As our world knits together, economic interdependencies change in both shape and nature. Supply chains, finance, labor, technological innovation, and geography interact in puzzling nonlinear ways. Can we step back far enough and see clearly enough to make sense of these interactions? Can we map the landscape of capability across scales? And what insights emerge by layering networks of people, firms, states, markets, regions? We’re all riding a bucking horse; what questions can we ask to make sure that we can stay in the saddle?
Welcome to COMPLEXITY, the official podcast of the Santa Fe Institute. I’m your host, Michael Garfield, and every other week we’ll bring you with us for far-ranging conversations with our worldwide network of rigorous researchers developing new frameworks to explain the deepest mysteries of the universe.
This week on Complexity, we speak with two SFI External Professors helping to rethink political economy: newly-appointed Science Board Co-Chair Ricardo Hausmann (Website, Wikipedia, Twitter) is the Director of the Harvard Growth Lab and J. Doyne Farmer (Website, Wikipedia) is Director of the Complexity Economics program at the Institute for New Economic Thinking at the Oxford Martin School. In this episode we zoom wide to try and find a way to garden all together, learning limits that can help inform discussion and decisions on the shape of things to come…
If you value our research and communication efforts, please subscribe, rate and review us at Apple Podcasts, and consider making a donation — or finding other ways to engage with us — at santafe.edu/engage. You can find the complete show notes for every episode, with transcripts and links to cited works, at complexity.simplecast.com. Heads up that our online education platform Complexity Explorer’s Origins of Life Course is still open for enrollment until June 1st! We hope to see you in there…
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Mentions and additional resources:
The new paradigm of economic complexity
Pierre-Alexandre Balland, Tom Broekel, Dario Diodato, Elisa Giuliani, Ricardo Hausmann, Neave O’Clery, and David Rigby
in Research Policy
How production networks amplify economic growth
James McNerney, Charles Savoie, Francesco Caravelli, Vasco M. Carvalho, and J. Doyne Farmer
Productive Ecosystems and the arrow of development
by Neave O’Clery, Muhammed Ali Yıldırım, and Ricardo Hausmann
Horrible trade-offs in a pandemic: Poverty, fiscal space, policy, and welfare
Ricardo Hausmann and Ulrich Schetter
Historical effects of shocks on inequality: the great leveler revisited
Bas van Bavel and Marten Scheffer
in Nature Humanities & Social Sciences Communications
Complexity 56 - J. Doyne Farmer on The Complexity Economics Revolution
The Multiple Paths to Multiple Life
Christopher P. Kempes and David C. Krakauer
in Journal of Molecular Evolution
Scaling of urban income inequality in the USA
Elisa Heinrich Mora, Cate Heine, Jacob J. Jackson, Geoffrey B. West, Vicky Chuqiao Yang and Christopher P. Kempes
in Journal of The Royal Society Interface
Complexity 12 - Matthew Jackson on Social & Economic Networks
Complexity 81 - C. Brandon Ogbunu on Epistasis & The Primacy of Context in Complex Systems
by Nick Hanauer (podcast)
Complexity 15 - R. Maria del-Rio Chanona on Modeling Labor Markets & Tech Unemployment
Will a Large Complex System be Stable?
by Robert May
by Stuart Kauffman
The Collapse of Networks
by Raissa D’Souza (SFI Symposium Talk)
Transcript provided by human-machine collaboration (podscribe.ai + Aaron Leventman ed.)
J. Doyne Farmer (0s): Democracy, I think we're seeing these days, is not functioning that well in many parts of the world. I think in the U.S. we suffered from having built in flaws to the system, but certainly it would be nice if we could find ways so that rather than having every election for president, we put out one bit of information, yes or no for the candidate that us Democrat or Republican, and maybe a few bits for the Senator and Congressman we vote for. Really the public is getting remarkably little informational input into the decision-making process.
And some people have been thinking about how to change that, to make that better, but it's still very experimental and I think also there's strong incumbents have enormous resistance to change.
Ricardo Hausmann (44s): In my mind, the real solution to inequality is not so much redistribution as inclusion as incorporating people into the possibility of mixing what they are, their letter, into longer words, into the kind of production networks that don't was talking about. And that leads to a very different agenda for inequality reduction. Do you send people a check or do you connect them to the urban transport network? Do you connect them to the financial approval? Do you connect it to the labor market? Do you connect to the schooling system?
So do you connect it with a bunch of all the letters where they would be able to participate in more complex production?
Michael Garfield (1m 48s): As our world knits together, economic interdependencies change in both shape and nature. Supply chains, finance, labor, technological innovation, and geography interact in puzzling nonlinear ways. Can we step back far enough and see clearly enough to make sense of these interactions? Can we map the landscape of capability across scales and what insights emerged by layering networks of people, firms, states, markets, and regions?
We're all riding a bucking horse. What questions can we ask to make sure that we can stay in the saddle? Welcome to
the official podcast at the Santa Fe Institute. I'm your host, Michael Garfield, and every other week, we'll bring you with us for far ranging conversations with our worldwide network of rigorous researchers, developing new frameworks, to explain the deepest mysteries of the universe. This week on
, we speak with two SFI, External Professors helping to rethink political economy. Newly appointed science board Co-chair Ricardo Hausmann is the Director of the Harvard Growth Lab. And J. Doyne Farmer is Director of the Complexity Economics Program at the Institute for New Economic Thinking at the Oxford Martin School. In this episode, we zoom wide to try and find a way to garden altogether learning limits that can help inform discussion and decisions on the shape of things to come. If you value our research and communication efforts, please subscribe, rate, and reviewus at applepodcasts and consider making a donation or finding other ways to engage with us at
You can find the complete show notes for every episode with transcripts and links to cited works at
.Heads up that our online education platform,
Complexity Explorers Origins of Life
course is still open for enrollment until June 1st. We hope to see you in there. Thank you for listening. All right. J. Doyne Farmer, Ricardo Hausmann. It's a pleasure to have you both on
. I am perhaps more daunted by the guests on this episode than I have ever been.
And that's I think a promising thing.
J. Doyne Farmer (4m 6s): Well, it's a pleasure to be here and it's particularly pleasurable to do so with Ricardo.
Ricardo Hausmann (4m 10s): Thanks for the invitation to connect with Doyne.
Michael Garfield (4m 14s): So I would like, and I know that we've already done this in episode 56 a little bit when we had you on the show last, but Ricardo, you're new here. So perhaps it makes sense to start with you and just provide a little bit of background. And then we can kind of shuttle this back and forth about your history as a researcher and particularly in the field of complexity economics, how you ended up involved with SFI and basically the road that got us into this conversation and the history of your relationship with each other as researchers.
Ricardo Hausmann (4m 49s): Sure. I got my undergraduate degree in an engineering school in a degree called Applied Physics. It was called Applied in Engineering Physics at Cornell. I couldn't get a scholarship to do physics. So I got the next best thing sort of. And in that process, my electives, I eventually took economics and I was really interested because I was planning to go back to my home country, Venezuela. And I thought that it didn't make that much sense to go back to study electrons in Minnesota, because they're the same as electrons everywhere, but the economy was different.
So it made more sense to do an academic career on the field. Or if I was in Minnesota, I would be able to contribute something. In the end I've been the last 28 years of my life in the U.S. so it didn't pan out exactly that way, but I always was thinking that the growth process really is a process sort of like of the development of kind of like a social brain. Then in order for society to know more individuals have to know different that you have to store these different bits of knowledge in heads.
And then you have to bring these heads back together again. You're going to run the company. You need people who know about procurement, about recruitment, human resource management, taxes, contracts, binding marketing, finance, accounting. No one knows all of these things. So you need teams to do these things for, I dunno, for perfume is very different than doing it for cell phones and stuff. So we start to think of society as sort of like an information processing machine that uses information, uses knowledge to change the world so that the world. You have a metal, a beautiful metal, but you don't want a beautiful metal.
You want an omelet. How do you go from a beautiful metal to an omelet, sort of you act on the world with a set of procedures that involve knowledge? And some of that knowledge can be embedded in tools. Others of that knowledge can be conceptualized and codified and shared in recipes. How to do manuals and so on. A lot of that knowledge is tacit and exists in brains and only in brains gets with enormous difficulty into brains. So I developed a paper back in 2007, 2008, 2009 that got the Santa Fe Institute to invite me.
And then I sort of felt at home. I sort of felt at home with people who thought about the world and more or less similar ways as I was thinking. And so I've always, since I don't remember if it was 2007 or 2008, but more or less since then, I've been connected to the center phase.
Michael Garfield (7m 28s): And since we can't necessarily demand that people go back and listen to prior episodes for your story, Doyne, I'd love to give you the opportunity to give us the same sort of entry.
J. Doyne Farmer (7m 39s): Well, I've always been fascinated by technological change, which is ultimately the thing that so biggest factor influencing how society changes. And I completely agree with everything Ricardo said. I've been coming at it from a bit of a different angle, both thinking about the evolutionary forces that exist at the frontier of technological change. Thinking about collecting data about how does technological change actually happen. One of the things that we found when we did this is that it's extremely heterogeneous.
Most things improve very slowly, if at all, over long spans of time, centuries, price of oil now is the same as it was 140 years ago, more or less, but some things like transistors dropping price at 40% per year, improving quality all the time. And so there's just a big spectrum of things. And how can we use data to predict what's happening? How can we use what we know to guide ourselves through things like climate change and changing the economy and the green energy transition?
So, yeah, that's just one of many things I'm interested in and I'm sure more will flow out during the course of the conversation.
Michael Garfield (8m 49s): I hope that we have an opportunity to touch on pretty much all of that at some point. I think where I'd like to start is I want to focus on kind of recent works here because both of you have a deep history of publications, but there's a lot of interesting stuff that's come out, even just this year. And Ricardo, you coauthored this piece with Pierre-Alexandre Ballandand others on the new paradigm of economic complexity, which my understanding is this was an introduction to a special issue of research policy, kind of introducing a topic that this is the meat of the last conversation I had with you doing about how do we think through these issues?
How do we measure them? How do we begin to have a conversation, a formal conversation about, as you said, a moment ago, the way that knowledge is stored and acted upon in society, and it's across this like a diverse pallet of heterogeneous platforms, they're all interacting with one another. I remember when I was an undergraduate student seeking a graduate program in complex systems in 2005, I was roundly discouraged by my advisors because they said first, tell me what complexity even is.
I think that conversation seems to have changed a lot in the last 17 years, that through the work of the two of you and many others, that we are getting much better at answering that question. And so I'd love for you to just give a little exposition on this particular piece and how you and the other authors are laying this out.
Ricardo Hausmann (10m 27s): So I have been for much of my research time and effort over the last 20 years, I've been asking myself where countries rich and where countries poor. And that's different from the question that is asking the question, how does technology evolve at the cutting edge? And I've been asking the question, why don't you do things that the world knows how to do? So the world knows how to make vaccines. Why don't, you know how to make vaccines, how to make cars, why don't you make cars or antibiotics or whatever.
How does technology diffuse and what are the obstacles for technological diffusion? Because it's sort of like there are many countries, rich countries in the world that live with incomes of $60,000. And there's a bunch of countries that live with incomes of less than $2,000. So there's a factor of 30 difference and why the hell? I mean, just do the things that others have figured out how to do. And so in answering that question, we sort of realized that in order to do something, you have to know how to do it.
So if I look at what you do, I can figure out how to do, and then sort of like figure out what are the cognitive relationships if you want between different things. So, if you know how to make a right-hand side shoes, you probably can figure out how to make left-hand side shoes. If you know how to make televisions, you probably know how to make computer monitors, but if you know how to make computer monitors, it doesn't mean that you know how to make chocolate. So there is a space, a cognitive space over which societies diffused knowledge and societies diffuse, and our economic complexity paradigm allows you to sort of ask the question, how much productive knowledge has diffused in a society?
How can it grow? What's in the phrase that Stuart Kauffman, I think coined, what's in the adjacent possible of a country and so on, and this idea has been taken not only to space of goods that you could make, but to space of Athens that you can do. So you can think of, okay, where is this place up in thing? And you know, what are things that are somewhat cognitively related to this technological class? Or you can do it to the space of scientific papers. So obviously it's easier to move from some other physical problem than it is to go from say, physics to psychology.
So there are these cognitive distances, and then you can serve like map what are these cognitive spaces are in production, innovation in science, and how can societies evolve in that space and how do they evolve in this space and how can you accelerate their evolution in that space? And that's a little bit what that collection of papers in that special issue tackle.
J. Doyne Farmer (13m 11s): Maybe I could add just to juxtapose something there. When you think about the question of how does technology evolve at the frontier? Part of the story is actually very similar to what Ricardo was just saying, because we have some set of capabilities and we add new capabilities that maybe didn't exist before, but they're always close to the ones that were already there, or let's say there are always things that can be done with just a few steps from the things that are already there. So the whole system builds on itself and the body of knowledge gets bigger and bigger through time, but it is very much about the adjacent possible.
And we've been trying to map that out. Ricardo has done a lot of stuff in mapping that out in the space of products that are exported. We've been thinking about that in the space of patents and looking at the question of how people focus their attention and what goes on. And one of the things we've seen is that people's focus of attention is remarkably consistent, that is you tend to see the focus in a given area in the patent space persist for the order of 80 years.
So people don't work on buggy whips anymore, but buggy whips had their day. And so that actually is very helps us understand where things will go in the future because this persistence gives us some ability to predict and reasonable what's going on.
Michael Garfield (14m 33s): So this is definitely something I want to dig into considerably more in just a moment, but before we get into that particular thing, I personally find it helpful. And I hope other listeners of this show find it helpful to recognize that there's a very deep history at SFI in thinking about these things through the lens of ecology and evolutionary biology and in the dynamics that have been explored in those two disciplines and their application. When we talked about this real early in the show with Brian Arthur in episodes, 13 and 14, about the application of evolutionary models and ecological models to the economy as a non-equilibrium system.
And more recently, Simon Levin and Andrew Lowe wrote the co-edited special issue at PNAS on the application of evolutionary models to financial markets. So, I'll link to those things in the show notes, because I think that there's something real key in SFI has research theme on political economies, thinking about these systems, as in some sense, alive like Chris Kempes and David Krakauer just wrote this piece recently on multiple paths to multiple life where by lensing everything through information theory. Sarah Walker also talks about this a lot that we see an economy or it government as a way of information encoding the like stable properties of an environment and into substrates that can then allow that system to make predictions about itself.
So like, okay, so with that said, there's a term that you and your co-authors bring up in this paper about relatedness, about the relationship between different classes of economic activities. And it's interesting to read this piece because it makes it clear that everything that you just said about the sort of path dependent evolution of these systems and how they spread into the adjacent possible looks a lot like the history of the evolution of biological organisms and the way that we can reconstruct phylogeny is the way that we can understand how different anatomical features are quote unquote homologous, like the wings of birds and bats, both evolve from an arm, or perhaps they're analogous like the wings of an insect of all from a different structure in the organism.
So, anyway, I'd just love to hear you expound on relatedness before we get in a little bit more deeper into production networks and supply chains and the relationships.
Ricardo Hausmann (17m 12s): So let me restart by saying, I'm going to use this very simple metaphor, but I think it's very clarifying. I think to do things you need to have some capabilities, the capabilities you need to make chocolate are different from the capabilities you need to make computer monitors. So I want you to think of products or industries as words and these productive capabilities as letters. So instead of thinking, the world is made out of earth, water, wind, and fire.
Let's think of the world being made of say apples. And these atoms combined into molecules. So products are like molecules and they are combinations of these letters. So in this interpretation, the adjacent possible is things that in letter space are not very many letters away. So for example, if you ask yourself a bear or a zebra and a lion, which one is closer to which, well, if you’re an ecologists and so on, they will tell you something.
But if you just use this Scrabble metaphor of letters and words, you'll realize that a zebra is just like a bear with an extra Z. So there's a one letter gap between a bear and a zebra, but between a bear on the lion, there's a four letters. The four letters you have to make bare or useless to make lion. And so there is an underlying space of capabilities that you're trying to uncover. Sometimes we call it phenotypically by just looking at similarities between things. Sometimes genotypically by looking at how much can we say about these letters?
And so once you get this letters and words, paradigm, whether it's for patterns where there's four saying the science, products, whether it's patents or scientific papers and song are combinations of these letters and our country can do the things that can be done with the letters that it has. So over time, maybe the space of letters grows, a button or a letter in order to survive, kind of has to be used for something. So if you know, you send those nuclear scientists to, I don't know, a country Cameroon, he probably will have no way to combine his letter with other letters and that letter will die.
So the letter has to be able to combine with other letters. And we derive from this theory of inequality, of theory, of growth, a theory even of the relationship between the state and the market, because some of these letters can be purchased in markets. So they have a market and the markets have this invisible hand and finance is one of these elements of the invisible hand. So the market is all like a self-organizing thing. In some sense Adam Smith was in my mind, the first complexity thinker. He sort of realized that there's something weird about this ability of the market to self-regulate, to achieve biologists would call it out or a reproduction or there's some replicating itself.
And it does so by having goods that have prices and prices are an information system. With prices, you can calculate profits. So it's the difference between the price of the output and the price of the inputs. So it tells you no, can you make money making this, even these prices, but profit is not just information. It's also an incentive to respond to information that is in prices and financial markets are trying to lend money to those that are expected to be profitable, which are those that are responding adequately to the information contained in prices.
So you have here the elements of a self-regulating self-organization of a complex system, but you can buy a Tesla, but you cannot buy a highway. You cannot buy traffic lights, you cannot buy traffic rules. You cannot buy traffic cops unless the government does something about it. So there's a bunch of these letters, these capabilities that cannot be bought in markets that they have to be provided by governments. They’re public goods. And if the government doesn't provide those, let's just call them vowels.
Most words require a combination of vowels and consonants, and then you need this cooperation between public and private and so on between the market and other forms of governments to make most of the technologies that exist in the world today. And actually, if anything, technological evolution has demanded more and more things that have to be provided through governments. And how is that regulated? Because in government, there are no prices. There's not supposed to be a profit motive that they call that corruption.
And there's no decentralized capital market that is asking itself, should I fund education? Should I fund social security? Should I fund defense? Which one is more profitable? There's no self-regulating scheme. So it's more centralized. It's more of the budget process and it's more politics. And so that's how I think about the world about these aspects of society and how they evolve.
Michael Garfield (22m 11s): Doyne, actually, maybe this would be a good opportunity to lean on you a little bit here, because have Ricardo says that we don't really have market systems that are being used as a kind of cognitive apparatus for government traditionally, it seems like that might be something that is changing in the last few years. I've seen a lot more conversation around prediction markets and the consideration of these things, not necessarily in terms of their actual pricing, but in terms of being used as a decision-making augment.
And I'm curious to know your thoughts on that, just your observation of a landscape and whether you see the uptake of these kinds of tools, whether you see as things get more and more technologically complex and the policies evolve to become much more intense and ineffable hyperobject, whether it's, you kind of see the boundary between these two domains blurring somewhat.
J. Doyne Farmer (23m 16s): Well, very good question. I think you may be getting out of the things that I'm truly consider myself an expert in, but just a few thoughts. First of all, somebody should really do a better study of prediction markets and how predictive they really are. I haven't seen such a study, but they certainly seem like a good idea. And I guess the question is how much can you really run doing that kind of thing? A related question would be carbon markets. There are a lot of attempts to make carbon markets that have this kind of built-in regulatory systems. So far they've been fairly unsuccessful.
And maybe because we don't quite understand how to do it. And it may be because coming back to what Ricardo said, some things are just better to have intelligent decision makers, make decisions. And for climate change, the most successful policy have been the most dictatorial, renewable portfolio standards have been very successful in rolling out renewables by having policy makers simply say, we need to have this fraction renewables by this date. And basically the utility companies don't have a choice anymore.
We've also done something that's maybe in the middle when we subsidize things like solar energy in order to boost its prevalence. Of course, even there we've ended up doing it in a fairly scary way because people always say, well, isn't the progress that solar energy has made just because of the subsidies. Well, actually, the subsidies are bigger for fossil fuels. The second biggest for nuclear power renewables come in third. And the subsidies for fossil fuels and nuclear power haven't lowered the cost.
The ones for a solar have lowered them dramatically. And so there was a question of using knowledge to understand how the policy levers can be used effectively. I think in the future, it certainly democracy is, I think we're seeing these days is not functioning that well in many parts of the world. I think in the U.S. we suffer from having democracy, 0.9 and they're built in flaws to the system, but certainly it would be nice if we could find ways so that rather than having in a democracy, we each every election for president, we put out one bit of information, yes or no for the candidate, the U.S. democratic Republican, and maybe a few bits for the Senator and Congressman we vote for.
So remarkably the public is getting remarkably little informational input into the decision-making process. And some people have been thinking about how to change that, to make that better, but it's still very experimental. And I think also there's strong incumbents have enormous resistance to change. It's very hard to experiment because the incumbents really don't want people to experiment. They want to keep things, the incumbent politicians, businessmen all want to keep things the way they are, and they don't want to change the system because they've risen in the system by either the status they had at the beginning, or by understanding how to play it.
So it's very hard. This is a place where evolution's very slow, I think,
Ricardo Hausmann (26m 18s): And to some extent, the fact that there are these very strong complementarities between possibilities in the market and things that government could be doing. Like they do have charging stations for electrical vehicles and so on. And so they're very strong complementarities. Society wants to harness those complementarities and it has created lobby groups. And, you know, the U.S. is somewhat transparent on this rather obscure field of lobbying. So they require a lobbyist to register.
And there are more than 20,000 registered lobby groups and they forced them to say, or who are they financing, except for some parts of this donations that are in vehicles, where that are less transparent, but in some sense, what has emerged is a market for influence. And it may not be the best way to organize influence, but it has emerged, I think because of this lack of enough communication between what the government could be doing and what society values, and it's not going through the political system, it's going through this lobbying system. Unfortunately.
Michael Garfield (27m 27s): So I'd like to double back, because something you just said Doyne, about this, about the challenges presented by incumbency and lock in here reminds me of a talk that Raissa D'Souza gave a few years ago at the science board symposium about the collapse of networks and the way that incumbency in a longer time horizon tends to undermine itself because inhibits as you were just alluding to the system's ability to perform at the highest level of its own intelligence.
So it reminds me of, I'm trying to use this as a lever or a dovetail into the paper that you co-authored with James McNerney at all on how production networks, amplify economic growth to get back into this question of the relationship between political economy and technological evolution, because there's that Bob May 1972 paper about how will a complex system be stable.
And there's this question about the gains as things become more and more complex, the economies of scale, the ability of, as you were just saying a moment ago, Ricardo, systems, as they expand their alphabet and their lexicon of technological products, but then the way that these systems also become more fragile to disruption in certain ways. And so, you know, just quickly as a callback to episode 81 that we just did with Brandon Ogbunu where he was talking about John Maynard Smith, offering a very kind of similar letter game analogy to the evolution of protein spaces.
And, you know, the way that as you move across this landscape, the words have to make sense. You have to be able to actually write a word that is legible in some sense. Anyway, this is, I'm kind of getting ahead of myself here, but I would love Doyne for you to introduce this paper and to explain what you and your co-authors found with respect to the relationship between production chains and how that is related to economic growth.
And then from there, I'd kind of like to explore what I was just saying, which is this push and pull between the desire of the system to grow and then the way that growth introduces new vulnerabilities.
J. Doyne Farmer (29m 54s): So let me maybe start back with the idea you threw out earlier about ecology and what is an ecology? I mean, if you just step back in a logical sense, what is it college about? Ecology is about the interactions of specialists. So animals are specialists in doing certain things. Grass is a specialist in extracting energy from the sun and turning it into grass. Zebras are experts in turning grass into zebras. Lions are experts in turning zebras into lions, and they figured out very specialized ways to do that.
And the relationships I just specifically gave that to indicate a trophic level. So we can say have a trophic level of grass is at the bottom. Zebras are in the middle, lions are at the top. Of course in the real world, things are more complicated. Ricardo mentioned, bears eat berries, berries also are carnivores, so things are in the real world are more complicated, but nonetheless ecologists have found it very useful to think, to organize the interactions of animals based on their specialization and how they interact with each other.
So the production networks very similar. We backed Adam Smith, who I agree with Ricardo was the first complex systems thinker. One of the key things Adam Smith realized that specialization is the key to the way the economy works. And so if you go on to business, you see the same thing. When I was at prediction company, we were specialists in looking at little relative wiggles and stocks and making bets to how some of the money that's sloshing around go into our pockets. And whether we were doing something useful or not, I don't know, but we all specialize in what we specialize because we're bound to be rational and we can't do everything. So we find something we can do that fits into the system. Now in the paper, you mentioned it was led by James McNerney we used this analogy with how we thought of the idea originally that we could organize the economy and trophic levels, just like grass, zebras, and lions. And James had this wonderful idea that because things are a bit different than that because when the lion eats the zebra, the lion gets the zebra, but the lion pretty much crunches the zebra and turns it into lion.
And there's not much zebra left. But when you're making stuff, when you're making a laptop, the company that's making the laptop, buys the chips from one company and they buy the screens from another company in the case from another company, and the lawyers are provided by another company. And so on, there's lots and lots of specialization. And if any of those make improvements, those improvements get passed on and lower the cost because competition drives prices down. And so we realized that that suggested that actually going beyond Adam Smith's idea, the economy specialized innovation is specialized and things that have deep trophic networks that is deep supply chains should improve faster than things that have shell supply chains, because there's more opportunities to innovate coming up the supply chain.
And so we, first of all, showed how you could compute trophic levels, which turned out to be very close to what are called output multipliers, but they're just used in a different way normally. And we showed that this is highly predictive about progress at the country industry level. So the predictions work extremely well. So it's part of showing how the ecology is really an important thing to understand how the economy is organized. Just a different aspect than what Ricardo's research is doing.
Now. I guess one of the things I'd like to say is that ecology automatically implies a certain kind of inequality because we're all specialists, we're all playing very specific roles. Some of those roles may be in more or less demand than others at some point in time. And ecology also sets up how evolution happens because the environment that affirm is evolving in depends on the entire ecology that the firm is sitting in. And so if you want to understand the evolution of firms, you have to understand the ecology of firms that they sit in.
So I think these ideas are essential for really understanding how the economy works. And I think it's nice that both from Ricardo's work my work and other work that's emerging in this we're really saying that these ideas have predictive value and can help us think about how to manage the economy better and actually how to deal with political economies like inequality because if you want to reduce inequality, for example, you had better understand what's causing it in the first place and the causes aren't as simple as some bad guys are stealing money from some poor guys.
So you really need to understand the root cause of those things if you want to regulate them. And so I think ultimately ecology should be a core concept for a political economy because it's at the root of the issues that emerge.
Michael Garfield (34m 49s): So Ricardo, just to give people a specific thing to hold onto here, I think we're kind of dancing around. There is actually a specific paper we'll link to in the show notes, Productive Ecosystems in the Arrow of Development that you coauthored with Neave O’Clery and Muhammad Ali Yıldırım. One of the things that you talk about how the network model you use here is predictive of product appearances. This is something I remember talking about with Jen Dunne about her trophic network research back in episode five, saying, can we use this type of thinking to actually inform the investment in innovation?
And it would seem to Don's point about inequalities that like, there is something about the, I guess, access to this kind of insight and the inequality of access to an understanding of calling an ecospace of product possibilities and economic relationships that reinforces those inequalities. It's like when you think about, I don't know, like evolutionary arms races in the Cambrian and like the way that the evolution of the “I” created this explosive entrance of the animal ecosystem into a three-dimensional space where suddenly the animals with better vision had this competitive advantage. Just to continue riffing on a kind of ecological or evolutionary metaphor I'd love to hear you talk in light of this paper and your work more broadly about what light does this shed on those kinds of problems like specifically when we're thinking about inequality of data access and the way that in some sense, it seems like the average end user of digital information platforms is more like a, trilobite sort of just trying to maintain, trying to survive in this dramatically on equal relationship with these very capable motile predators in the water column.
I don't know if that makes any sense.
Ricardo Hausmann (36m 54s): Let me say three things. The first one you mentioned before that, you know, as this complexity increases, things become more fragile. Actually it's the other way around. As complexity increases, things become less fragile. In the nineties. I did a lot of work on volatility and instability and crises and so on. And so if you go back to the Scrabble model where you brought these capabilities are letters and products are words, it has the following implication that the more letters you have, the more words you can write and the longer the words that you can write.
So if you have a lot of letters and one particular work gets in trouble, you can recombine those letters to make many other words. If you have very few letters and you know, one word gets disrupted, you may not have other things to do with those letters. So a country like Venezuela, my home country, not very few letters, it was all making oil. The oil industry gets in trouble. And the economy implodes, you have a city like New York city where it had an enormous concentration in the garment industry.
In the 1950s, the garment industry disappeared and nothing happened to New York City. The car industry shrunk and dramatic things happen to Detroit. So more complex places have more letters. They can sustain more words. It can sustain longer words. You want to think of the length of a word as in some sense of measure of a product complexity. So actually we've shown that more complex countries are less volatiles, et cetera. So that works quite nicely on the inequality side, I think that a lot of the inequality that matters is inequality in productivity.
It's not that there was a pie and some people are getting a big chunk of the pie and others are getting a small chunk of the pie. It's the different people are baking pies of radically different sizes. So it's not in so much in the slicing as it is in the underlying productivity, the technology that they're using. So in essence, poor people are letters and they could be participating in long words, but they happened to be trapped in places that have few letters. So there's an enormous inequality in the variety of letters in places.
And this inequality is in some sense, indogenous because the more letters you have, the bigger the payoff of getting any extra letter, because if you have two letters and I give you one, there's only two additional combinations that you can do with this letter. If you have 10 letters and they give you one, you can combine it with 10 others. So this leads to desire to accumulate productive capabilities in places that already have a lot of productive capabilities. So if you cannot put all the letters everywhere, in some sense, you're trapped between putting some letters everywhere and put them all letters somewhere.
The market would like to put all letters somewhere. And that's why you get no places as dense as New York City and people cramming into a subway and so on because they want to mix their letters with other letters. That's why you see these enormous efforts that people put in just commuting to work in combining themselves. So in my mind, there's a lot of the discussion in the equality that leads to a discussion on redistribution. And in my mind, the real solution to inequality is not so much redistribution as inclusion as incorporating people into the possibility of mixing what they are, their letter into longer words into the kind of production networks that don't was talking about. And that leads to a very different agenda for inequality reduction. Do you send people a check or do you connect them to the urban transport network? Do you connect them to the financial approval? You connect it to the labor market? Do you connect to the schooling system? So do you connect it with a bunch of all the letters where they would be able to participate in more complex production?
Michael Garfield (40m 47s): We talked about that a bit with Matt Jackson and 12.
J. Doyne Farmer (40m 50s): Maybe I could just underline a point that Ricardo made, which is we've been looking at productivity. And so what is the distribution of productivity? As Ricardo said, there's a huge diversity. Well, it's even worse than that in that the distributions are very heavy tailed. That is, if you actually wade through the data, you look at individual firms in Europe, for example, they're consistently what's called a levy stable distribution, which has very fat tails, tails that are so fat, the mean exists, but the variance doesn't exist.
In other words, the variance, if you ask what's the width of the distribution, the width that you compute using say standard deviation depends on how many points you get. As you get more and more points, the width that you calculate trends up and up to infinity. And the exponents around 1.5 or 1.6, and this is a really important factor that needs to be taken in account. When people think about productivity, it turns out many economists have been doing things like coding statistic, the standard deviation of the productivity distribution, or throwing away the negative tail and taking logarithms that give you a very distorted view of what's really going on.
But this is just the underlying Ricardo's point that there's a huge diversity out there. And we really have to cope with that because it's inherent to the economy.
Michael Garfield (42m 10s): So I mean, precisely to that point, I'm thinking of work that she was like Vicky Yang and Geoff West and Chris Kempes, and a couple other folks I'll link to in the show notes, talking about decomposing earlier work on the benefits of urbanization and finding that actually the economic advantage of living in a city is like, you were just saying a moment ago, it's like very, very disproportionately in favor of something like the top decile of people living in the city and that the poor living in the city are actually not really getting any advantage at all.
And so this is something that you see it at a number of different scales. And when I think about Nick Hanauer’s Pitchfork Economics, and these other people, Hanauer being this billionaire who stepped forward and said to other billionaires, we need to think about this as more than just profit maximization, because ultimately we're going to undermine ourselves. And so that's what I mean, Ricardo, I think I'm really glad that you made the distinction here between the ways in which this kind of complexity offers more or less vulnerability, but like there is this sense in which building a tower to the sky, a very narrow tower, this levy distribution, it does seem like eventually it's part of the process by which systems create indogenous collapse.
And so when we're talking about supply chains, I want to use this as a lever into the paper that you led recently on trade-offs in a pandemic, Ricardo. But I want to give Doyne one last opportunity to speak to this piece on production networks and economic growth that watching the supply chains disrupted as they were during the COVID pandemic and still are in many ways, I couldn't get a new heater installed in my house this winter. It was impossible.
And I've been seeing just online this thing has accentuated has kind of accelerated the conversation around relocation movements around the balkanization of the internet. The precariousness that people feel in these circumstances has in certain ways kind of challenged the rhetoric around globalization and around the sort of unilateral benefits to long supply chains and very complex global economic structures.
And so I'm curious how you see these trade-offs and then I think that gives us a good point of entry to talk about specifically, Ricardo, how you and Ulrich Schetterare thinking about the trade-offs involved in policy around the containment of COVID 19 and future pandemics.
J. Doyne Farmer (44m 52s): Well, so one of the remarkable things about COVID is suddenly supply chains become very interesting to everybody. Why, because there are certain ways in which having a complex economy with lots of specialization is not as robust as we would like because when parts become too specific or too tied to one particular manufacturer, then a disruption can cause a big problem. There was a tsunami in Japan that knocked out a piston ring manufacturer, and that caused Toyota to not be able to produce cars for several months and several other brands around the world that depended on those specific piston rings.
So supply chains do have some vulnerabilities, though I think it's also interesting look with COVID how quickly they adapted. We went from having no masks to having masks up the wazoo in a matter of not that many months. And I think one of the things that I think we should get better at is actually understanding how our own supply chains work because they're out there, but nobody actually has a map. There was no global map of supply chains. This is in part because companies keep them secret. Governments also keep them somewhat secret, like the Chinese keep their supply chain secret.
Why? Because in a war, what do you do? You tackle the enemy supply chain and vice versa. But on the other hand, when we have problems, understanding those supply chains makes it very useful in trying to resolve problems. Now, one of the papers we did that I'm most proud of in terms of its practical application is a paper we did on predicting the impact of COVID on the U.K. And actually as a function of the strictness of the lockdown policy, the trade-off between how many desks there were likely to be and how badly the economy would get hit.
And the model we made, I think is a very complex systemsy kind of model. There's a long tradition going back to Leontief thinking about supply chains in a static way and calculating the equilibrium. And that's been very useful, but we did it dynamically. So, you know, in COVID there were both supply shocks and demand shocks. They were running simultaneously and they were propagating through the supply chains. So at an industry level, we predicted the shocks based on what we thought COVID was going to do to the labor market.
We knew that lack of laborers would shut things down. And then we could just see how this propagated in both directions and collided. And we predicted that in real time, in the second quarter of 2020, the U.K. was going to take a GDP hit of 21.5%. They took a GDP of 22.1%. And when we go do the retrospective to see what happened, there were a few places we got lucky, but basically it worked because we had accurate models of the production that were, we actually interviewed industry experts, industry by industry, so that we knew the production, the essential aspects of the production network in each industry and steel, for example, if you're going to make steel, you need iron, you need heat, you need coke.
But you know, you look at the inputs to the steel industry. Restaurants are an input. You can make steel without restaurants. You can make steel without management consultants. So we actually found all of the essential inputs of each industry and then just ran a simulation and it worked amazingly well. So that's all just a pitch to say, we had to do it at the industry level. If we can actually start to map out supply chains better at the firm level and the product level, then I think we could really understand how supply chains work. We could manage them much better. And in crises we could do a better job.
One of my ex-students is now doing this for the Austrian economy due to the hit that they're taking to the withdrawal of Russian gas and oil. And it's a very similar kind of model at the end of the day,
Michael Garfield (48m 36s): Is that Maria?
J. Doyne Farmer (48m 39s): Maria Chanona and Tony Pichler. Go ahead.
Michael Garfield (48m 45s): I wanted to bring her into this actually, because we interviewed her during the board of trustees symposium on complexity economics a couple of years ago, and the work that she did with you on the disruptions to the labor market from automation. And so that's intimately related to what you're talking about here and so Ricardo, when you talk about bringing a more complex analysis to the question of redistribution versus moving people into different parts of the network, it's kind of akin to something that you said earlier about how, you know, what do you do?
Like when you put a nuclear reactor physicist in Cameroon, like, does that actually work? Are we making words that actually make sense here? And Maria's episode was really fascinating because it was kind of about the landscape of possible paths that people can take in re-skilling themselves as the markets are disrupted. And so there's a similar thing here that kind of gets into the question of how we can use the techniques that the two of you have developed. And, you know, whoever wants to get into this I'd love to hear from you both on this, about the way that sometimes the possible is not actually adjacent or that there's these dips.
If you want to talk about like an evolutionary fitness landscape that are impassable, the bridge is knocked out by a flood. So it seems as though, as the landscape of the century gets more and more turbulent, more and more people are going to get stranded, not only literally on islands that are going under the sea, but on islands in the labor market landscape or islands and the productive networks of interconnected cities and countries that are now imperiled in a way by this change. And I'd love to hear you speak to this.
Ricardo Hausmann (50m 32s): I think it's important to understand how production networks and the global value chains have a very positive side that is somewhat unexpected. You can think of international trade as training words, but with global value chains, in some sense you can trade syllables and syllables are shorter than words. And so places that are enabled to make the word can still make the syllable. So let's take a concrete example. To make a shirt you have to procure the right materials at the right price and the right quality and song. You have to design the shirt, you have to count it. And so it, you have to package it. You have to brand it, you have to market it. You have to distribute it. In the old days if you didn't perform all those tasks, well, you lost your shirt. Right now with global value chains, you can have procurement done in one place, cutting and sewing down a load place, design in a different place, branding, marketing in a different place.
So countries can get into the business with fewer letters and then they can more parsimoniously add letters to their letters. So for example, the Novo started assembling the IBM ThinkPad, and eventually they took the whole thing, but in this sort of a gradual approach of adding some letters. So that has represented a very important avenue for growth and development for many countries that started with few letters. The jump to the next syllable was shorter than it would have been if they had to make the whole word.
So in that sense, that aspect of global value chains have reduced global inequality and have allowed for a more parsimonious increase in productive capability. Now it's one thing that you get a shock and if you have a long value chain, maybe it's disruptive. Don't speak about that. But it's also true that in are in a complex place, they're doing a lot of stuff. And one part of the economy that gets into trouble is not going to percolate to the whole thing, but what happened in the U.S. medium size, small size city, the rust belt and so on is that they were relatively undiversified.
They got hit by the China shock. That industry, that they were exporting to the rest of the country and the world got disrupted. If capable people left, the less capable people stay with very serious social problems, drug addiction, and depression and so on. And you get these bad, bad outcomes. Those tend to happen precisely because there's insufficient diversity of letters where if you get disrupted from an industry there, another job you can do somewhere else. There is a very nice paper by Frank Neffke who used to be at the growth lab with me.
And he's now in complexity, science, helping Vienna. And he showed that many puzzles in industrial economics and in economic geography, can be solve by noticing that how much you earn as an individual depends on how complimentary you are with the people you work with. So an anesthesiologist working on his own is not much better than a bad lecturer. He will just put you to sleep. But an anesthesiologist working with a surgeon is a different story.
And what the large firms allow is for better matching, better complementarities. And what large cities allow is for better complementarities. And he shows that many of the puzzles though, wage firms size wage puzzle, the city size wage puzzle can be explained by these better, by these more complimentary team. So a lot of inequalities you observe have to do with this inability to collect. We published another paper with Frank Neffke in August, 2021 using a business travel data.
We used paper using business travel date, the old data, unfortunately in the middle of a situation where there was no business travel, everybody had shut down their borders. But our idea was that in order to implement technology, you need know-how and moving know-how into brains is very slow process. And Malcolm Gladwell says it takes 10,000 hours to become good at something. And 10,000 hours at 40 hours a week is like five years. So it's a very slow process, but moving brains is a very quick process.
So we showed that business travel increased productivity, increased employment, increased exports. And so we speculate what would happen if suddenly you shut down those connections? So besides the sort of like these physical networks that Doyne was talking about coal, iron to make steel, this aggregation of know-how and the combinations of anesthesiologists and surgeons that need to happen for production to happen are quite central to the story of complex production.
J. Doyne Farmer (55m 39s): I think you brought up, Michael, this model that Maria made of occupational mobility. And I think that actually connects closely with a lot of the kinds of things that Ricardo's group has been doing. And that what Maria did in that model was look at data on specific occupations and look to see when people change occupations, which occupations do they move into, and how can you predict that? Well, you can predict it at least in part based on work activities. So if you know what the work activities are that each occupation performs, you can predict that.
And so it goes back to a different form of adjacent possible, and that there's an adjacent possible in occupation space. And you can actually see how workers move around in that space. And if you have a shock, like a coal mine shuts down in Kentucky, how are you going to deal with that because it's probably not the right answer to build a solar plant in Kentucky and have the coal miners go tend to the solar plant. Instead, you have to really understand that a fusion to understand the right policies to efficiently move the workers around.
And one of the things we showed in that model was that the labor frictions are large. They can dramatically increase unemployment because people can't get to the job that they want to have because there was no natural way to transition to it. And it can cause things to take years for people to diffuse through the network. Now, just to amplify another point Ricardo was making, this network is very closely coupled to the production network and the two things are co-evolving. So if the production network changes, the occupational mobility network needs to change to adapt, or the occupational emphasis in each occupation needs to change, which means people have to move through the occupational mobility network.
And so those things have to adapt in tandem, but similarly, if you want to do a business and you don't have the right talent, you can't get there. So when we're thinking about something like climate change, we really want to make sure, first of all, one, understand what the implications are for unemployment. So we need to think about the occupational mobility network. And we also want to make sure that we don't get bottlenecked because we don't have the workers we need to do the things we need to do. So we need to think about that connection as well.
And I think this is a general pattern that the economy can be thought about in terms of these networks and the networks are coupled together. The thing we're trying to do now is couple the patent network so we can really think about innovation and growth and how that's influencing the system. So now we're talking about three different networks, and then of course, there's the financial network that Ricardo mentioned earlier that needs to be coupled in, whereas the capital going to come from. And so I think one of the big contributions complex systems is starting to make, and something is, think about these interacting networks, build agent-based models for how they function and couple them together at higher levels.
So that we can really start to think about how the economy really works.
Ricardo Hausmann (58m 38s): And let me maybe mentioned some related work that's being done led by Frank Neffke and others. So we've looked at these occupational spaces and so you can easily measure transitions between one occupation and another. So that gives you a sense of cognitive distance if the same person can do both, but there is a thick description of these occupations. There's a database called OwnIt, which tells you the knowledge, skills, ability tools that a certain occupation needs to have, training.
And so we can look at these descriptions to look at distances, cognitive distances, between occupational and then ask whether these cognitive distances predict these transition probabilities that Doyne was talking about and they do. And then through an enormous research project that could not have been done without the machine learning and natural language processing and so on, we took something that predates OwnIt and it goes back to 1930s, something called the dictionary of occupational titles.
And so we looked at the history of occupations going back to the 1930s to answer questions that happened before Moore's law and before computers and before things, why, for example, the wage gaps between men and women fall in that period. And what was happening to the content of work that had that. We also looked at there's automation and there's some indexes of what jobs are likely to be more or less likely to be automated. Then are they in central parts of this network where people have an easy time moving to other occupations or the things that are near those occupations?
Are they also going to be automated? So there's a very rich research agenda on this issue. And I think also though the combination of this with production that don't mention is also extremely promising and it's still to be done. It's still in the to-do list.
Michael Garfield (1h 0m 43s): So the last paper that I want to discuss on this call, I mentioned a moment ago, this paper that you led Ricardo with Ulrich Schetter on horrible trade-offs in a pandemic poverty fiscal space policy and welfare seems to be a place where all of these factors kind of come into focus together in terms of the way that networks are shut down and the way that interleaves with questions of inequality and exposure to risk. I'm thinking about, again, just to give an over-simplistic biological metaphor, this idea of what kind of animals are capable of hibernating, the role of brown fat in the metabolism of bears and so on.
So I would love to hear you provide some introduction to this piece, to the model that you built here and what your work says in terms of the considerations that policymakers ought to bear in mind in the event of future pandemics or other network-related sociological diseases. So what were the findings here?
Ricardo Hausmann (1h 1m 54s): And let me say Ulrich Schetter is all fantastic guy love working with him. He has his very deep research agenda, but I always bothering to think jointly about some hot issue. And lately we've been thinking about smart sanctions to Russia, and we can talk about that. It's related to what Doyne was saying. If you want to harm Russia and the production network and their reliance on imports and so on, how can you do it? And what's the best way to do it and how you do it at the cost to yourself, et cetera.
In the middle of the pandemic, we started to think analytically, you know, what will the pandemic look like in poor countries? And obviously there were two instruments that countries were using. They were using lockdowns and telling people you have to stay home. Otherwise you will infect others, especially if you don't have testing and so on, and nobody had testing, but in order for you to stay home, you have to be able to survive by staying home. So I have to send you a check for you to stay home without going to work.
So you have lock downs and you have transfers. And in the U.S. the transfers were so massive that household income actually went up during the pandemic. So we asked ourselves, what are the trade-offs when a country cannot borrow like the U.S. borrow when countries have limits in their capacity to borrow. So when you solve the optimization problem, they obviously are not going to be able to send those transfers in the same magnitude, but they are going to, as a consequence, not be able to stay home.
And if they leave home, they're going to affect others. So the pandemic is going to be a worst. So there's some sense of trade-off between staying home and not dying from a pandemic and staying home and dying from hunger or depravation. So in this trade-off is worse. If governments don't have fiscal space and we use that paper and these arguments to justify why the world had to be very generous in making transfers to governments so that they could sustain their social policy during the pandemic,
J. Doyne Farmer (1h 3m 60s): Maybe to riff on that a little bit, we're actually just finishing up a paper where we look at the City of New York, where the phone company has got 600,000 volunteers to let their cell phones be tracked so they could see where their cell phone is every hour. So you can look and see how people are moving around the city during the pandemic. And you can see when the pandemic starts, then you can see that we're going back and forth on a certain route pandemic starts, and then they are doing that. You can see they stayed home. So we can back out information about whether they're going to work or not.
And then, then we could run a model in the style I mentioned before to try and understand what the consequences will be to the economy. The same time you can look at what the consequences are epidemiologically. And I should say, this is led by Marco Pingallo. And we teamed up with a bunch of epidemiologists in the group of Alesandro Espinyani. And so they're doing the epidemiology, we're doing the economics and you really see the way these things interplay in a very fine grain way.
And the data is of course, highly imperfect, but a lot about what economic activities are going on in each neighborhood, even if you don't know the supply chain. So if you make some fairly heroic assumptions about the input output network, you can then actually predict what the economics effects are going to be neighborhood by neighborhood. And you see very clearly they're much worse on poor people, rich people. The Bronx gets hit a lot harder than Manhattan. And you can really see this trade-off once you built the model, the model matches the data.
You can then look at the trade-off as you turn the dial back and forth for the lockdown policy to see the trade-off between deaths and economic pain. And it's very clear on many cases counter-intuitive because sometimes it's just kind of the slope you would think. Other times you can have these flat plateaus so that there are really sweet spots in the policy space for what should be done. And so I think both this kind of work in what Ricardo said are both giving us the potential tools to have much finer insights about how to manage things like this.
Ricardo Hausmann (1h 6m 8s): Yeah, this is really cool. I didn't know about that work, but one of the political economy, since you started asking about political economy, one of the political economy implications is that rich people like lockdowns and don't like transfers and poor people like transfers in don't like lockdowns. And so a lot of the tensions around the pandemic policy make a lot of sense, given sort of like this difference between how sustainable it is for you. I mean, bears that have fat that can sustain themselves and animals that need to be eating on a daily basis.
Michael Garfield (1h 6m 42s): I mean, perhaps the bow to tie on this involves statements that you both have made recently about the inequities and impact here. You mentioned in this paper that there are other differences beyond like the level at which subsistence consumption is possible, such as the ability to work from home, Doyne, that kind of connects to what you were just talking about, about being able to parse or infer industry based on mobility data. It occurs to me that there's a something funny, and I'd love to hear your thoughts on this given recent news about lockdowns in Shanghai and the struggles that people have been having, even in these relatively affluent major metropolitan areas.
And you mentioned in this paper that a general lockdown as a blunt policy instrument and that smart testing and containment policies or alternating lockdowns may substantially improve efficiency, but are unlikely to fundamentally change the trade-offs. So given the research that both of you have just mentioned here and, Doyne talking about sweet spots and policy space, I'd love to hear a little bit more about that. And then this kind of thinking, not only in sort of the wartime considerations of fighting a pandemic, but more broadly in light of what we were talking about earlier in this call about the disproportionate gains to different members of society on from technology and from urbanization, how we might think about this in order to frame the kinds of interventions that we understand are necessary to keep society from becoming so unequal it collapses on itself.
Ricardo Hausmann (1h 8m 20s): Let me just give you this insight. Production has become information intensive. Making an airplane implies spending 10 or 15 years, designing the airplane before you make the first airplane and contracting all the parts before you make the first airplane. And then they come out like cookie cutters. There are many people who work with concepts and manipulate ideas, and there are people who have to touch materials and change materials. We used to call ones that used concepts, white collar workers, and the ones that touch materials, blue collar workers.
Well, it just so happens that the stuff that you need to touch with your hands, you have to be there. You cannot do them from home, but conceptual manipulation it's easily done from home. So that created a divide between those that could stay home and work, and those that needed to go to work and that cut along income distribution lines. We have been thinking about a different longer term effect on them. It sort of showed us that there are a lot of things that don't need social agglomerations that can be done from afar.
So we've been looking at how might that change the nature of global work and will that open avenues for developing countries to do things in their towns and cities for these global value chains, connected by zoom and internet and whatever so that you don't have to hire from the local labor force, but you can hire from a global labor force. And there are some platforms that are doing that, Upwork and others where you can there's trading past, some are peer to peer. Some are companies that are organizing, so like a set of services to be done remotely.
And I think that's good for less developed places and especially remote places that have been very hampered by distance, sort of like the death of distance for these activities, but it will be bad for the urban agglomerations where these activities are used to corrugate.
J. Doyne Farmer (1h 10m 23s): Maybe just rip on that a little bit, first of all, let me just say a little bit about the database that Ricardo mentioned. It's so amazing example that the bureau of labor statistics put this together. It was a key tool that allowed us to predict the size of COVID and do so fairly accurately and very farsighted that they did that because you can really see lots of information about occupations, like how close together the people work. Well that told us something about how much they were going to infect their coworkers, which then related to whether they could work at home or not.
And then it just pops out of the data for exactly the reasons Ricardo said that poor people have a much harder time working from home and they got hit a lot harder as a result by lockdowns, but maybe just take the inequality question in a different direction. One of my economy, physics colleagues Victor Yakovenko from University of Maryland made an extremely simple model where he assumes that imagines, that people just randomly collide with each other. He has a bunch of agents in the model. They're given some wealth at the beginning, they're all given the same wealth.
You start with a perfectly equal world and they collide. And every time they collide, which can take, you know, an economic interaction, one of them ends up a little better off than the other. And so that means there's a wealth transfer from one to the other, every time they collide. And he has one other simple rule, which is that they get a certain amount of income, proportionally income they have. So they, they get to invest their income. And those two simple rules, you just automatically get a distribution that looks more or less exactly like the distribution of income that we see in every country in the world.
And it has a heavy tail shape. The body's just right. And I think what that's telling you is that, okay, it's telling you that there is inequality of something that's built in. There are forces that are generating inequality, and if we want to control it, we have to understand those forces. And we have to think about the right policies, like Ricardo said, that will damp it out. And I totally agree with Ricardo that it's much more sustainable to change the number of pies people make rather than how much of the pie they're given.
I think we really need economic models that take that kind of diversity into account as agent-based models can, but it hasn't been properly exploited yet. Something that's very much on the agenda to do and think about, well, to be able to simulate how inequality will change as we vary the policies and the economy that we have control over. And so that includes transfers, but it also includes other things about workforce and education and so on that have more long lasting effects.
So I think complexity, economics and agent-based modeling in particular when combined with these rich data sets and the kind of thinking that Ricardo has been talking about really give us the potential to have a real laboratory to understand how policies will affect the economy. And then I think the next step is to understand how the economy affects society and vice versa, because you could connect these models to political science ideas. You can think about the role of governments and different types of governments, authoritarian governments versus democratic governments, and how these things play out with things like global value chains or the big event that's looming is the green energy transition.
And as the green energy transition happens, if a given country wants to navigate it well, how can they do that? And what political factors does that depend on more deeply.
Michael Garfield (1h 14m 1s): Excellent. Thank you. So, I mean, just in closing, you know, a lot of the questions that the two of you have raised in the last quarter or so of this call seem like areas of focus for SFI in the exploration of this new research theme. And in particular, Ricardo, you're leading a meeting at SFI this summer on some of this stuff. And I would just love to give you both an opportunity to stump your participation, your involvement in this program, and to hint at some of the questions and research collaborations that you're excited about exploring in the months and years to come with this.
Ricardo Hausmann (1h 14m 40s): Sure. I mean, this organizing an event with James McNerney on the evolution of technology and so on. I would inscribe it into a bigger effort that the center is going on political economy, because in the end, what has changed in the world is our knowledge intensity of production. I mean the work of Robert Boyd, and so on, you had shown that even, you know, hunter gatherers rely enormously on knowledge and information to survive, et cetera.
It just has become so much more so. And I find their work super informed about the evolve our world, but the technology is embedded in a system of education. It's embedded in a system of science. It's embedded in the system of production. It's embedded in the system of governance. So somebody invents a cell phone and they want their property rights on the spectrum. Otherwise cell phones, companies can't work. And so how do you get those coordinations and so on?
And so the trick in SFI is to bring people together that have very different backgrounds and very different takes, put them together and see what happens. So I'm waiting for the sparks to jump. I'm not trying to be too dirigiste in this. You have a general thematic topic and people who have been written about it a bit. Let's see how it goes.
J. Doyne Farmer (1h 16m 10s): First of all, let me just say one of the things that occurs to me as I'm on this call is Ricardo and I need to interact more than we do, and I'm hoping the SFI can provide a vehicle for that to happen. So Ricardo, we need to make sure we get there together more often than we do.
Ricardo Hausmann (1h 16m 26s): That's actually, we are together now thanks to SFI.
J. Doyne Farmer (1h 16m 29s): Yeah, that's true. And so this is already connecting us, because I think it's pretty clear from this conversation, how much our activities and thinking resonates. And that's something that SFI does very well. Now to stump my own wearers I guess I think we need to create models of the economy, digital twins though, the word digital twin evokes, too much detail. We're talking about things that are much more, a very crude representations of the economy, even when they get complicated, but traditional economic models have had a very hard time dealing with inequality.
It's a big topic and macro economics and dynamic stochastic general equilibrium models. But when you look at the way they've put a quality in those models, it's very clumsy. And at the end of the day, I don't believe any of the results that they generate. Whereas in an agent based model, you can put inequality in with all the precision you want. You can have a synthetic population and your model, if you want to hundreds of thousands of agents, it's quite feasible to do that, that really capture all the demographic diversity in the world.
You can put in firms that capture at least some reasonable level of verisimilitude of production. You can simulate the economy at the level of individual firms and individual products, and we have more and more data sets that allow us to calibrate these kinds of models so that they're fairly realistic. You don't want to build some gigantic complicated thing and push start. You build the pieces one at a time, you carefully test them against each other, like the model like Maria has, the model that Tony Pichler built for COVID dynamics, which is really a dynamic input output model models that we built for the energy transition.
But we're at the point where we can begin to start coupling these models together. We can begin to start really calibrating them in much more realistic ways. And I think it gives us a policy laboratory at the end of the day, that allows us to think about the essential questions and political economy because political economy is a word that evokes a danger of getting lost in philosophical discussions. And I like those kinds of discussions, but I think we need to go beyond it to provide some useful quantitative tools that allow us to really think about the consequences of decision-making allow actually business people to think about strategy.
If I want to be a player in the green energy transition, how do I do it either as a, a government civil servant or as the CEO of the company? And I think we can build models that really make that possible. And you can then ask that question, well, how do I do it responsibly so that I don't make inequality worse as a result of this? So, I think we're approaching an ability to understand and model ourselves because it's a kind of a, to use a phrase that Ricardo started this discussion with a social brain, the kind of the consciousness gives society kind of a model of itself that allows it to plan its own behavior at least if we can get the political process to line up with that. And so I think that these things should play in a central role and SFI can play an essential role in demonstrating turning political economy to something that has practical, concrete, quantitative underpinnings.
Michael Garfield (1h 19m 54s): Excellent. Thank you both so much for not only your extensive contributions to this research, but also taking so much time to talk with us today about all of this.
J. Doyne Farmer (1h 20m 4s): There's a lot of fun. Thanks, Ricardo
Ricardo Hausmann (1h 20m 8s): Me an opportunity to update myself on Doyne's recent work,
Michael Garfield (1h 20m 13s): A big part of the goal of this show.
J. Doyne Farmer (1h 20m 16s): I look forward to more conversations, so I hope
Michael Garfield (1h 20m 19s): Great. Both of you take care.
J. Doyne Farmer (1h 20m 20s): Thanks.
Michael Garfield (1h 20m 23s): Thank you for listening. Complexities produced by the Santa Fe Institute, a nonprofit hub for complex system science located in the high desert of New Mexico. For more information, including transcripts research links and educational resources, or to support our science and communication efforts. Visit Santafe.edu/podcast.