Odd Lots - What Complexity Economics Can Add to Our View of the World

Episode Date: July 22, 2021

Over the past year it's become clear that traditional economics doesn't necessarily do a great job of accounting for real world problems like transport gridlock or irrational decision makers. For instance, sawmills' first response to the Covid crisis was to cut back production because they were scarred by the memories of the 2008 housing bust. Container shipping issues have caused widespread supply chain issues, and so on and so on. Enter complexity economics, which views the economy as the outcome of decisions by sometimes irrational participants who are constantly interacting and learning from each other. In this version of economics, nothing is ever stable or at equilibrium and everything is always changing. Brian Arthur, economist at the Santa Fe Institute and visiting researcher at PARC, explains why complexity economics might be the perfect way of viewing the world right now.See omnystudio.com/listener for privacy information.

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Listen on Bloomberg Radio, stream the show live on the Bloomberg business app, or listen to the podcast. That's Bloomberg this weekend. Saturdays and Sundays starting at 7 a.m. Eastern. Make us part of your weekend routine on Bloomberg Television, radio. and wherever you get your podcasts. And welcome to another episode of the Oddlots podcast. My co-host Joe Wisenthal is away today, so it's just going to be me for a bit. And one of the discussions we've been having on Oddlots

over the past year or so is this idea that traditional economics doesn't actually do a great job of taking into account a lot of important parts of the world economy right now. And what I mean by that is if you look at something like the supply chain issues we've been having in the market, we've been talking a lot about the shortage in semiconductors, or if you look at things like transportation gridlock, we've spoken tons about the chaos and container shipping this year, a lot of those sort of factors or complexities don't actually make it into traditional economics. So for instance, one of the things I learned this year was that the sort of classical definition of comparative advantage where, you know, one nation is very good at making guns and the other

nation is very good at making butter or wine versus cloth, something like that. And so they're supposed to trade with each other. That classical model doesn't actually take into account transport costs, or at least it didn't when it was first proposed. So if economics is all about simplifying assumptions in order to make it useful, you kind of have to ask about the trade-off between simplifying everything and then overlooking quite important things that are impacting the economy. So it's something that both Joe and I have been thinking about this year. We've been exploring alternative types of economics. We had Professor Steve Keene on on a recent episode where he was talking about how economics doesn't actually do a very good job of taking into account

things like energy costs. And we are going to be continuing that discussion today. So I'm very, very excited to introduce Brian Arthur. He's a economist who specializes in something called complexity economics, which automatically kind of sounds like something that we need to talk about in the current environment. He's a professor at the Santa Fe Institute and also a visiting researcher over at Park in Palo Alto. So Brian, thank you so much for coming. on. Thank you indeed. I'm really looking forward to this. So I guess my first question has to be, what is complexity economics? Yeah, I'll have to take a deep breath here. Economics has a lot of simplifying assumptions. I'm a theorist in economics, meaning I like to look at the economy

theoretically or formally. And we use a lot of mathematics. to do that. Standard economics, it's called neoclassical economics, brings in a lot of simplifying assumptions at particular questions. They generally, it's like clearing the clutter and just saying we want to get right down to the nitty gritty in any problem. And to do that, they, in standard economics, we assume that all the problems that the players in the economy face, that might be in very or banks or shipping companies, we assume that they're all facing well-defined problems, that they're all optimizing, meaning they're doing the best that any mathematician could. They're hyper-rational.

And the solutions they come up with are to be an equilibrium. So the pattern is at equilibrium. Nobody in these solutions has an incentive to change. Complexity economics, not so much deliberately, but we were motivated by the whole idea of looking at the economy much realistically. And so we might start by assuming that the banks or government departments or companies are different. They're not all identical. But once you assume they differ, then they don't. quite know that other companies, other companies obviously aren't the same as them. They don't

quite know what they might do, how much resources they have, what technologies are available to them. So generally speaking, when you don't know about other people or other players, you're subject to what economists call fundamental uncertainty. It's not just that you can't put probabilities and things, you simply don't know. So it turns out then you get yourself very quickly into a mess. The problems then that any individual agent is facing are not well defined. They're not subject to logic. You don't quite know what situation you're in. It's like quite often landing in a country. You might be a very skilled negotiator, but you land in some East Asian country. You've never been in. You don't know what procedures are, what the customers are, or how business operates

there. So the situation generally for agents to quite some degree, subject to uncertainty, and not well defined. You're in not so much a mathematical problem then. You're in an ill-defined situation. So in complexity economics, part of the economy we're looking at there in some situation. There are in some situation isn't well-defined, but they're not helpless. We act as human beings all the time in ill-divated situations. We meet someone, go out with them to figure out what they're like or to get to know people. Not sure what we're doing. So we assume that in the economy, agents are maybe adopting different ideas or hypothesis about situation they're in. They're exploring.

They're trying new things. Over time, they may repeat what they're trying. They'll learn whether something works. And in general, they drop things that don't work and they experiment things that might work. And in that way, they're kind of bootstrapping way up. So let me ask, so this idea of not assuming that everyone acts in the same rational manner, I have to say, is intuitively attractive to me because I feel like I encounter my fair share of irrational people on a daily basis, maybe because I spend too much time on social media. But I feel like naturally, if you look at the world, you can see that people aren't always acting with their best interest in mind, or at least there certainly,

not agreeing on what that best interest is compared to other actors or participants in a certain event or economic transaction. But everyone is sort of, so I guess what I'm getting at is in traditional economics, you assume everyone's rational, they're all sort of pulling together in the same way. But in something like complexity economics, if you assume people can be irrational or at least they look at things from different perspectives, they're looking at them from a variety of different perspectives, right? So how do you actually go about incorporating that into a model? You might have pretty good guidance from behavioral economics. You might have some good idea. You had a show on earlier that I very much liked about container shipping, and you can launch a

container ship somewhere maybe in Singapore is something and tried to bring it to Rotter Dam and then somehow things break down in the Suez Canal. You simply don't quite know when the canal is going to be cleared. You can't really rely on probabilities. You're not sure what people you're doing will do. And so you start to form ideas. But if everybody's doing this mutually, then the situation you're in being caused by many agents,

players together keep shifting. The stock market's like this. So it's not that there's a rational solution when a problem's ill-defined or not well-defined. There isn't a rational solution. There's only a rational solution if the problem's rational or logical. If you're in this sort of really hazy situation and I would maintain the norm, everybody's mutually exploring. So take the stock market, for example, in this form,

of economics. There isn't a rational solution. There might be if everyone was doing something perfect, then you might lose money. Of course, you can lose money, but you might lose money because you're doing something different from the smart people. But you really don't know what other people are doing. You might have a good idea. You might have some good guidance, but don't quite know what you're up against, what's going to happen. In this sort of economics, you're not assuming there is a well-defined mathematical, logical, rational solution. You're basically saying, we're all in this together and we're mutually trying to get smart. Let me give you a very quick example.

You might be, say, training multiple people to play chess or go. I'm thinking of the big breakthrough and AI where AlphaGo learned. how to play really, really smart games of Go. And they started off by assuming that there were other players that didn't know much. And they kept trying things and seeing what worked. And it wasn't as if there was a rational solution or if there was that might take many lifetimes of many, many, many universes to find. it was much more that you're playing against something you're trying to figure out what would work well.

In that situation, the only thing you can do is to look for good strategies. The reason we all this complexity is that other people are doing the same. And as their learning and they're shifting and they're trying to try out new ideas and strategies, then the problem you're in keeps changing. So the market changing as people get smarter, but as people get smarter, things are shifting and you have to shift what you're exploring. So we're backing off from the whole idea that this is a perfect solution. It's rather saying that we're trying to see what works, and that keeps shifting.

So something like the stock market, if you look at the stock market, so I guess the implication here is that you're never really reaching an optimum outcome in the stock market because you don't know what that outcome should actually look like. And so stocks are always moving. People are always trading because they're trying new things and they're sort of bouncing off of each other. So maybe, you know, one year people are really into value investing and then the next year it turns into momentum and everyone's sort of interacting with each other and the system, it's, self, the dominant way of acting in the system changes over time. Yeah, I'd say it's very much like that. But I want to stress something here and that is there isn't an optimal outcome if you don't

know. If you know that there's probability that copper futures or costs for container ships are going to do search and search versus something else, yeah, you can figure out something rational. But if you don't know, I don't know when there's going to be a big hold up in the Sewells Canal, I don't know what's fashionable and will come along. You don't know, therefore, there isn't some lurking in the back end behind some curtain, some optimal solution. You're trying to make your way in a situation that is created by other people who don't know trying to do their best in a situation. I'm Francine Lacquah, an award-winning journalist, and I've got a new podcast, Leaders with Francine Laquois from Bloomberg Podcasts. I've interviewed everyone from Heads of State to fashion icons about the news of the moment. But I've always been curious, who are these people as leaders?

I don't think there's one right way to be a leader. Make decisions. A poor decision is always better than no decision. Listen to new episodes every other Monday. Follow leaders with Francine Lacqua wherever you get your podcast. We talked a little bit about how traditional economics, neoclassical economics, is all about these simplifying assumptions and sort of framing the world into a model that you can then use to tell you something about it or, you know, about how people might act and what the effects might be.

It sounds like given the intricacies of complexity economics, the fact that you're, you know, not just dealing with these uncertain situations, but laser focused on them, and the fact that you're kind of looking into the feedback loops created by different sorts of behavior, it just sounds really, I'm trying to come up with a synonym for complex, but like it is all about the complexity. So I guess my question is, how difficult is it to do economics in this way? Well, I don't think that my group at Santa Fe was the first to think this way of economics. People could do this or think about this in the past. But in the 1980s, 1990s, computation came along.

So we could use of computers. To do standard mathematics, you know, writing down equations using algebra and calculus, you have to simplify an awful lot. And so I've no argument against what was done for about 150 years, 120 years at that time. Economists wrote down equations. They made very strong simplifying assumptions, and they got really good results. However, the economy has changed and we got computation. Let me give you an example, one that is probably easy to picture.

with complexity you're basically looking at elements anywhere over time forming patterns that those elements in turn are trying to react to the elements might cars in traffic say densely packed and along some freeway or something and the elements are creating something that we choose to call traffic the local cars around them and they're reacting to the traffic Now, there isn't an optimal strategy for any car. You don't know what the other cars are going to do, but you can watch what they're doing. And you can start to have simple rules. If this car gets too close, I'm going to change lanes. This was very hard to do with the standard mathematical setup, too many moving parts. So once computers came along, we could look at C. situations like that in real time, we could give the cars, maybe simple rules and say, well,

if the cars in front get closer, then I'm going to break slightly until situations restored. And so we're looking at how patterns like that move and change. It's a bit the same in the stock market or maybe in some complicated trading situation. You can't unload your freighters in, say, Cape Town. You have to put in there maybe to offload, but you find there's delays you didn't even think of. So everything's adjusting, readjusting, and you're trying to learn along the way what works, what doesn't work. We can study that by computers. It'd be almost impossible to set up fixed equations to try to study that.

So this sort of economics, I have beliefs or actions or strategies of driving my car, but other players have other drivers as well. And I'm trying to do my best in that, but there is no optimal thing to do something because you don't fully know what people are going to do. This sort of economics, complexity economics, is viewing the world the situation as a little of caution. I'm trying out actions or strategies in a world created by other people trying out actions and strategies, but I'm not sure what they're trying out or what they will think of next. So how does all that operate? So let me ask you a question about, I guess, second order effects, because I think it will help understand complexity economics.

But does this school of thinking lead to different policy? or different policy recommendations compared to neoclassical economics? And what would the difference be? Well, I'll give you two instances where it certainly does. We can talk about economies. We can talk about situations where people are trying to do their best, trying to learn what works, trying to explore an experiment mutually,

and that changes and recreates a situation. And to do that, I said, you can track that if you're brilliant maybe and remember everything, but normally we use computers to track what happening. And this allows us, once we use computers and we're not using equations, what we can do is set up models in our computers. So this immediately allows us to use more details. Your readers are probably, sorry, your listeners are probably familiar with the whole idea that there are very good mathematical models, say, for epidemics and pandemics. But they're pretty simple because all mathematics has to be kept simple if you want to turn the crank and get solutions.

At the start of the pandemic about a year ago, or in the early days, we assumed that people were either infected or they were not infected. We assume that they interacted certain rates and infect each other. It turned out that detail didn't give you enough realism. And you could start then once you look at things by computer, you can have much more detailed models. And say, oh, well, people in retirement homes are not out partying every night, but on their 20s might be. And so the dynamics might be very different. There might be networks of interaction. Once we agreed to be real, is what if we gave vaccines to older people? Well, obviously, deaths will go down because they're most at risk. But what do we give vaccines? to younger people, because they may be the ones that are doing most of the disease transmission late at night in out at parties and bars. We can use the computer as what we call a policy lab.

We can assume realistically that the agents and the economy, what we're looking at in this case, COVID, that agents differ. They've different ages. maybe they have their own networks that we can describe and we can get much better detail. Let me give you one other thing that might interest your listeners. A very different example. If you're doing standard economics,

you want to write down a few simple equations, not very many, otherwise it gets too unwieldy logically. And very often, as I said, we assume everybody's the same. So maybe the year is 1990 and there's a possibility of having things, computers, televisions and so on manufactured abroad, possibly in China, somewhere like that. And you want to see if that's a good trade policy. Standard trade theory would say, oh, yeah, if the Chinese can do that. more cheaply, we should have that, and then we can sell them soybeans to make the revenue so we can pay them and so on. But when we did that sort of modeling around 1990, just to keep the whole

problem simple, we tended to assume that everybody in the US was the same. What we didn't do and what you can do now if you're willing to use computation is to say, well, maybe the agents different. Maybe there's farmers in the middle of America. Maybe there's manufacturing sectors, say, in what we're now called the Rust Belt. If all these jobs go to China, it doesn't affect the United States hominously, but we may be differentially put out of jobs. Everybody in California might be much better of.

Everybody in Ohio, maybe not because they've lost their manufacturing jobs. So what we're finding said if we had had better models around 1990, mathematical models or economic models, we've been so quick to say, oh, yep, let's shift all the textiles and electronics off to Asia. And they have cheaper labor, so everybody in the U.S. will be better off. That wasn't what happened. People in the middle of the country were put out of work. And then if you had detailed models, you might have seen if they were put out of work. You might have massive social disruptions.

You might have quite turbulent politics, which happened. You might have opioid crises, suicides, all of which showed up. regionally, but weren't taken account of in the models. I think the policy is very important. So if you permit, let me give you one other example. Yeah, go for it. This is fascinating. Okay. There's another thing. I want to iterate that standard economics, I think, does an excellent job. I'm trained as a neoclassical economist, and I think it does very good job. But what standard economics does as it simplifies. We'll assume everybody's the same.

We'll assume that the outcome will be in equilibrium, meaning that there's no player, no agent involved, no bank that would want to do anything different because all the incentives are in balance. And I think that standard economics is really, really good at allowing us to understand the economy, not just to control or manipulate what happens politically or economically, but to really understand things. And this is a huge, but standard economics hasn't been able to deal with crises, financial collapses like in 2008, energy market

disruptions like what happened in the year 2000 in the electricity market here in California, why don't we see these crises and collapses with standard economics? Of course, a few economists do, but if we're doing standard theory, you don't see financial collapses coming, say, like in 2008, with a subprime lending market. Why? The reason is subtle. The thing is an equilibrium. Nobody has any incentive to do anything different.

If you make that assumption, all your models will bear that out. Therefore, nobody can come up with some new strategy. They think of to manipulate the system or to rig the system in their own favor, precisely what we saw happen in 2008. So we're assuming that the economy, go back to thinking about the stock market, is an open system that people are discovering new things all the time. They're discovering new ways possibly to manipulate the system in their favor. Under complexity economics, you're not assuming the outcome is in equilibrium and nobody has an incentive to do anything different.

The way I summarize complexity economics is it's viewing the economy as an evolving system. The economy is like an ecology. Every so often, new players discover new strategies, just like in a real ecology, new species arise. and things that looked to be in equilibrium before are suddenly thrown out of equilibrium. It's like introducing the new fish into Lake Victoria, suddenly that really upsets the system and everything has to adjust.

By assuming that we're not necessarily in equilibrium, by assuming that problems are not necessarily well-defined, We're basically looking at the economy ecologically. We can see all kinds of phenomena arising. You can get the news whenever you want it with Bloomberg News Now. I'm Amy Morris. And I'm Karen Moscow here to tell you about our new on-demand news report delivered right to your podcast feed. Bloomberg News Now is a short five-minute audio report on the day's top stories.

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and analysts we're all over the world. Listen to the latest from Bloomberg News Now on Apple, Spotify, or anywhere you listen. So it sounds almost like complexity economics is about rather than displacing other schools of economics or neoclassical economics, it's more about sort of augmenting or complementing the methodology and making the models more robust and more sophisticated. in being able to take into account changing situations? Yeah, I would say that, but I wouldn't say it's an add-on to standard economics, because standard economics does have standard assumptions.

All problems are well-defined, everything happens at an equilibrium, and so on. It's more like every so often in mathematics or physics, you throw out the basic axioms In mathematics, you could throw out the parallel axiom, or you could fifth axiom and say, what would mathematics look like if you didn't have that axiom and you'd get a very different sort of mathematics. So what we're really doing is trying to move towards realism, saying, okay, people are not just muddling along. They might be doing quite sophisticated things, but everybody's doing. that and as they do that, the patterns are forming differently. So I would not say this is an addition to the standard approach. I think of a subtle up that says some problems in the economy, in fact, very many are perfectly well looked at with equilibrium economics. It might be that

markets, for example, from day to day, the price is roughly at equilibrium, markets clear, sort of economics is perfectly appropriate. So it's more like saying that if the wind doesn't blow, the sea will go flat. That doesn't contradict anything. But there are different sorts of questions. If the economy is just talking about how quantities are produced and production and consumption and patterns of that, the standard approach I think is pretty good. I've no quarrel with it. However, if you're asking, how do new products come along? How do new strategies develop? I'm sitting here in Silicon Valley. And there's no such thing really in the tech business as equilibrium, We're exploring here how machine learning and AI work, how types of biotech and proteomics work, and that's changing month by month.

So how do you maneuver and how does the economy work if the incentives are changing around you and you have to readjust and do things differently? It's a bit like surfing. Years ago, I used to, believe it or not, I used to surf in Hawaii. It's not that there's an optimal approach to a wave. If there is, I haven't seen it yet, because you don't know where the wave is going to go next. So what you're doing is adjusting your balance, adjusting your direction, you're looking ahead, maybe 20 feet, maybe 50 feet, or 100 feet and you're trying to say, be better to be over there, you want to avoid the white water and stay in green water and so on. So this is really an economics where you're

looking at adjustment, things forming. And you start to ask questions like, where do labor unions come from? Where do insurance companies come from historically? Where do legal arrangements come from. You can begin to see we're in that murky, uncertain world. We don't know, for example, what sort of legal arrangements there should be for large platform companies. So it's not as if there's an optimal strategy out there. We will get somewhere that might take two or three decades. We're trying this, we're trying that, we're thinking about this, learning that. So, complacent. Complexity economics applies to these situations where things are forming, where you've never seen them. So it's not that this is an add-on to do more detail to standard economics.

Complexity economics isn't an add-on to standard economics. It's the type of economics that would be appropriate if new things are coming along, but you don't know what they are. if people are strategizing in different ways, but you simply don't know what that's going to bring. It's a bit like you could say standard economics would be a bit like fighting standard battles, say in the 1600s, you set up your army, the Duke of Marlborough sets up his, you pretty much to know how the rules are, you let things interact and so on. Now you're in a situation where you don't know who you're up against. You don't quite know what the rules are.

You don't know how things will work out. You don't know what new weapons will be used. And so you're backing and filling all the time. How do you make an economic side of that? So just on that note, let me ask you a question. And I'm really hoping that you were able to talk about this. Just on this idea that complexity economics is better equipped to deal with new things, new situations, new behaviors that might evolve, what does complexity economics say about

our current situation? So we just had a global pandemic, very different to previous global pandemics. We had a policy response from governments, at least in the U.S., that was very, very different to previous policy responses, and we basically had an economic crisis that was quite unusual compared to other ones. So what does complexity economics tell us about the current situation? To my mind, complexity economics is not set up to tell us exactly what we should do in any new situation like that or any new crisis. It's set up as a way to understand how players, mutually adjust to a situation they're co-creating that they don't really understand.

So it's a way to look at the economy. It's a way to understand something. And I think that the COVID crisis is a pretty good illustration of this at work. In March 2020, there was an awful a lot of fear about COVID, and I think rightly so, because we didn't know how it worked. We didn't know how the infections would play out. We didn't know how bad it would be. So we were kind of groping in the dark. Complexity economics tries to look at situations like that. It may not say there's an optimal strategy, but we did come up with coping strategies. We tested, we tried, we We thought that face masks weren't a good idea because it would deprive hospitals, and we thought face masks were a great idea, and so on. We coped.

We were in the middle of a situation that people were creating, as well as being part of people, human beings, co-created the pandemic by interacting and infecting each other, not deliberately. of course. And we were trying, everybody was trying mutually to cope with this. And then finally, by December or so last year, we thought we figured things out, but then along came vaccination. And that brought a whole new set of things to understand. What if there weren't enough vaccines, what if we hadn't made the right arrangements like the EU hadn't really in the early days? What of the vaccinations didn't work? What if you couldn't get hold of vaccines, say, like in South Africa or somewhere like that? And so we were learning and everybody was mutually adjusting and readjusting.

The economy isn't one thing or the other. It's not a well-oiled, perfect equilibrium machine where everything's ticking. And to the degree it's that standard economic supplies. The economy isn't always exploring new territory at the other extreme, and we're learning and trying to adapt and co-adapt. Somewhere in between. So I would say that when you're replicating strategy or when you're replicating the same situation from day to day, you could talk about optimizing well-defined problems, optimal behavior,

standard economics. when the situation is being created by the strategies you adopt, should we test for COVID, should we separate people, should we have social distancing, then you're in a different situation. So complexity economics is really about people co-adapting,

co-learning, and figuring out what works. I'd like to make two points about industry because I think your show has a lot to do with industry, if I may. Yeah, we've certainly been talking about it a lot this year, again, because I think one of the unexpected things from COVID was the degree to which it would cause these supply disruptions, not necessarily because borders were shut, although that was part of it, but just because we had this really unexpected import boom over in the States with everyone. at home and ordering a bunch of goods. Yeah. So please. So complexity economics is a way of looking at the economy.

How do you look at industry this way? What I would say is if industry is fairly constant, so we're turning out steel the years 1950, and one year, one month is roughly not too different from the previous one standard economics would apply. You optimize inputs and outputs and you optimize the technology and prices come into equilibrium. But we're not as much in that situation. So the years now say 2021. And many industries, and I think this is very much illustrated in your show here, industries may try to optimize, say, okay, I have this fleet of oil tankers, and I can sit in some

office, say, in wherever Hong Kong or London or summer, New York and optimize their schedule. Things are interactive, they're complicated, and there's new, unexpected, uncertain, uncertain, events coming along the time the Suez Canal closes. You're held up at some port unexpectedly and so on. So optimization may not be quite appropriate. The strategies that are more appropriate are not just pure optimization. They have to take a kind of resilience. How do I recover?

How do I cope with the unexpected? said I'm running the entire US or California electricity grid. My strategy should not be how to optimize that. There may be forest fires, there may be disruptions. There may be more energy available than I expect. My strategy and industry isn't just optimizing. It is how do I cope with these unexpected, unthought of things? What if there's players who come out?

up with something nobody's ever thought of. You can't optimize you're in an unknown situation or it's partially unknown. What you can do, though, is put things like this, make computer models. It's a bit like the COVID strategy a year ago. What if this gets out of control? What if that happens? What if we don't get a vaccine for three years and so on? And you can test all those strategies. The industry itself is moving from just simply being repetitive, which you can optimize in, to being resilient where you're looking for reactions. So complexity economics is basically saying, how do firms react to unexpected things in a reasonably smart way and what would industry look like if that were the norm? And we're rapidly.

shifting from what I call a standard productive economy. There's inputs, there's factories, there are outputs, there are human beings in the middle, there's planners, there's workers. And we're shifting more and more into what I call an autonomous economy. The trading system used in finances, largely autonomous, supply chains. are partially autonomous, as you would know. Autonomous means that they're self-regulating. There isn't a human being planning each step. We're moving towards getting air traffic control systems that will be autonomous.

That's already being pointed out. Usage of things like blockchain, Bitcoin, that type of trading. finance is partially autonomous. And in cases like that, I wouldn't say elements are acting on their own. We're moving into an economy or an industrial setup. If things are autonomous, where the elements and that setup are in conversation with other elements. So if you're using blockchain or if you're using some of these new financial arrangements that you've talked about in your show, different players, different contracts may be automatically or autonomously in conversation with other elements. That is exactly the whole domain of complexity. Again, go back to

cars and traffic. Maybe I... I have a driverless traffic system in 10 years time. I'm in, say, Los Angeles. But I'm not driving in a car, sitting in a car that's driverless. I'm sitting in a car that's in a driverless convoy of driverless cars. And each car is in conversation with cars around it. So what complexity economics is trying to do is to say, what if you have a sort of autonomous or semi-autonomous system where the elements on that economy,

there might be trading strategies, trading systems, there may be cars and driverless convoys, there may be different electricity generating systems where these are in conversations with other systems. How can we understand something like that? how can we control it, what policies would be necessary. That's very different from saying we have a 19th century economy. So again, I would say we're moving fairly fast into a system where the players, the elements, the cars, the planes that are landing in an autonomous air traffic control situation, are automatically in. conversation with other like elements. How does that work? How can we think of it? How should we control it? We can't optimize it because you never quite know what's going to happen next,

but you can certainly make it resilient. I do like that complexity economics is making an attempt to understand the real world economy as it kind of matures and becomes technologically more advanced. And it's true, if you look over classical economics, the literature there, so much of it just seems so old-fashioned nowadays, not in the sense necessarily of what it's actually saying about economics, but just the idea of, you know, this country makes wine and this country makes cloth, and they're going to trade with each other, you know, never mind changes in transport technology or manufacturing technology and things like that. Anyway, Professor Brian Arthur, we're going to have to leave it there, but it has been an absolutely fascinating conversation, and I really appreciate

you taking the time to walk us through complexity economics. Thank you so much, and I'm delighted to have been with you. Thank you, indeed. Thank you so much. Well, Joe isn't here to bounce off of, so I'll just go ahead and give you, I guess, my two big takeaways from this. Number one, I think it's fairly clear that there are shortcomings in traditional economics. And you can argue that they're there for a reason that you have to have simplifying models. You have to have simplifying assumptions about how the world works in order to, you know, make policy recommendations and make them relatively quickly. But I also think that given the advancement in technology, given the advancement in computer

systems that the professor was describing, it does seem natural that we now have the capacity to model the real world on an altogether different and much more advanced scale. And I think it's interesting to see a school of economics that's really trying to take that technology and use it to contribute to the literature in an interesting way. And then the second thing that sort of stood out for me is related to that point, but just the notion that in the 1990s, when we were talking about free trade and outsourcing this idea that we didn't have the computational power to actually model what that would mean for every single segment of society in every different country, we could only look at it sort of from a broad bird's eye level view going, well,

obviously, we want cheaper goods and obviously they want cheaper goods. And so we're just going to trade with each other and everyone's going to be happy. And 20 or 30 years later, it's very, very clear that that wasn't necessarily what happened. The outcome was rather different. And it's extremely interesting to think, what if you had had something like complexity economics, what if you had had more advanced, more sophisticated models, what sort of policy decisions would you have made? So I guess I'll leave it there.

This has been another episode of the Oddlots podcast. I'm Tracy Allaway. You can follow me on Twitter at Tracy Allaway. You can follow Joe at The Stowart. You can follow our producer, Laura Carlson at Laura M. Carlson. And you can follow the head of Bloomberg podcast, Francesca Levy, at Francesca Today. Thanks for listening. This is Tom Keane inviting you to join us for the Bloomberg Surveillance Podcast. It's about making you smarter every business day. I'm Paul Sweeney. We bring you complete coverage of the U.S. market open. We cover stocks, bonds, commodities, even crypto, all the information you need to excel. And I'm Alexis Christophress. Bloomberg surveillance also brings you the analysis behind the headlines.

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