Speaker 1: Hello, and welcome to another episode of the Odd Lots podcast.
Speaker 1: My co host Joe Wisenthal is away today, so it's
Speaker 1: just going to be me for a bit. And one
Speaker 1: of the discussions we've been having on Odd Lots over
Speaker 1: the past year or so is this idea that traditional
Speaker 1: economics doesn't actually do a great job of taking into
Speaker 1: account a lot of important parts of the world economy
Speaker 1: right now. And what I mean by that is, if
Speaker 1: you look at something like the supply chain issues we've
Speaker 1: been having in the market. We've been talking a lot
Speaker 1: about the shortage in semiconductors, or if you look at
Speaker 1: things like transportation gridlock, We've spoken tons about the chaos
Speaker 1: and container shipping this year, a lot of those sort
Speaker 1: of factors or complexities don't actually make it into traditional economics. So,
Speaker 1: for instance, one of the things I learned this year
Speaker 1: was that the sort of classical definition of comparative advantage,
Speaker 1: where you know, one nation is very good at making
Speaker 1: guns and the other nation is very good at making
Speaker 1: butter or wine versus cloth, something like that, and so
Speaker 1: they're supposed to trade with each other, that classical model
Speaker 1: doesn't actually take into account transport costs or at least
Speaker 1: it didn't when it was first proposed. So if economics
Speaker 1: is all about simplifying assumptions in order to make it useful,
Speaker 1: you kind of have to ask about the trade off
Speaker 1: between simplifying everything and then overlooking quite important things that
Speaker 1: are impacting the economy. So it's something that both Joe
Speaker 1: and I have been thinking about this year. We've been
Speaker 1: exploring alternative types of economics. We had Professor Steve Keene
Speaker 1: on on a recent episode where he was talking about
Speaker 1: how economics doesn't actually do a very good job of
Speaker 1: taking into account things like energy costs, and we are
Speaker 1: going to be continuing that discussion today. So I'm very,
Speaker 1: very excited to introduce Brian Arthur. He's a economist who
Speaker 1: specializes in something called complexity economics, which automatically kind of
Speaker 1: sounds like something that we need to talk about in
Speaker 1: the current environment. He's a professor at the Santa Fe
Speaker 1: Institute and also a visiting researcher over at Park in
Speaker 1: Palo Alto. So, Brian, thank you so much for coming on.
Speaker 1: Thank you. Indeed, I'm really looking forward to this. So
Speaker 1: I guess my first question has to be what is
Speaker 1: complexity economics? Yeah, I love to take a deep breath here.
Speaker 1: Economics has a lot of simplifying assumptions. I'm a theorist
Speaker 1: and economics meaning I like to look at the economy
Speaker 1: theoretically or formally, and we use a lot of mathematics
Speaker 1: to do that. Sta underd economics. It's called neoclassical economics
Speaker 1: bring this in a lot of simplifying assumptions that particular questions.
Speaker 1: They generally it's like clearing the clutter and just saying
Speaker 1: we want to get right down to the nitty gritty
Speaker 1: in any problem. And to do that they in the
Speaker 1: standard economics, we assume that all the problems that the
Speaker 1: players in the economy face there might be investors, or
Speaker 1: banks or our shipping companies. We assume that they're all
Speaker 1: facing well defined problems, that they're all optimizing, meaning they're
Speaker 1: doing the best that any mathematician could. They're hyper rational,
Speaker 1: and the solutions they come up with are to be
Speaker 1: an equilibrium. So the pattern is an equilibrium. Nobody in
Speaker 1: these solutions has to change complexity economics not so much deliberately,
Speaker 1: but we were motivated by the whole idea of looking
Speaker 1: at the economy much realistically, and so we might start
Speaker 1: by assuming that the banks, or government departments or companies
Speaker 1: are different. They're not all identical, but once you assume
Speaker 1: they differ, then they don't quite know that other companies.
Speaker 1: Other companies obviously aren't the same as them. They don't
Speaker 1: quite know what they might do, how much resources they have,
Speaker 1: what technologies are available to them. So, generally speaking, when
Speaker 1: you don't know about other people or other players, you're
Speaker 1: subject to what economs called fundamental uncertainty. It's not just
Speaker 1: that you can't put probabilities and things you simply don't know.
Speaker 1: So it turns out then you get yourself very quickly
Speaker 1: to mess The problems then that any individual agents is
Speaker 1: facing are not well defined. They're not subject to logic.
Speaker 1: You don't quite know what situation you're in. It's like
Speaker 1: quite often landing in a country. You might be a
Speaker 1: very skilled negotiator, but you land in some East Asian
Speaker 1: country you've never been in. You don't know how pro
Speaker 1: seizures are, what customers or or how business operates there.
Speaker 1: So the situation generally for agents to quite some degree
Speaker 1: object to uncertainty and not well defined you're in not
Speaker 1: so much a mathematical problem then you're in an ill
Speaker 1: defined situation. So in complexy economics, part of the economy
Speaker 1: we're looking they're in some situation isn't well defined, but
Speaker 1: they're not helpless. We act as human beings all the time,
Speaker 1: and it will the situations we meet someone who out
Speaker 1: with them to figure out what they're like or to
Speaker 1: get to know people. I'm not sure what we're doing,
Speaker 1: but so we assume that in the economy, agents are
Speaker 1: maybe adopting different ideas or hypothesis, but situation they're in,
Speaker 1: they're exploring, they're trying new things. Over time, they may
Speaker 1: repeat what they're trying to learn whether something works, and
Speaker 1: in general they drop things that don't work and the
Speaker 1: experiment things that might work, and in that way they're
Speaker 1: kind of bootstrapping way up. M hm. So let me ask, So,
Speaker 1: this idea of not assuming that everyone acts in the
Speaker 1: same rational manner, I have to say, is intuitively attractive
Speaker 1: to me because I feel like I encounter my fair
Speaker 1: share of irrational people on a daily basis, Maybe because
Speaker 1: I and too much time on social media, but I
Speaker 1: feel like naturally, if you look at the world, you
Speaker 1: can see that people aren't always acting with their best
Speaker 1: interest in mind, or at least they're certainly not agreeing
Speaker 1: on what that best interest is um compared to other
Speaker 1: actors or participants in a certain event or economic transaction.
Speaker 1: But everyone is sort of So I guess what I'm
Speaker 1: getting at is, in traditional economics, you assume everyone's rational,
Speaker 1: they're all sort of pulling together in the same way.
Speaker 1: But in something like complexity economics, if you assume people
Speaker 1: can be irrational, or at least they look at things
Speaker 1: from different perspectives, they're looking at them from a variety
Speaker 1: of different perspectives. Right, So how do you actually go
Speaker 1: about incorporating that into a model. You might have pretty
Speaker 1: good guidance from behavioral economics. You might have some good
Speaker 1: idea you had to show on earlier that I very
Speaker 1: much like about container shipping, And you can launch a
Speaker 1: container ship summer, maybe in Singapore's something, try to bring
Speaker 1: it to Rotterdam, and then somehow things break down in
Speaker 1: a so as canal. You simply don't quite know when
Speaker 1: the canal is going to be cleared. You can't really
Speaker 1: rely on probabilities. You're not sure what people you're doing
Speaker 1: will do, and so you start to form ideas. But
Speaker 1: if everybody is doing this mutually, then the situation you're
Speaker 1: in being caused by many agents payers together keep shifting
Speaker 1: the stock markets like this. So it's not that there's
Speaker 1: a national solution. When a problem is ill defined or
Speaker 1: not well defined, there is no rational station. There's only
Speaker 1: a rational solution if the problems rational or logical in
Speaker 1: this sort of really hazy situation, and I would maintain
Speaker 1: the norm everybody's mutually exploring. So take the stock market
Speaker 1: for example. In this form of economics, there isn't a
Speaker 1: rational solution. There might be if everyone was doing something perfect,
Speaker 1: then you might lose money. Of course you're gonna only
Speaker 1: but you might lose money because you're doing something different
Speaker 1: from the smart people. But usually don't know what other
Speaker 1: people are doing. You might have a good idea, you
Speaker 1: might have some good guidance, but I don't quite know
Speaker 1: what you're up against. What's going to happen. In this
Speaker 1: sort of economics, you're not assuming there is a well defined, mathematical, logical,
Speaker 1: rational solution. You're basically saying, we're all in this together
Speaker 1: and we're mutually trying to get smart. Let me give
Speaker 1: you a very quick example. You might be say training
Speaker 1: multiple people to play chess or go. I'm thinking of
Speaker 1: the big breakthrough in AI where Alpha Go learned how
Speaker 1: to how to play really really smart games of Go.
Speaker 1: And they started off by assuming that there are other
Speaker 1: players that didn't know much, and they kept trying things
Speaker 1: and seeing what worked. And it wasn't as if there
Speaker 1: was a rational solution, or if there was that might
Speaker 1: take many lifetimes of many, many, many universes to find.
Speaker 1: It was much more that you're playing against something, You're
Speaker 1: trying to figure out what would work well in that situation.
Speaker 1: You're the only thing it can do is to look
Speaker 1: for good strategies. The reason we all as complexity as
Speaker 1: that other people are doing the same and as they're
Speaker 1: learning and they're shifting and they're trying to try out
Speaker 1: new ideas and strategies, then the problem you're in keeps changing.
Speaker 1: So the market changing as people get smarter, but as
Speaker 1: people get smarter, things are shifting and you have to
Speaker 1: shift what you're exploring. So we're backing off from the
Speaker 1: whole idea there's a perfect solution. It's rather saying that
Speaker 1: we're trying to see what works and that keeps shifting.
Speaker 1: So something like the stock market um, if you look
Speaker 1: at the stock market. So I guess the implication here
Speaker 1: is that you're never really reaching an optimum outcome in
Speaker 1: the stock market because you don't know what that outcome
Speaker 1: should actually look like. And so stocks are always moving,
Speaker 1: people are always trading because they're trying new things, and
Speaker 1: they're sort of bouncing off of each other. So maybe
Speaker 1: you know, one year people are really into val you investing,
Speaker 1: and then the next year it turns into momentum and
Speaker 1: everyone's sort of um interacting with each other and the
Speaker 1: system itself um the dominant way of acting in the
Speaker 1: system changes over time. Yeah, I'd say it's very much
Speaker 1: like that. But I want to stress something here, and
Speaker 1: that is there isn't an optimal outcome if you don't know.
Speaker 1: If you know that there's probability that copper futures or
Speaker 1: our costs for containerships are going to do search and
Speaker 1: search for something else. Yeah, you can figure out something rational,
Speaker 1: but if you don't know, I don't know when there's
Speaker 1: going to be a big hold up in the source canal.
Speaker 1: I don't know what's fashionable and will come along. You
Speaker 1: don't know there fur there isn't some lurking in the
Speaker 1: back nd behind some curtains, some optimal solution. You're trying
Speaker 1: to make your way in a situation that is create
Speaker 1: it by other people who don't know trying to do
Speaker 1: their best in the situation. We talked a little bit
Speaker 1: about how traditional economics, neoclassical economics is all about these
Speaker 1: simplifying assumptions and sort of framing the world into a
Speaker 1: model that you can then use to tell you something
Speaker 1: about it, or you know, about how people might act
Speaker 1: and what the effects might be. It sounds like, given
Speaker 1: the intricacies of complexity economics, um the fact that you're,
Speaker 1: you know, not just dealing with these uncertain situations but
Speaker 1: laser focused on them, and the fact that you're kind
Speaker 1: of looking into the feedback loops created by different sorts
Speaker 1: of behavior. If you're it just sounds really um, I'm
Speaker 1: trying to come up with a synonym for complex but like,
Speaker 1: it is all about the complexity. So I guess my
Speaker 1: question is how difficult is it to do economics in
Speaker 1: this way? Well, I don't think that my group at
Speaker 1: Santa Fe was first to think this way of economics.
Speaker 1: People could do this or think about this in the
Speaker 1: past but in the nine eighties nine nineties, computation came along,
Speaker 1: so we could of computers to do standard mathematics, you know,
Speaker 1: writing down equations using algebra and calculus. You have to
Speaker 1: simplify an awful lot, and so I've no argument against
Speaker 1: what was done for about a hundred and fifty years,
Speaker 1: hundred and twenty years. At that time, economists wrote down equations,
Speaker 1: they made very strong simplifying assumptions, and they've got really
Speaker 1: good results. However, the enarmy has changed and we got computation.
Speaker 1: Let me give you an example one that's it is
Speaker 1: probably easy to picture with complexity. You're basically looking at
Speaker 1: elements anywhere over time, forming patterns that those elements, in
Speaker 1: turn are trying to react to. The elements might parse
Speaker 1: in traffics densely packed in along some freeway or something,
Speaker 1: and the elements are creating something that we choose to
Speaker 1: call traffic the local cars around them, and they're reacting
Speaker 1: to the traffic. Now, there isn't an optimal strategy for
Speaker 1: any car. You don't know what the other cars you're
Speaker 1: gonna do, but can watch what they're doing, and you
Speaker 1: can start to have simple roots. If this car gets
Speaker 1: too close, I'm going to change lanes. This was very
Speaker 1: hard to do with the standard mathematical set up, too
Speaker 1: many moving parts. So once compute came along, we could
Speaker 1: look at situations like that in real time. We could
Speaker 1: give the cars maybe simple rules and say, well, if
Speaker 1: the cars in front get closer, then I'm going to
Speaker 1: break slightly until the situations restored. And so we're looking
Speaker 1: at how patterns like that move and change. It's a
Speaker 1: bit the same in the stock market or maybe in
Speaker 1: some complicated trading situation. You can't unload your freighters in
Speaker 1: say Cape Town. You have to put in there maybe
Speaker 1: to offload, but to find there's delays you didn't even
Speaker 1: think of. So everything's adjusting, readjusting, and you're trying to
Speaker 1: learn along the way what works, what doesn't work. We
Speaker 1: can study that by computers, be almost impossible to set
Speaker 1: up fixed equations to try to study that. So this
Speaker 1: sort of economics. I have beliefs, our actions or strategies
Speaker 1: driving my car, but other players have other drivers as well,
Speaker 1: and I'm trying to do my best in that. But
Speaker 1: there is no optimizing to do some because you don't
Speaker 1: fully know that people are going to do this sort
Speaker 1: of economics. Complexity economics is viewing the world. The situation
Speaker 1: has a little of I'm trying out actions or strategies
Speaker 1: in a world created by other people trying out actions
Speaker 1: and strategies, but I'm not sure what they're trying out
Speaker 1: or what they will think of next. So how does
Speaker 1: all that operate? So let me ask you a question
Speaker 1: about UM. I guess second order effects because I think
Speaker 1: it will help understand complexity economics. But does the school
Speaker 1: of thinking lead to different policies or different policy recommendations
Speaker 1: compared to neo classical economics and what would what would
Speaker 1: the difference be? Well, I'll give you two instances where
Speaker 1: it certainly does. We can talk about economies. We can
Speaker 1: talk about situations where people are trying to do their best,
Speaker 1: trying to learn what works, trying to explore an experiment mutually,
Speaker 1: and that changes and recreates a situation. And to do that,
Speaker 1: I said, you have to contract that if you're brilliant
Speaker 1: maybe and remember everything. But normally we use computers to
Speaker 1: track quote happening, and this allows us. Once we use
Speaker 1: computers and we're not using equations us, what we can
Speaker 1: do is set up models in our computers, so this
Speaker 1: immediately allows us to use more details. Your readers are
Speaker 1: probably are sorry. Your listeners are probably familiar with the
Speaker 1: whole idea that there are very good mathematical models safe
Speaker 1: epidemics and pandemics, but they're pretty simple, because all mathematics
Speaker 1: has to be kept simple if you want to turn
Speaker 1: the crank and get solutions. At the start of the
Speaker 1: pandemic about a year ago or in the early days,
Speaker 1: we assumed that people were either affected or they were
Speaker 1: not infected. We assume that they interacted certain rates and
Speaker 1: infect each other. Turned out that detail didn't give you
Speaker 1: enough realism and you could start. Then once you look
Speaker 1: at things by computer, you kind of much more detailed
Speaker 1: models and say, oh, well, people in retirement homes are
Speaker 1: not partying every night, but in their twenties might be,
Speaker 1: and so the dynamics might be very different. There might
Speaker 1: be networks of interaction. Once we agree to be realistic,
Speaker 1: what do we gave vaccines to older people? Well, obviously
Speaker 1: deaths will go down because they're most at risk, But
Speaker 1: what do we gave vaccines to younger people because they
Speaker 1: may be the ones that are doing most of the
Speaker 1: these transmission late at night in out at parties and bars.
Speaker 1: We can use the computer as what we call a
Speaker 1: policy lab. We can do some realistically that the agents
Speaker 1: in the economy. What we're looking at in this case,
Speaker 1: say COVID that agents differ their different ages. Maybe they
Speaker 1: have their own networks that we can describe and we
Speaker 1: can get much better detail. Let me give you one other,
Speaker 1: one other thing that might interest your listeners, very different example,
Speaker 1: if you're doing standard economics, you want to write down
Speaker 1: a few simple equations, not very many, otherwise it gets
Speaker 1: too unwieldy logically, and very often, as I said, we
Speaker 1: as soon everybody's the same. So maybe the year is nine,
Speaker 1: and there's a possibility of having things computers, television's and
Speaker 1: so on manufactured abroad, possibly in China somewhere like that,
Speaker 1: and you want to see if that's that's a good
Speaker 1: trade policy. Standard trade theory would say, oh, yeah, have
Speaker 1: the Chinese can do that more cheaply, We should have that,
Speaker 1: and then we can sell them. Soy beans two make
Speaker 1: the revenue, so we conmpare them. So on. But when
Speaker 1: we did that sort of modeling around, just to keep
Speaker 1: the whole problem simple, we tended to assume that everybody
Speaker 1: in the US was the same. What we didn't do,
Speaker 1: and what you can do now if you're willse computation,
Speaker 1: is to say, well, maybe the agents different. Maybe there's
Speaker 1: farmers in the middle of America, maybe there's manufacturing sectors
Speaker 1: saying the what now called the rust belt. If all
Speaker 1: these jobs go to China, it doesn't affect the United
Speaker 1: States homelessly, but we may be differentially put out of jobs.
Speaker 1: Everybody in California might be much better off. Everybody in Hawaii,
Speaker 1: in Ohio maybe art because they've lost their manufacturing jobs.
Speaker 1: So what we're finding said, if we had had better
Speaker 1: models around, mathematical models or economic models, we're been so
Speaker 1: quick to say, oh yeah, let's shift all the textiles
Speaker 1: and the electronics off to Asia, and they're cheaper labor,
Speaker 1: so everybody in the US will be better off. That
Speaker 1: wasn't what happened. People in the middle of the country
Speaker 1: were put out of work. And then if you had
Speaker 1: detailed models, you might have seen if they were put
Speaker 1: out of work, you might have massive social disruptions. You
Speaker 1: might have quite turbulent politics, which happened. You might have
Speaker 1: opioid crises, suicides, all of which showed up regionally, but
Speaker 1: weren't taken account of in the models. I think the
Speaker 1: policy is very important. If you permit, let me give
Speaker 1: you one other example. Yeah, go for it. This is fascinating. Okay,
Speaker 1: there's another thing I want to iterate that standard economics
Speaker 1: I think does an excellent job. I'm trained as a
Speaker 1: neoclassical economist, and I think it does a very good job.
Speaker 1: But what standard economics stores is it simplifies will, so
Speaker 1: everybody is the same will as soon that the outcome
Speaker 1: will be an equilibrium, meaning that there's no player, no
Speaker 1: agent involved, no bank that would want to do anything
Speaker 1: different because all the incentives are in balance. And I
Speaker 1: think that standard economics is really really good at allowing
Speaker 1: us to understand the economy, not just to control and
Speaker 1: manipulate what happens politically or economically, but to really understand things.
Speaker 1: But and this is a huge but standard economics hasn't
Speaker 1: been able to deal with crises, financial collapses like in
Speaker 1: two thousand and eight, energy market disruptions like what happened
Speaker 1: in the two thousands in the electricity market here in California.
Speaker 1: Why don't we see these crises and collapses with the
Speaker 1: standard economics of course a few economists do. But if
Speaker 1: we're doing standard theory, you don't see financial collapse the coming, say,
Speaker 1: like in two thousand and eight with the subprime lending market.
Speaker 1: Why the reason is subtle thing is an equilibrium. Nobody
Speaker 1: has any incentive to do anything different. If you make
Speaker 1: that assumption, all your models will bear that out there,
Speaker 1: for nobody can come up with some new strategy they
Speaker 1: think of to manipulate the system, or to rig the
Speaker 1: system in their own favor, precisely what we saw happen
Speaker 1: in two thousand and eight. So we're assuming that the economy,
Speaker 1: go back to thinking about the stock market is an
Speaker 1: open system that people are discovering new things all the time.
Speaker 1: They're discovering new ways, possibly to manipulate the system in
Speaker 1: their favor. Under complexity economics, you're not assuming the outcome
Speaker 1: is an equilibrium. Minobody has an incentive to do anything different.
Speaker 1: The way I would summarize complexity economics is it's viewing
Speaker 1: the economy as an evolving system. The economy is like
Speaker 1: an ecology. Every so often, new players discover new strategies.
Speaker 1: Just like in a real ecology, new species arise, and
Speaker 1: things that looked to be an equilibrium before suddenly thrown
Speaker 1: out of equilibrium. It's like introducing a new fish into
Speaker 1: Lake Victoria suddenly that really upsets the system and everything.
Speaker 1: That's just by assuming that we're not necessarily in equilibrium,
Speaker 1: where by assuming that problems are not necessarily well defined,
Speaker 1: we're basically looking at the economy ecologically. We can see
Speaker 1: all kinds of phenomena arising. So it sounds almost like
Speaker 1: complexity economics is about, rather than displacing other schools of
Speaker 1: economics or neo classical economics, it's more about sort of
Speaker 1: augmenting or complementing the methodology and making the whole the
Speaker 1: model is more robust and more sophisticated in being able
Speaker 1: to take into account changing um situations. Yeah, I would
Speaker 1: say that, but I wouldn't say it's an add on
Speaker 1: standard economics because standard nomics does have standard assumptions, all
Speaker 1: problems are well defined, everything happens at an equilibrium, and
Speaker 1: so on. It's more like every so often in mathematics
Speaker 1: or physics, to throw up the basic axioms. In mathematics,
Speaker 1: you could throw out the parallel acts. You could skip
Speaker 1: facts um and say what mathematics look like if if
Speaker 1: you didn't have that axiom, and you get very different
Speaker 1: sort of mathematics. So what we're really doing is trying
Speaker 1: to move towards realism, saying okay, people are not just
Speaker 1: muddling along. They might be doing quite sophisticated things, but
Speaker 1: everybody is doing that, and as they do that, the
Speaker 1: patterns are forming differently. So I would not say this
Speaker 1: is an addition to the standard approach. I think the
Speaker 1: subtle up that says some problems in the economy, in fact,
Speaker 1: very many are perfectly well looked at with equilibrium economics.
Speaker 1: It might be that the markets, for example, from day
Speaker 1: to day the price is roughly an equilibrium markets. Clear
Speaker 1: sort of economics is perfectly appropriate. So it's more like
Speaker 1: saying that if the wind doesn't blow, the stea will
Speaker 1: go flat. There's that doesn't contradict anything, but there are
Speaker 1: different sorts of questions. If the economy is just talking
Speaker 1: about how quantities are produced and production and consumption and
Speaker 1: patterns of that, the standard approach I think is pretty good.
Speaker 1: I have no quarrel with it. However, if you're asking
Speaker 1: how do new products come along? How do new strategy
Speaker 1: is developed? I'm sitting here in Silicon Valley, and there's
Speaker 1: no such thing really in the tech business as equilibrium,
Speaker 1: where exploring here how machine learning and AI work, how
Speaker 1: types of biotech and proteomics work, and that's changing month
Speaker 1: by month. So how do you maneuver and how does
Speaker 1: the economy work? If they sentience are changing around you
Speaker 1: and you have to readjust and do things differently. It's
Speaker 1: a bit like surfing years ago. I used to believe
Speaker 1: it or not, I used to surf in Hawaii. It's
Speaker 1: not that there's an optimal approach to a wave. If
Speaker 1: there is, I haven't seen it yet because you don't
Speaker 1: know where the wave is going to go next. So
Speaker 1: what you're doing is to adjusting your balance, adjusting your direction.
Speaker 1: You're looking ahead maybe twenty feet, maybe fifty ft or
Speaker 1: a hundred feet, and you're trying to be better to
Speaker 1: be over there. You want to avoid the white water
Speaker 1: instead in green water, and so on. So this is
Speaker 1: really an economics where you're looking at adjustment, things forming,
Speaker 1: and you start to ask questions like where the labor
Speaker 1: unions come from, where the insurance companies come from? Historically,
Speaker 1: where do legal arrangements come from? You can begin to
Speaker 1: see where in that murky uncertain world. We don't know,
Speaker 1: for example, what sort of legal arrangements there should be
Speaker 1: for large platform companies. So it's not as if there's
Speaker 1: an optimal strategy out there we will get somewhere that
Speaker 1: might chick two or three decades. We're trying this, we're
Speaker 1: trying that, we're thinking about this, learning that. So complexity
Speaker 1: economics applies to these situations where this are forming where
Speaker 1: you've never seen them. So it's not that this is
Speaker 1: an add on to do more detail to standard economics.
Speaker 1: Complexity economics isn't an add on two standard economics. It's
Speaker 1: um the type of economics that would be appropriate if
Speaker 1: new things are coming along but you don't know what
Speaker 1: they are, if people are strategizing in different ways but
Speaker 1: you simply don't know what that's going to bring. It's
Speaker 1: a bit like you could say standard economists would be
Speaker 1: a bit like fighting standard battles, saying six hundreds, you
Speaker 1: set up your army, the Duke of Marlborough sets up
Speaker 1: is you pretty much to know how the rules are,
Speaker 1: you let things interact and so on. Now you're in
Speaker 1: a situation where you don't know who you're up against.
Speaker 1: You don't quite know what the rules are, you don't
Speaker 1: know how things will work out, you don't know what
Speaker 1: new weapons will be used, and so you're backing and
Speaker 1: filling all the time. How do you make an economic
Speaker 1: side of that? So, just on that note, let me
Speaker 1: ask you um a question, and I'm really hoping that
Speaker 1: you're able to talk about this, But on this idea
Speaker 1: that complexity economics is better equipped to deal with new things,
Speaker 1: new situations, new behaviors that might evolve, what does complexity
Speaker 1: economics say about our current situation? So we just had
Speaker 1: a global pandemic um very different to previous global pandemics,
Speaker 1: We had a policy response um from governments, at least
Speaker 1: in the US that was very very different to previous
Speaker 1: policy responses, and we basically had an economic crisis that
Speaker 1: was quite unusual compared to other ones. So what does
Speaker 1: complexity economics tell us about the current situation? To my mind,
Speaker 1: complexity economics is not set up to tell us what
Speaker 1: we should do in any new situation like that, or
Speaker 1: any new crisis. It's set up as a way to
Speaker 1: understand how players mute actually adjust to a situation they're
Speaker 1: co creating that they don't really understand. So it's a
Speaker 1: way to look at the economy, it's way to understand something,
Speaker 1: and I think that the COVID crisis is pretty good
Speaker 1: illustration of this. At work in March, there was an
Speaker 1: awful lot of fear about COVID, and I think rightly so,
Speaker 1: because we didn't know how it worked, We didn't know
Speaker 1: how the infections would play out, we didn't know how
Speaker 1: bad it would be, so we were kind of groping
Speaker 1: in the dark. And complexity economics tries to look at
Speaker 1: situations like that. It may not say there's an optimal strategy,
Speaker 1: but we did come up with coping strategies. We tested,
Speaker 1: we tried. We we thought that fist masks weren't a
Speaker 1: good idea because it would deprive hospitals, and we thought
Speaker 1: fast my masks were a great idea, and so on.
Speaker 1: We coped. We were in the middle of a situation
Speaker 1: that people were creating as well as being part of people.
Speaker 1: Human beings co created the pandemic by interacting and infecting
Speaker 1: each other, not deliberately, of course, and we were trying,
Speaker 1: everybody was trying mutually to cope with this. And then finally,
Speaker 1: by December or so last year, we thought we could
Speaker 1: figured things out. But then along came vaccination, and that
Speaker 1: brought a whole new set of things to understand. What
Speaker 1: if there weren't enough vaccines, what we hadn't made the
Speaker 1: right arrangements, like the EU hadn't really in the early days.
Speaker 1: What of the vaccinations didn't work? What if you can
Speaker 1: get hold of vaccines, say like in South Africa, some
Speaker 1: are like that. And so we were learning and everybody
Speaker 1: was mutually adjusting and readjusting. The economy isn't one thing
Speaker 1: or the other. It's not a well oil perfect equilibrium
Speaker 1: machine where everything's tacking. And to the degree it's that
Speaker 1: standard economic supplies, the economy isn't always exploring new territory
Speaker 1: at the other extreme, and we're learning and trying to
Speaker 1: adapt and co adapt somewhere in between. So I would
Speaker 1: say that when you're replicating strategy, or when you're replicating
Speaker 1: the same situation from day to day, you could talk
Speaker 1: about optimizing well defined problems, optimal behavior standard economics. When
Speaker 1: the situations being created by the strategies you are not
Speaker 1: should we test for COVID, should we separate people? Should
Speaker 1: we have social distancing? Then you're in a different situation.
Speaker 1: So complexity economics is really about people co adapting, co learning,
Speaker 1: and figuring out what works. I'd like to make two
Speaker 1: points about industry, because I think your show has a
Speaker 1: lot to do with the industry, if I may, Yeah,
Speaker 1: we've certainly been talking about it a lot this year
Speaker 1: again because I think one of the unexpected things from
Speaker 1: COVID was the degree to which it would cause these
Speaker 1: supply disruptions, not necessarily because borders were shut, although that
Speaker 1: was part of it, but just because we had this
Speaker 1: really unexpected um import boom over in the States with
Speaker 1: everyone staying at home and ordering a bunch of goods.
Speaker 1: So please, complexity economics is web looking at the economy.
Speaker 1: How do you look at industry this way? What I
Speaker 1: say is if industries fairly constant, so we're turning out steal,
Speaker 1: the years nine and one year, one month is roughly
Speaker 1: not too different from the previous one. Standard economics would apply.
Speaker 1: You optimize inputs and outputs, and you optimize the technology,
Speaker 1: and prices come into equilibrium. But we're not as much
Speaker 1: in that situation. So the years now say one and
Speaker 1: many industries, and I think this is very much illustrated
Speaker 1: in your show here, Industries may try to optimize. Say, okay,
Speaker 1: I have this fleet of oil tankers, and I can
Speaker 1: sit in some office saying wherever Hong Kong or London
Speaker 1: or summer New York and optimize their schedule, thing s
Speaker 1: or inter if they're complicated and there's new unexpected uncertain
Speaker 1: events coming along the time Sue Canal closes, you're held
Speaker 1: up at some port unexpectedly, and so on. So optimization
Speaker 1: may not be quite appropriate. The strategies that are more
Speaker 1: appropriate are not just pure optimization. They have to take
Speaker 1: a kind of resilience. How do I recover? How do
Speaker 1: I cope with the unexpected? So I'm running the entire
Speaker 1: US or California electricity grid, my strategy should not be
Speaker 1: how to optimize that. There maybe forest fires, there may
Speaker 1: be disruptors, there may be more energy available, and I
Speaker 1: expect my strategy and industry isn't just optimizing. It is
Speaker 1: how do I cope with these unexpected, un thought of things.
Speaker 1: What if there's players who come up with something nobody's
Speaker 1: ever thought of. You can't optimize, You're in an unknown
Speaker 1: situation or it's partially unknown. What you can do. Those
Speaker 1: put things like this make computer models. It's a bit
Speaker 1: like the COVID strategy a year ago. What if what
Speaker 1: if this gets out of control? What if that happens?
Speaker 1: What if we don't get a vaccine for three years?
Speaker 1: And go on? And you can test all those strategies.
Speaker 1: The industry itself is moving from just simply being repetitive,
Speaker 1: which you can optimize in, to being resilient where you're
Speaker 1: looking for reactions. So complexity economics is basically saying, how
Speaker 1: do firms react to unexpected things in a reasonably smart
Speaker 1: way and what would industry look like? Flat were the norm,
Speaker 1: And we're rapidly shifting from what I call a standard
Speaker 1: productive economy. There's inputs, there's factories their outputs, there are
Speaker 1: human beings in the middle, there's planners, there's workers, and
Speaker 1: we're shifting more and more into what I call in
Speaker 1: an autonomous economy. The trading system used in finances largely autonomous.
Speaker 1: Supply chains are partially autonomous. As you would know, autonomous
Speaker 1: means that they're self regulating. There there isn't a human
Speaker 1: being planning each step. We're moving towards getting air traffic
Speaker 1: control systems that will be autonomous. That's already being pointed
Speaker 1: out usage of things like blockchain bitcoin, that type of
Speaker 1: trading and finance is partially autonomous. And in cases like that,
Speaker 1: I wouldn't say elements are acting on their own. We're
Speaker 1: moving into an economy or are an industrial setup if
Speaker 1: things are autonomous, where the elements and that's set up
Speaker 1: are in conversation with other elements. So if you're using blockchain,
Speaker 1: or if you're using some of these new financial arrangements
Speaker 1: that you've talked about in your show, different players, different contracts,
Speaker 1: maybe automatically or autonomously in conversation with other elements. That
Speaker 1: is exactly the whole domain of complexity. Again, go back
Speaker 1: to cars and traffic. Maybe I have a driverless traffic
Speaker 1: system in ten years time. I'm in say Los Angeles,
Speaker 1: but I'm not driving in a car sitting in a
Speaker 1: car that's driverless. I'm sitting in a car it's in
Speaker 1: a driverless convoy convoy driverless cars, and each car is
Speaker 1: in conversation of cars around it. So what complexity economics
Speaker 1: is trying to do is to say, what if you
Speaker 1: have a sort of autonomous semi autonomous system where the
Speaker 1: elements and that economy, they might be trading strategies trading systems.
Speaker 1: They may be cars and driverless convoys, they may be
Speaker 1: different electricity generating systems. What these are in conversations to
Speaker 1: other systems? How can we understand something like that? How
Speaker 1: can we control it? What policies would be necessary. That's
Speaker 1: very different from saying we have a century economy. So again,
Speaker 1: I would say we're moving fairly fast into a system
Speaker 1: where the players, the elements, the the cars, the planes
Speaker 1: that are landing in an autonomous air traffic control situation
Speaker 1: are automatically in conversation with other like elements. How does
Speaker 1: that work? How can we think of it? How should
Speaker 1: we control it? We can't optimize it because you never
Speaker 1: quite know what's going to happen next, but you can
Speaker 1: certainly make a Brazilian I do like that. Complexity economics
Speaker 1: is making an attempt to understand the real world economy
Speaker 1: as it kind of matures and becomes technologically more advanced.
Speaker 1: And it's true if you look over classical economics um
Speaker 1: the literature there, so much of it just seems so
Speaker 1: old fashioned nowadays, not in the sense necessarily of what
Speaker 1: it's actually saying about economics, but just the idea of,
Speaker 1: you know, this country makes wine and this country makes
Speaker 1: cloth and they're going to trade with each other. Um,
Speaker 1: you know, never mind changes in transport technology or manufacturing
Speaker 1: technology and things like that. Anyway, um, Professor Brian Arthur,
Speaker 1: we're going to have to leave it there. But it
Speaker 1: has been an absolutely fascinating conversation and I really appreciate
Speaker 1: you taking the time to walk us through complexity economics.
Speaker 1: Thank you so much, and I'm delighted to have been
Speaker 1: with you. Thank you, Bunty, Thank you so much. Well,
Speaker 1: Joe isn't here to bounce off of, so UM, I'll
Speaker 1: just go ahead and give you, I guess my two
Speaker 1: big takeaways from this. Number one, I think it's fairly
Speaker 1: clear that there are shortcomings in traditional economics, and you
Speaker 1: can argue that they're there for a reason that you
Speaker 1: have to have simplifying models. You have to have simplifying
Speaker 1: assumptions about how the world works in order to you know,
Speaker 1: make policy recommendations and make them relatively quickly. But I
Speaker 1: also think that given the advancement in technology, given the
Speaker 1: advancement in computer systems um that the professor was describing,
Speaker 1: it does seem natural that we now have the capacity
Speaker 1: to model the real world on an altogether different and
Speaker 1: much more advanced scale. And I think it's interesting to
Speaker 1: see a school of economics that's really trying to take
Speaker 1: that technology and use it um to contribute to the
Speaker 1: literature in an interesting way. And then the second thing
Speaker 1: that sort of stood out for me is related to
Speaker 1: that point, but just the notion that in THEES, when
Speaker 1: we were talking about free trade and outsourcing this idea,
Speaker 1: that we didn't have the computational power to actually model
Speaker 1: what that would mean for every single segment of society
Speaker 1: um in every different country. We could only look at
Speaker 1: it sort of from a broad bird's eye level view, going, well,
Speaker 1: obviously we want cheaper goods, and obviously they want cheaper goods,
Speaker 1: and so we're just gonna trade with each other and
Speaker 1: everyone's going to be happy. And twenty or thirty years later,
Speaker 1: it's very very clear that that wasn't necessarily what happened.
Speaker 1: The outcome was rather different, And it's extremely interesting to think,
Speaker 1: what if you had had something like complexity economics, What
Speaker 1: if you had had more advanced, more sophisticated models, what
Speaker 1: sort of policy decisions would you have made? So I
Speaker 1: guess I'll leave it there. This has been another episode
Speaker 1: of the All Thoughts podcast. I'm Tracy Alloway. You can
Speaker 1: follow me on Twitter at Tracy Halloway. You can follow
Speaker 1: Joe at The Stalwart. You can follow our producer Laura
Speaker 1: Carlson at Laura M. Carlson. And you can follow the
Speaker 1: head of Bloomberg podcast, Francesco Levi at Francesco Today. Thanks
Speaker 1: for listening.