the 2000, or whatever it is, calories per day on the average that a human being needs to stay alive. And the question is, how does that scale across different organisms? So the basic question is, how do you translate that into the sort of network language? Well, it turns out, biologists have already done that because they don't actually measure directly the food you eat each day. The way that you metabolize is use, respiration use oxygen to oxidize the food and turn it into metabolic energy, into ATP, which is your molecule that is your currency of energy. And so the way that it is measured is by the intake of oxygen in your breathing, just measure the respiratory rate. You could also measure, if you could do it, the blood rate through your aorta coming out of your heart would be the same thing because it's carrying the oxygen to the cells. Coming through the mouth is measuring the oxygen coming into the body. You could also measure, by the way, just a signed comment. And it was done in the early days, measures the heat, the heat that's coming off. That has to be a conservation of energy. Anyway, so that's great because if it's the measure of what's flowing through the network, you have a network theory, you have to calculate what is the flow through a network. And what happens if I change the size of the network? I mean, basically, well, it sounds simple, but it took a long time to put that into mathematics. And the other, but the fundamental thing that you have to think about is what are the universal generic properties of the network that transcend design? Because you are not a tree. I mean, we both look like sort of fractal like networks, but our circuitry system and a tree are quite similar, but they're very different in the engineering sense because you are a bunch of tubes joined together, your circuitry system, but a tree is a fiber bundle, like an electrical cable that sprays out. So obviously, whatever the principles of the network are, they have to transcend the design. So they can't depend specifically on the kind of network. And certainly if you went down to the cellular level or to insects, it's again a different kind of system, even though they're still networks. So the ones that we focused on in the end were, and I'll just say them, because they are relevant when we come to cities. That is the first is something we call space filling. And that just means what it almost is obvious, but the network has to feed every cell. Every cell has to be serviced. The end of the network has to end close enough to the cell to supply it with oxygen in the same way that a city has evolved so that every person or business, there's a roadway to get to it. Otherwise, it's not part of the city. I mean, it's almost by definition. In fact, it's a very good definition, I think, maybe what a city is and what an organism is. You have to be connected to that network. So that's the first thing, and you have to put that into mathematics, which I'm honestly not going to talk about here. The second is that the terminal unit of the network, the last branch, so the capillary in the circulatory system, for example, is invariant, meaning a whale, an elephant, and a human being, if you looked at our capillaries, they'd be essentially the same. And that's the idea is that in evolving new species of a mammalian species, natural selection doesn't sort of reinvent the fundamental units. You keep basically the same cells, our liver cells are pretty much the same. The cells, the capillaries, and so on, all are essentially the same. And the thing that makes us different is the network that is built up from those. In the same way, you could argue that all cities have certain fundamental units. I mean, all cities have people. It doesn't matter where you are, I mean, the city is not a city unless it has people. They're the fundamental units, and you build cities based on people, and we'll come to that. So there's that idea. And then the last one is the biggest assumption, but also by far the most powerful, and that is that these systems, because of the kind of continuous feedback mechanisms implicit in natural selection, kind of the survival of the fittest, have led to an approximate optimization of something. The ones that have survived and that now exist and have flourished are ones that have somehow optimized relative to the environment and their struggle of survival of the fittest. And so to put that into simple terms, in terms of the context of these scaling laws, and the networks, it's that the structure of our circulatory system has evolved in order to minimize the amount of energy we allocate to our heart for pumping blood through the system to supply oxygen and nutrients to the cell. And the idea is we've minimized it, we sort of optimized the system so that we can devote maximally energy to our Darwinian fitness, meaning to sex and reproduction and the rearing of offspring to pass on the gene. So that's that was the idea that you minimize energy and that's for a physicist, that's essential and powerful, because then the task is you've got to solve the equations for flow within each tube of the network, you have to create a network, and then with all its various parameters, and then you say I'm going to optimize it by minimizing the energy that's the pump is putting out to keep the system going. That's a long, tedious, complicated, challenging, but in principle straightforward calculation, but it took a year to do it. And when you come out, when you finish with that, what was fantastic? Oh, and then by the way, then having done that, you say now I'm going to change the size of the system. Now I'm going to ask how does it change? How do all these things change as I increase the size of the system? So when all that was done, what popped out was fantastic. It was just that you got a mathematical theory, predictive mathematical theory for the network, meaning not just the structure of the network, but the various lengths and radii of the tubes are, but also the flows in them. How much how much blood is flowing in the eighth branch of a hippopotamus is a circuitry system. That's what you that's what was solved. A very specific trivia element. So that's not so interesting. Well, it is. It could be interesting. But what was more interesting was that the scaling that the three quarters popped out of that calculation. And that was very nice and you could trace through why it was three quarters, where it came from. And that's to do the three, by the way, is to do with the space that you live in. We live in three dimensions. So that three, if we lived in two dimensions, that would have been a two. And before it was actually three plus one was the dimensionality of space we live in plus one. And in some sort of hand waving way, which the equation showed you how to do it, was because the optimal configuration was an approximate fractal. That is this kind of self similar behavior of our system. And the plus one was sort of a manifestation of the maximum fractality of the system. And that was giving rise to this optimization of the system. So I'm not really explained that the equations do it. But it's sort of a hand waving way of explaining why those numbers turned out the way they did. So in two dimensions, it would be two thirds from the three quarters. But anyway, so that was very nice. But what was much more powerful in a way was that we now had a complete theory. And now we can apply it to all these other, in those books that I mentioned, there were probably 50, 75 scaling boards of various things. And we could drive almost all of them from the spirit. Did you present any of that in a biology conference? And what was their reaction back in the day? Yeah, so it got it got it was interesting. Yes, it's been, it's a whole area actually now that became it's become known as the metabolic theory of ecology, funnily enough, which I don't like. That was because my major collaborator was a wonderful scientist named Jim Brown, who was a very well known ecologist, he happened to be president of the American Society of Ecologists, I forget what the, and whatever the professional name of it is in the United States. He was oriented towards ecology. And he gave this name, which I didn't like, but it's stuck. And so it's now a sort of area, a little area of biology. So it got, it got very enthusiastic response in many quarters. And it got a lot of criticism in others. I was waiting for it. Yeah. It got it got totally, but I wouldn't say bimodal, it was probably a continuum, but it felt bimodal that some, you know, I mean, I hate to even say this. Some were saying this deserves a Nobel Prize. Others saying this is a bunch of old rubbish. What is this got to, you know, et cetera, you know, this is and this is wrong and that's wrong and so on. And, and what about this? What about that? And it was amazing because it was an extraordinary opus. In fact, there was an article written by a biologist, a very well known biologist called Nicholas Cornell wrote, there were a couple of kind of essays in nature, saying that this is Newtonian in the way it's attacking. It was embarrassing. It was ridiculous, hyperbole. But it was wonderful to get, you know, that kind of publicity and that kind of acknowledgement. On the other hand, there was a lot of criticism, both technically, which were wrong, but there were criticisms. And, but also just philosophically, because biology has this long funny thing. I mean, there's a whole part of biology that sort of doesn't believe that physics really has much to do with it. And yet, at the same time, there's kind of what they call, but not me, they call physics envy. So it's a very curious, so this created a little bit. Well, it's sort of, I don't know if it's in the past, but it's long enough ago that, you know, so in fact, funny enough, I was asked, I don't know when, maybe in the late 90s, maybe early 2000s, to give, there's a famous lecture at the Natural History Museum in London on Darwin's birthday. It's called the Darwin birthday lecture. And I was extremely flattered and honoured to be asked to give this lecture, which was great. They then decided that they should have someone at the end give sort of like a rebuttal, which I found bizarre, you know. And so this guy, I can't name him. It was a well-known biologist, gave this absolutely weird reaction to it, which was, I mean, there was in fact, fortunately, there were lots of people, we went to dinner afterwards, I'm with him and others. And luckily, almost everybody was on my side. So that was actually very nice. But you see, but it's still there, I would say, in the community, that certainly exists. But things have moved on. So, but I did get, as I say, from some of the most distinguished biologists, tremendous positive feedback, including, by the way, feedback that said, you're going to get a lot of shit for this. Yeah, I can imagine. No, for my colleague, you know, no, it's very interesting. And it was repeated to jump ahead to a lesser extent when we did the city work. Yeah, I'm curious about that, because we'll come to that. I have a schizophrenic relationship. Yeah. One of the other things is what I realized is, and it's a shame because, you know, when the first paper that was written, which explained what this calculation I just told and explained, and did much more, was a paper for science. And the science editor sent it out. It's the only paper I've ever had, which had nine reviews. And that nine reviews went from, this is, you know, a fundamental breakthrough. It solved a famous problem that you get a Nobel prize to, that was one end. The other end was, under no circumstances should this be published in science. In fact, I don't think this paper, you know, deserves publication in any biological, I mean, things that were outrageous actually, including one, you know, actually was a later paper that said, you know, mathematics has no play, a referee saying no place in biology. You know, I mean, so it's pretty extraordinary. So, you know, it's, but it got nine reviews and the paper was demoted, the referee, the editor, who was terrific, by the way, said, when he agreed, he said, look, I'm going to ignore, you know, this paper should be published, and we're publishing it, but originally it was going to be one of those long papers in science, you know, and that was written that way. And he said, unfortunately, because of that, I think I'm going to have to pay some attention to the negative reviews. I'm going to ask you to cut the paper down to me. And we did, but the paper suffered. And it suffered for two reasons. One is I wrote it as a physicist, because I didn't, I didn't appreciate biological culture. And the second was it had to be cut back. And it would be much better to her in a certain sense or started from the beginning. You know, I mean, I've rewritten the paper from scratch rather than cut piece here, cut there. And it's very unsatisfactory. But the other thing I learned later, and this is certainly true when I got into more social sciences, is what constitutes an explanation is different in different sciences. And that shocked me that, and I still started struggle with that, that many biologists and social scientists think that statistical fitting, and then use extrapolation is an explanation. Rather than what a physicist thinks, and what I think science tells us, I'm going to be quite arrogant with this, but you need dynamics. And it's better to have, I think one of the things that physics taught us, better to have a model that isn't complete, and may even be wrong in some aspects, that explains something to begin thinking about a problem that makes predictions that can be tested against data. So you can iterate better to do that than just do mindless bits and be obsessed. That's the thing I learned about biologists and people in the social sciences, including urban people, obsessed with statistics. P values and R squared, and all the rest. Yes. But don't take them seriously for crying out loud. I mean, that's my issue. And I've stayed there. And I would say that I feel more and more, even with my young collaborators, I'm a minority of one. It's a standing joke. I've always belittling statistics, which is unfair. I mean, obviously, you need statistics. I mean, that's obvious. Statistics are important, and you need to have significance tests, and no obvious thing. But when you raise it to a level equal to a theory or a model, and not recognize that it's okay to have wrong models, sometimes it can't be, especially in these systems. Jesus Christ, I mean, it's amazing to get anything. I mean, these are some of the most complex phenomena in the universe. This is so much easier than understanding the origins of the universe. That's what's so extraordinary about this stuff. I mean, we understand more about the origins of the universe than we do about how a city works. Pretty amazing. You use the word complex there before we go into cities. I know that in your book, you say that we can't define complexity as we can define life. But when you see it, you know it is that it is, right? Can you point some complex stuff to us and say, okay, that is complex? Yes, no, I think that's right. I mean, you know, a lot of people, of course, struggle with what is complexity. And I don't know if I did it in the book, but I certainly do it in sort of the equivalent of summer school lectures, because I try to introduce it by talking about what is complexity to begin with. And I often do it by first introducing simplicity. What is simplicity? I mean, by that which is the opposite. And to put it, you know, in one line, physics is simplicity. Physics is the science of simplicity. What do I mean by that? I mean that you can understand, you can, you know, let's face it, you can, I mean, in certain sense, you can encapsulate all of motion at the classical level in two equations, so I can write down Newton's second law of motion and the law of gravity. And that's sort of, and, you know, of course, or you can do all electricity and magnetism, all of, you know, what's going on here between us by writing down Maxwell's equations. So, you know, there are an extraordinary collapse of a huge amount of data and experiments and things which amazingly, we can encapsulate in this symbolic fashion with these equations that capture their structure and dynamics. So, you know, so simplicity is that you can write, you know, a small number of equations that give you to a very, to a great deal of accuracy, the motion and structure of the system. And after all, the classic case of the solar system, you know, we can, we know with extraordinary accuracy the motion of the planets and indeed the motion of all the satellites so that you and I can have this conversation. You know, it's amazing, even including corrections due to general relativity. I mean, it's sort of amazing. That's simplicity. So it may be very complicated. So a Boeing 747 is extremely complicated, but it's maybe not complex in this language because, you know, there's sort of a book that Boeing aircraft company has that tells you how to build one, which means in principle, I could give it to you. And in principle, you could build a Boeing 747, see what I mean. Now, that's in complete contrast to understanding your brain or a city or an economy and so on. Because these consist of enormous numbers of components, constituents, actors, said earlier, bodies have 10 to the 12 hundred trillion cells. Cities have enormous numbers, often enormous numbers of people, but enormous numbers of components that make up the city. They have a dynamic that involves not just the infrastructure, but all the complications of the social interactions, all the love and the hate and the politics and so on. And most importantly, they're continuously adapting and evolving. And that over relatively short periods of time, especially in case of city, not so much in biology, but they have built into them this question of adaptation and evolution and some form of natural selection. And that's completely absent from the physical, the world of physics that's called that and the world of simplicity. And that leads to these multiple scales, not that you don't have multiple scales in physical systems, but these emergent multiple scales that happened from out of the dynamics. I mean, a city is a great example because you have scales from the individual household, the individual person, all the way up to groups and dynamics and then the entire city. And all of those coexist and they interface strongly with each other. And you can't think of a individual human being disconnected from a social network and therefore disconnected from the idea of city. I mean, it may be a very small town or village, but we are in communities. And so that's all intertwined. And it's very hard to separate scales in a complex system, extremely difficult, whereas in physical systems, it tends to be much easier. So there's all these characteristics so we can describe them. And when I give a lecture, I try to give various examples. But to give a precise definition, I think it's extremely difficult. And as you said, it's a bit like trying to define life. I think life in some ways may be easier to describe than complexity because complexity, life is just one aspect of complexity. Complexity has become something that involves all these remarkable systems that have evolved on the planet. I mean, the planet, I mean, it's so interesting to me, having spent most of my career doing elementary particle physics and a little bit of astrophysics and so on that, you know, the physical world is so is simple. And we can understand the cosmos right down the equation, understand the formation of galaxies and so on. But, you know, on the planet, it's messy. It's the messy world of the planet on here, something very special occurred, presumably elsewhere, maybe, but something very special. And there's this great big mess, which looks random, chaotic, arbitrary, capricious. And the thing that's so exciting is to find that underlying that there are apparently some regularities and commonalities like these scaling boards. And that's what I got excited about in terms of the biology and then trying to extend those ideas to cities to ask underneath what seems to be an arbitrary mess that seems to be contingent upon a city case, city case, geography, culture, history, you know, individual behavior and so on. Amazingly, there's regularities. And that's very exciting because maybe we can start making a science of these things, a science in the sense of physics, I'm not saying the rest is in science, but a more quantitative science. And that's where I'm coming from in attacking this. And this is specifically the reason I wanted you on this podcast to discuss exactly about this and on scaling laws in cities. I also have to dedicate this podcast episode to my dear friend and colleague, Joe, who passed away two years ago, and he was doing his PhD research on scaling laws in cities. Oh, my gosh. And he was focusing on looking at the environmental footprints of cities and how to look at the, how can we apply the scaling low lens and is the environmental footprints of cities reducing, if we increase the size of cities and things like that. So this is a particularly emotional. That's been a backdrop for a lot of this work, actually, much bigger is the thing that I'm bigger backdrop is just the whole question of sustainability, global sustainability, and the interface between understanding urban systems and ecosystems and therefore biology is ultimately a crucial component of that. And even though I thought about I haven't worked on it, which is amazing, even though I've been part of proposals to do this and so on. And it's one of those things that has stayed not on the back burner, sort of on the middle burner, ready to cook. And I've never really gone into it. And I feel sort of badly about that. And partly, and we may be we'll come to this later on, partly because I equally important is, I think what I got focused on was this question, which is even bigger than that was this question of, is it even conceivable that the socioeconomic system that we have evolved, which is most represented, in fact, by cities, I mean, cities are the epitome in a way of what that means. Are they, in fact, sustainable long term? I mean, is the whole system as a system actually sustainable? And that I became obsessed with that. And the idea that part of that system invokes, and again, maybe we'll talk about speeding up the time and what that does to us. And so I came more focused on that than on this coupling between the environment and cities, which is a part of that. But that's very sad, what you told me that he was, I didn't know that, that he was working on it. I will send you some of his paper and think he will be extremely happy. Already that we have this conversation, I think he would be smiling and would be listening. And we had this intense conversation as well with another friend, Yves, about this dichotomy or schizophrenia that I had that I have this engineering background where I love quantitative assessments of cities. And on the other hand, I have an urbanist background, which kind of tends to dismiss all of this. Exactly. And it's always such a difficult experience reading your work because I love the idea of having the simplicity of these equations, as you mentioned them. But at the same time, I'm saying to myself, how is it even possible to have this complex system, which is a city, which is a layer of economy, ecology, politics, culture and all of that. And we can simply have a simple solution to it. And so I'm schizophrenic about it. Can you help me? I certainly appreciate that. And because I've been now sort of part of at least peripheral to the sort of urban science community, and by the way, one of the things that I've enjoyed, well, let me say something else first, actually. So I really, the work, I love the biology, and I've loved the work on cities tremendously. It came as a surprise to me. And I'll talk about that in a minute. And the biggest surprise was that when I got into the city stuff, I thought it was going to be boring. Both cities were boring. I didn't know anything about it. And I was shocked once we got into it. I was amazed. And it really opened my eyes. And especially because I very quickly realized that the future of the planet is the cities. And I was shocked that I didn't realize that, and even more shocked that the world doesn't know it. And the academic world doesn't know it. I still, I mean, I still get so frustrated and angry that all the talks about sustainability and global warming and so on, still hasn't recognized the crucial role of the cities and therefore, that you need to understand that it's not just, you know, it's all very well saying, yes, it's the cities and it's people, but you need to understand it. And I think that's, that's my frustration. So, but as I've got into the community, and I've given lots of talks at city conferences, where I'm one of about three academics, I mean, these are, you know, practitioners, politicians, I don't know, more of the planners and architects and so on, who come from a non science background, typically, or economists, maybe, I've appreciated more and more this schizophrenia that you talk about. And I, and I, and in fact, one of the things I realized quite near the beginning, once I had this kind of epiphany about, you know, the role of cities was that I'm less concerned that my papers get published or published in high profile journals. That's good, of course, I'm an academic, that's nice, but that's much less important than trying to interface with practitioners. And because the question, the obvious question might be as a practitioner, so what? You know, I mean, well, it's a silly, I think that's a silly response, frankly, but I recognize it, and it comes from this schizophrenia. And so one of the things that helped me in that, and I did talk about this in the book, and you know it much better than I, is that, you know, if you look at cities that have been designed ab initio, they're almost always failings. They're soulless, they don't work, people are upset, they're not happy. And it takes, you know, Washington DC has taken 100 years for it to become a place that everybody hates, to a place that's now quite exciting, you know, very, very interesting place to visit lots of young people, lots of activity. But when I visited it 30, 40 years ago, it was still a dead, oh, it was a terrible place. And so they speak of Brasilia or an Islamabad camera, they all have the same. And that is testament in my view, my perspective. That's because these cities were designed without understanding how cities really, what is the science of cities, at least, you know, really coming to terms in a quantitative, principled way. Now, not that I'm saying I've done that, that's for sure, but we need to we need to start to develop that. And I was particularly struck, and I did talk about this in my book, when Norman Foster, the architect, was designing this city in the desert, Mazda, which you know, probably more about than I, and he drew a square. And I thought, I was going to use a four letter bad word, but I won't. But I thought, you must be kidding. I mean, a square city, it's almost, it already tells you that there's something going to be terribly wrong with this place to begin with. And indeed, when I was there, I realized there was something terribly wrong. And no, it'll evolve, you know, like everything. It's, it's an above the fact that you don't, you haven't recognized a prairie, any of these laws and any of these things. And the fact that you don't realize that it's evolving organic beast is, is, is a terrible, I mean, great cities are great, because they've been around a long time and they've worked organically. And they form, they have formed the emergent laws that we're going to talk about the scaling laws and so on. So, so in answer to your question, look, these scaling laws and the laws are related to them about the various networks and so on are a, are, of course, approximate their course, what we call course grain. They're not precise, but they are at work, and they work in a statistical sense. And if you're going to build a city or work on developing parts of the city, you better know those laws. Why work against them when those laws are going to be in effect because those laws have come about because of the organic nature of social interactions and people interacting with their infrastructure. And so put that in at the beginning, try to work with it when you're designing a city or mitigating a problem in a city, recognize that. And so at a minimum, be aware of them. And, and I think that's sort of in a nutshell, sort of the lesson of this. And so I've talked a lot with, you know, developers, construction people, you know, politicians and so on, these various things. I'm still very frustrated that it's hard to get, get it really together because the cultures are so different. And, you know, I mean, the worst possible case is probably China. Now, I suspect China is going to have, it already has tremendous problems with these new cities that it's building. And it has to build them has to build, you know, I'm very empathetic, they have to build, you know, a couple of 200, 300 new cities of a million people in the coming years, which is absolutely extraordinary. But they just build them, you know, what I used to say, you know, Soviet style, you just sort of build, I mean, you don't sort of think, just build these. And it's sort of like, you know, not recognizing the human beings live in them. I mean, let's put it in simple terms, that cities, I mean, that was the other thing I learned. As I got into this, was I didn't realize that much of the talk about cities and stuff written about cities, think of them as infrastructure, not recognizing that the infrastructure is just a background and a stage, and a kind of machine for creating social interaction. And, you know, and I realized, I had inadvertently come to the same conclusions that the wonderful, brilliant, Jane Jacobs had come to intuitively, in a highly non scientific, flaky way, but brilliant insight. And I, you know, I reread the book, I read that book, I think, not seriously, just because it was a book that was around, you know, I don't know, 40 years ago, I read Lewis Muffin and Jane Jacobs, when I was probably 20 years old, or whatever it was, 30 years old, I don't know, just as a cultural, you know, books that you read, I've completely forgotten, they probably stayed back here in my head somewhere. But then I reread Jane Jacobs. And of course, with a title like that, I'm sure you would have, it has the keywords that you were looking for, the life and death of extraordinary. No, I realized, I felt my God, and I have to say, I forgot, you know, I was using those words. And then I suddenly realized, my God, of course, that's Jane Jacobs. I'd better go back and reread, which I did. So that was great fun. So anyway, I'm sorry, I'm sort of, you know, labbing on here. But it is a big issue, the one that you've raised, which is not important in biology, which I talked about earlier, this sort of two cultures phenomenon. It's not important because it doesn't have, it only has academic consequences. This one, I fear, has serious consequences that we, that I think we do need to somehow integrate this sort of more sciencey, physicsy approach, or at least see how far it can be taken with traditional approaches. So I see this purely complementary. It is in no way replacing anything. It's simply complementary and providing another lens to look at what is a fundamental problem facing society. And, you know, I think we need to somehow cultural that in some way. And I hope we can. So before we get to how we can use scaling laws to plan and to think about our global sustainability challenges, perhaps we can spend just a moment so that you explain what your discoveries were with your colleagues such as Luis Bettenacourt and all of that. And I enjoyed, for instance, one of the findings that said that if you have a 10 million inhabitant city, it would have 15% less infrastructure than two 5 million inhabitant cities. So that's one very striking element. Could you perhaps present some some others? Yes, absolutely. So let me say about the scale because we've talked about them. But haven't actually said what they are. Other than the implications that there are laws, which are quantitative and in a certain in that sort of core screen statistical way predicted. So just historically, you know, I was working on this with this biology and it was the collaboration and several young people and so on. But it became very obvious that since it was a network based theory that it's obvious to try to extend it to social organizations in particular cities and companies. And cities were much easier because data was public. I was more interested in the beginning in companies. But that data is very hard to get. And it's like there's still a problem. But city data by and large is available. And we started a new collaboration. And that's when, as you mentioned, in particular, Louis Bettencourt joined. And that was fantastic. But the difference to begin with was that, unlike the biology, where I came in stone cold, there were books that had summarized all the data. Here, I came in, well, stone cold for cities, but not stone cold in terms of knowing what needed to be done. I had a vision. And you had tested it once and it had worked. So you knew what you were looking for. Yeah, exactly. I had a template. This is what we're going to do. And so the first thing was, do cities scale? And so after all, the biology had said, look, the whale lives in the ocean, the elephant has a trunk and the giraffe a long neck. We walk on two legs and the mouse scurries around. They look quite different in many ways. In most ways, they look different. Yet, if you measure anything, pretty much, they're all scaled versions of one another, nonlinear by these quarter powers. But we're actually scaled versions of one another. And the question is, is New York scaled up Los Angeles, which is scaled up Chicago, et cetera, et cetera, even though they're different geographies, histories, cultures, blah, blah, blah. Well, the only way to answer that is to get data. Now, and Luis joined and then was it Lobo and so on. And they did the work, not me. They put together, they got the data. And I guess it's not quite true. Before then, there was a preliminary to that because I worked with, I don't know if you know him, Dirk Helbing. Do you know Dirk Helbing at Etihad in Zurich? I read in the book, but I don't know. Yeah. So Dirk was a physicist, but he spent much of his career on urban transportation. And he had an institute. And then he moved to Etihad and he works on social questions, social problems, social science, mostly. But he and I had talked and he, the two of us and the student, I talked to him about this whole program. And we did some preliminary work in which we looked at, let's see, the ones I remember in that first, it was the very first paper on scaling in cities, which is really cited, unfortunately, but it had the number of petrol stations as a function of city size. And then I showed a picture in the book and the number of restaurants, the things like that, all things. And this work was done prior to Google really being hugely useful. So, if we had to go out and get yellow pages, can you believe it and count anyway? Investigative work, yeah. Yeah, it was, it was exactly. And we saw these wonderful scaling laws. And that was fantastic. And that gave impetus to putting together a supermoder, a broader collaboration. And then Luiz and Jose in particular got together this further data. And to cut a long story short, we discovered that just as with the gas stations, the petrol stations, the infrastructural quantities scale just like biology because they're networks, they're physical networks, electrical lines and roads and so on buildings even. And that they scaled in a similar way. They certified power laws. You plot them log log and they are straight lines. There's more variants than there was in biology. Not surprising cities haven't been around that long at organisms. Okay, but they showed very strong evidence of scaling. And they showed economies of scale. That is the slopes were sublinear. There were less than one. The only difference was the slopes were 0.85 instead of 0.75. That was the only basically the infrastructure. And we also learned, which was very satisfying, that wherever we could get data across the globe, it looked the same. So there was again this kind of universality in the infrastructure with this 0.85. So we also then looked at socioeconomic quantities, which are the very essence of the city, much more than that infrastructure. The number of patents produced, the amount of crime, the number of flu cases that disease and so on, all the things that involve social interaction. And we discovered something new. And that was that they scaled super linearly, meaning that the slopes, they still satisfy power laws, straight line on the log log part. But the slopes were bigger than one, which meant that in contrast to biology and in contrast to infrastructure, namely, the bigger you are, the less you need the capital, exactly what you said a moment ago, that if you take a city of 10 million people, it requires less infrastructure than systematically and predictably, let's be pointing this statistical way, then two cities of 5 million or four cities of 2.5 million, et cetera, et cetera, in a systematic way. And there's this extraordinary saving with all infrastructure, the bigger you are. So in that sense, the bigger the city, the better in that purely material sense. But then we also discovered the opposite for the socioeconomic quantities, the super linear scaling meant that instead of the bigger you are, the less per capita, the bigger you are, the more per capita. So the bigger you are, the more social interaction per capita, the more patents produced, therefore, the more innovative a city is, the more crime per capita, the higher the wages per capita, et cetera, et cetera. And that was very satisfying because it was not only, it was extraordinary, it's universality, because at least in this coarse-grained way, the scaling laws were true across different metrics, quite different metrics, everything from GDP to patents to AIDS cases, but also across the globe, primarily across the globe in most of the, in all the countries that we had looked at at that time. And that was very compelling. That was extremely compelling. And to follow up on that, it took a couple of years, we started to think about theories. It was very clear that this theory, whatever the theory, the extension of the theory in biology to networks had to invoke the dynamics of social networks. So we had to understand social networks and the structure of social networks. And most importantly, and the biggest challenge is how do those social networks interact with the infrastructure? You can't separate the two. And it is no accident that the economies of scale in the sublinear scaling, 0.85 is 0.15 less than one. And that, and the super linear scaling is the same 0.15 bigger than one. And that is very simplistic, is because these two networks are interconnected and intertwined, there's an integration and a tension between them. So that was quite fascinating. And we confirmed it, or at least we had strong support from it, when we teamed up with friends and colleagues at MIT, Carlo Ratti's group, where he had access at that time to mobile phone data. And billions and billions of these telephone calls. And I was actually, I have to admit, I was quite, I was not very enthusiastic. I just thought that was not going to be a good proxy for social interaction. Well, this was early enough where, you know, suddenly in the United States, maybe 50% of the people at that time, now everybody hasn't had mobile phones. But the data we had were from somewhere African countries, where there was, where there was much greater use. Anyway, cut a long story short, what was great is we had that data. We analyzed it. A young man named Marcus Schlapper, again, he's actually at ETH. He's like you, he comes out of engineering actually. He did most of the analysis to analyze the degree of social interaction as a function of city size. And of course, this theoretical framework would predict that it would, the number of interactions would scale with a, as a function of city size in the same way as all these socioeconomic metrics, meaning a superlinious exponent of 1.15. And it was beautiful. It turned out that the data agreed with that extremely well, and all kinds of other things that came out of that data that was very nice. And I became a convert and quite passionate about the use of mobile phone data, as I realized this was a marvelous proxy for social understanding, social interaction, and therefore to understanding the dynamics of a city. So that was great. So the theory, I would say, is not on the same basis by a long shot, really, as the biology. But I think enough has been done to convince certainly me and many others that there is a theoretical understanding of the scaling laws in cities and their connection to infrastructure. And one of the things that convinced me early on was the extension, which we haven't talked about, the growth of cities. We didn't talk about the growth of organisms, but one of the triumphs of the biological work was the understanding of growth. And that is that the idea was extremely simple. And it goes to the heart of something you're interested in. And that is that you ask, you say, okay, you have this metabolic rate, you have this metabolism. What is it used for? Well, it's used to keep the system alive, to sustain it. So therefore, it repairs damage that's occurred. And, you know, something has fallen on the part, you replace it, a cell that's died, you replace it, and then you add new stuff. And you can write down the equations for that. And when you do it, you get extremely good agreement with growth curves for organisms and certain universality emerges for growth. And it was very powerful. And one of its nice things is that it explains why it is that you grow quickly and then you stop. And the stopping of the growth, the cessation of growth, is related to the fact that the driving, the driving resource, the supply side is scaling sub-linearly. It's only scaling as much as the three quarters. But the demand side is growing cells at a linear rate. And linear beats the supply, the demand beats the supply, so it stops. You can't keep up with yourself, so to speak, and you stop growing. And that's what this shows. That was great for biology, but it's terrible for socioeconomic systems. They don't really die, do they? They're supposed to be open-ended. I mean, that's our paradigm. But what was lovely and what convinced me that we had it all right, we felt, at least I felt, was that when you took the superlinear scaling and you did the same thing and you formed something that you talked about earlier, at the beginning, a social metabolic rate, so to speak, and you said the same thing. That social metabolic rate gets, roughly speaking, allocated between maintenance, the repair of buildings and roads, and the repair of people with hospitals and so on, on the one hand, and on the other, the growth of new stuff, the growing new buildings and developing new roads and growing new people and so on. If you write that down, the superlinear behavior of what now becomes what we call social metabolic rate gives rise to open-ended growth. And that's what we see, and it agrees very well with much of what we see. And that was great. So it was a very nice package because you would say, look, the underlying mechanism is social interaction, social networks. But what is the nature of social networks? The nature of social networks is that we talk to, we talk, A talks to B, B talks to C, C talks back to A, and we build on each other. We build up on each other and we create ideas. We innovate, and it is that process fundamentally that creates ideas. That's where ideas come from, that's where the theory of relativity came from, that's where Google came from, that's where Toyota came from, that process. And the city is the machine that facilitates and encourages that. That's what's so wonderful about the city. And so that was sort of the idea that that does it. And that positive feedback gives rise to superlinear behavior because the more you have of that, the more you get. And so it builds on it on itself. And that then leaves to open-ended growth. You just keep building more. And that was great. And so I was very excited by that. But then I realized the equations had a fatal, well, not a fatal, potentially difficult difficulty in its consequences. It wasn't wrong, but it had built into it mathematically something that is called a finite time singularity. And that means in English, the following, that in this thing is open-ended and is growing, in some finite time, it will go to infinity. That is, you'll get an infinite GDP or you'll get an infinite number of patents produced. Don't say that to economists. They will love your theory. Yeah. So that's, that is obviously crazy. That can't be. And indeed, the theory tells you that it can't be because it says if you continue on, the system collapses, it sort of stagnates and collapses. And so you say, okay, and it happens in a finite time. So it might be five years, 10 years, 100 years, but it ain't going to last. And so the question is, how do you, how do you prevent that? And then it gets, we've got into some speculations, which it now goes beyond cities. But the idea was simply that, look, you know, that growth curve assumes that sort of the background metric, the background culture is the same all the way through that growth period. So the idea is the idea evolved that we have paradigm. So we have the idea of bronze age, you know, that somehow that period was dominated by bronze or iron age. And now, you know, recent times, the industrial revolution, coal and steel are now more recently computed. And then that evolved into the internet. We have the internet age, so to speak. So the idea evolved was quite simple in a way that the way you avoid it is you're growing within that paradigm. And before you reach the effects of the singularity, you better make a major innovation or a paradigm shift. You better adapt to something new and reinvent yourself and start over again. So the idea was that you would do that, and you sort of reset the clock, start over again, you did another singularity and sort of, so the idea was you sort of go through these continuous cycles, which is what we see in socioeconomic systems. But this is supposed to be a quantitative theory. And this theory says not just that, but it tells you something that is quite disturbing that you have to do it two things. First of all, that everything speeds up. The bigger you get, not only do you get more of the capital, but things get faster. And that in particular, you have to make these innovations faster and faster to be sustainable. And when you look at the data, you can predict the speed at which you need to innovate and the increasing pace of life. And amazingly, the data agrees. Now, this is a speculative part of the theory, but it was very compelling. And that part is interested me tremendously. And I mentioned that earlier, that increasing pace of life and the need to innovate faster, and that you put it in simple terms, that something, some new innovation might have taken 100 years really to develop a thousand years ago, now takes 15 years. And soon it's going to take 10 years. So is it conceivable that sort of every five years or four years, you're going to have the equivalent to an IT revolution? Well, that's so that seems unlikely. And we're not. And the other question is, can we adapt? You know, we already have that. Can we, you know, this brain of mine, which is very old, I'm now over 80, I've had to adapt to this laptop, to the computer, I have to write in latex, I have to adapt to all those web-face things and so on. And it's hard. And, you know, it's a huge challenge. And this brain that has to do that is the same brain that I would have had 10,000 years ago, or 50,000 years ago. And are we coming to a limit to that? So how fast we can, it's not that we can't adapt, but you've got to do it faster and faster. That's the point. So that's where I'm at in terms of my, you know, where the city work has surprisingly taken me, that I become less, I hate to admit this in this conversation, less focused on the city itself, you know, which was what I was interested in. I'm still interested in my still work on it, than I am on the consequences, these long-term consequences of the whole process and the whole dynamic. So let's perhaps finish with that then. How do we... Sorry, I do want to say one of the things that interested me when you asked me to be on the podcast was that when you wrote the message, I was very intrigued. I think whether you mentioned social metabolic rate or not, I don't remember, but it was implied, was the kind of circular economy and so on, which is of course related to this, obviously. So I was quite excited and interested about that. Yeah, so I'm wondering about some of the applications that we can do based on this knowledge into, you know, the urbanoscene, as you call it, or, you know, the pressing issues that we're having. So I heard that you want more and more to work hand in hand with practitioners. So how do we deal with, you know, we know what is the consequences of our today's cities, meaning that if you have more of this today's city, this is what you can expect, right, sub-linearly or super-linearly depending if it's a socioeconomic one or infrastructure. So that's, we know what to expect, but as you said, we might build different types of cities that might not fit the model or we could repair cities. So what is, let's say, your takeaway message to practitioners based on the scaling laws when we face these global challenges? A very easy question. Yes, this is the big challenge and I struggle with it and I have to admit, and I may repeat myself in this, but first, one of the questions that's implicit in your question is what is the role of the internet? What we're doing now? I mean, the pandemic accelerated what was already happening so that we interact, you know, we have Zoom talks and so forth and we're sitting here talking kind of two-dimensionally. It's very frustrating, you know, it works, but it ain't the same. I'd so much prefer to be with you in your office, to spend a few days a week so that we talk for half an hour here. You know, that informality is crucial, you know, for the has been in any way in the past for developing ideas, for being creative and just for living, you know, and we're denied that to some extent by this, but it saved us and it's great. So we're going to get more of it, whether we like it or not, forget about pandemics. It's clearly becoming more and more an integral part of life and a cousin of that is of course the much touted, somewhat hyperbolic talk about, you know, smart cities and so on, all of which is good. No, I mean, it's good, you know, obviously, but to do that, but it's so, I have mixed feelings about that. I mean, obviously, I welcome it and I welcome big data and I welcome AI, but, you know, I definitely come from a conservative physics background, but understanding plays a fundamental role in progress ultimately. And that's, you know, my passion. And so that goes to the other part of your question, maybe the main part, practitioners and so on. And that's what I said before. I think, you know, much of what has been done in cities, and I say this, A, from observation, and B, because people who build cities, well, build small towns, I've taught, I've been approached by people build small towns rather or developments. And they say to me, you know, you know, basically, we just work by rules of thumb. You know, I know lots of architects, by the way, who think about these things. And they say, you know, we just work by rules of thumb. This is what was done, you know, we know we should put a park here, we should do this, you know, that's, you know, we have sort of, you know, there should be this much green and this much that these sort of, well, yes, there's, they probably have some basis. But it isn't science. I mean, that's not science. And, you know, I wrote in my book about, you know, ships that were designed by rules of thumb and couldn't work. Or, you know, there's famous cases of cathedrals that collapsed. Well, you know, we now have some science. And I think we need to do it. And we also have the beginnings of an understanding, the beginnings of a theory of a conceptual framework. And, you know, we're just, as I say, scratching the surface. But we need to integrate some of this with urban planning, development, urban geography. I mean, urban geography has had some, you know, obviously quantitative science in it, clearly in urban economics, likewise. But one of the things that I'm sure you're familiar with, one of the things I was shocked at when I got into this were these different fields with urban in front of them. And they never talked to one another. You know, I mean, you talk to urban economists, and they don't, they for sure were like all economists. They weren't, they're worse than high energy physicists. They looked, you know, they think they know everything about talk to anybody. I'm joking, of course, but you know what I mean. I know what you mean. But the point is that there are these various fields, often, you know, within a university, you'll have departments that have urban geography, urban economics, and so forth. And they don't talk to one another. And, you know, this is terrible of itself. But add to that this perspective, I don't know if you want to quote, you know, people talk about the science of cities. Okay, but whatever it is, but bringing in some of this stuff of physics of cities, maybe, I don't know, whatever. But we need to integrate all these, first of all, within academia. I think there needs to be a much more inclusive kind of kind of framework being developed, and more open and transparent. And part of that is to bring in more of the practitioners and spending much more time with practitioners, and really trying to get practitioners to work with the kinds of regularities of laws that we understand. I mean, one thing I didn't talk about, I don't know if you're familiar, we wrote a paper last year in Colorado on mobility in cities. And we discovered this, we had discovered and explained this extraordinary law for mobility. If you ask, how many people do you take a place in a city? And you ask how many people are coming to that place from a distance are away? And how often are they coming? It goes, there's a simple law that you can actually derive that says it should go one over the distance squared times one over the frequency of their visit squared one over r times frequent rf squared. And it's almost exact, this law is ridiculous. I mean, it's even better than the scaling laws. And it's true. And we looked at it in, where did we look? Singapore, Boston? What's the capital of anywhere in African country? And so there were four places in different continents, we did, and the degrees, it's extraordinary. Well, you should know that if you're dealing with transportation cities. So one of the places that got turned on to our work, I think in terms of practitioners, was Singapore. And I've spent quite a bit of time in Singapore. And Singapore is unique, of course. So it's a funny place to be doing this, because it's an urban system of one. Therefore, scaling has nothing to do with it, in that sense. On the other hand, the dynamics does, of course, the understanding where the scaling comes from does, and they got really interested because Singapore is maybe unique. It is obviously unique, but its uniqueness has led it to realize that even though it's sort of number one in many socioeconomic metrics, it's very vulnerable. Things could change. So it needs to think carefully and seriously about its future. And it respects science. So I've spent quite a bit of time talking to people there and trying to interact. And that's been partially fruitful. And in fact, amazingly, in one of their five-year and 25-year plans, they used the scaling laws, actually. It's been a problem there, too. It's the same issue. The schizophrenia problem is certainly there. And it's very understandable. You have immediate problems you want to deal with when you're a practitioner. You can't wait to understand and all the rest. You've got to get things built or whatever. So it's an issue. But I would simply say that we have to make bigger effort to integrate the whole field, to integrate some of this stuff into it, to see the relationship among and crucially and importantly, to somehow get practitioners, politicians involved. And also, to make it even bigger, to recognize this central role seriously of cities in the future of the planet. And of course, that's where I'm at. So even though that's maybe not where I spend, I do not spend the majority of my time by far working on it. I spend a huge amount of time thinking about it. I don't do it because I think it's so important. So I'm not sure that's very useful. It's encouraging, at least. So rapid fire question. What will you work on on 2022 or will you or any projects, personal projects or Well, I'm, I'm several things quickly. One is we extended this to universities, which was interesting, by the way. Secondly, one crucial thing which I we did not talk about is deconstructing the city. You know, we consider the city homogeneously, basically. And that's also obviously wrong. Of course, in core scale, that doesn't matter. But we want to go down to, you know, more fine grain level. So questions of neighborhoods, and so on. And we've done a little bit of work on that. That's hard. I would like to have, I mean, that's another thing I was frustrated by. When I look at the literature, there's no first of all, there's obviously the ongoing question of what is the city. But I think even more challenging is what is a neighborhood. It's all very well saying neighborhood, downtown, and so on. And people, but there has to be an operational quantitative definition somehow. And I've tried that. Maybe it's impossible. So there's that. And we've worked on that some we've worked on in terms of deconstruction inequality in cities. The whole question of, you know, the different layers of people and the scaling laws applied to them. And that was fascinating, I must say. So there's all that, that's ongoing. On the bigger picture, developing a big picture of the Anthropocene. That's ongoing. And in fact, I have a meeting following this in about two hours of our group, a separate different group, that is trying to develop a similar kind of theory about the globe as a whole. Can we do it? That evokes it anyway, it's, it's very challenging, extremely ambitious. It may not come to anything. We've made progress. We have one small paper that we've written. Let's see. So that's ongoing. The other thing that I didn't talk about that's associated with this. And we have a paper under review in nature, actually, on companies on doing similar things for companies and the growth of companies and so on. And that's also very interesting. And it suffers, it's already suffering before we begin from the schizophrenia problem, for sure, or the different cultures problem. You know, I mean, it's so interesting. Economists think, even though they use mathematics quite differently than physicists, that we're much more physics oriented. Although an economist, one of the collaborators is an anthropologist, actually, who comes, knows a lot about economics. So these are all these ongoing things and they're all sort of interrelated. And I'm also toying, even though I said I never would do it again, writing another book, as a follow up somehow. I don't know if I will. I'm not a very slow, laborious writer, so I'm not sure I'm ready to really launch into it. And I don't have many, going back to the beginning of our conversation, don't have very many years, if any, left. Well, I'd be happy to read it if you write one. Do you have any other books or articles, or perhaps even a movie that you'd like to recommend for listeners or watchers? Oh, what an interesting question, my goodness. No, not really. I mean, you mean that would be related to this? Or not necessarily related, something that spiked your interest lately. And you said, oh, that was new to me. No, I must say, one of the things that has happened to me in the last year, and I don't think it's something to do with the pandemic, I'm not reading very much, which is very disturbing to me. And I was a bit thrown by it, feeling almost guilty. And then, this is interesting, people can look it up. I came across totally accidentally a quote from Einstein, who said, you should read less, you're reading too, and your old age, you tend to read too much, one should read less, because I forget how he says it, but the implication was, it sort of pollutes the mind. And that's sort of interesting. And it can, of course, one of the troubles is when you know too much about a subject, it stops you from being creative about it, other than doing something very focused. But if you want to think bigger about it, if you know too much, you can think of all the reasons why it's wrong, or it won't work. And that's sort of interesting of itself. So, but what I was going to say is I'm not doing much reading, which is disturbing, but this Einstein thing left me off the hook. But no, I've read some books, I've been reading books about philosophy in America, actually. There's a writer named Louis Menon, who writes mostly from New York, he's a professor at Harvard, who wrote a book about American philosophy every time, would highly recommend actually, pragmatism, it's pragmatic philosophy. But I've been watching, I watch, I'm a great fan of two things. I'm a huge fan of old, for old black and white movies, post-war American film noir, Humphrey Bogart type, I suppose is the image. I love them, even when they're terrible. I just really enjoy them as a relaxation. They're great. I love them. And, you know, the classic ones of Baltes Falcon and Casablanca, they're the genre, but there's hundreds of these dead things. Most of them are not very good, but they're great. So that's one passion. The other passion I have, that I spend some time on, but is I'm a great, I'm very passionate about football. In my fantasy world, had I had choice in my life, I would have wanted to be a famous football player, more than a famous physicist or scientist, I would, that would have been my choice in life. I did play football, and I was, I was competent, and that's about it. I was okay, but not good enough, that's for sure. So I watch quite a lot of football, and too much, mostly the Premier League, because I'm English originally, and I watch primarily the Premier League. And I have the burden in life, one of the great burdens I carry in life is I'm a passionate follower, mostly because my family was of Tottenham Hotspur, who are always, who are always failing, always coming close and failing. And I, and I, and I remain passionate, partly because there's some lesson in life there. Oh, by the way, there is another project I'm working on. Ah, yes, that's related to that. And that is, I predicted the Premier League table, not for last, the season before last, I just took the data, by simply adding up the cost of the players and their wages and ordering them, and it matched extraordinarily well, the Premier League table, I guess it was 2020, 2020, 2020, I only did one year. And that year, I was better than the 10 leading pundits of the BBC. And my point is I'd like to write a paper on this to show that the hype about the manager, or how brilliant they are, and so on, is, to put it mildly, hugely overrated, because probably my mother could manage Real Madrid, or Manchester City, and win. And that's my final word. Well, thanks so much, Jeffrey. It was really, really nice talking to you. I hope we're going to meet sometime in the future, and discuss about all of this. No, I would. So you're at, you're in Lausanne, right? Is that right? Correct. Yes, and Celine Rosenblatt is there. Correct as well, yeah. Yes, whom I know, you know, I've known over the years, actually. I don't know how well, but we interacted since she was a student, actually. And funnily enough, I was, she wanted, she was trying to put something together in Lausanne, I think before the pandemic, and I was coming. So hopefully this will happen someday. I will come, or you'll come here, or we'll meet at some meeting. I will enjoy that. Great, fantastic. Thanks, thanks a lot, Jeffrey, and thanks everyone to listening until the end. If you liked this episode, please share it with fellow practitioners, with fellow scientists, and just tell us what you've learned, and I'll see you in two weeks. Thanks, everyone.