Hello, everyone. My name is Charles Gun-Free, I'm director of the Oxford Martin School, and let me welcome you to the Oxford Martin School.
When I buy a book, I scribble in the book the date that I bought it and where I bought it.
So I know where I first became aware of aware of and former speaker.
And it was, and I've just looked it up, saw myself down below.
It's July 1998 and I was in Seattle at the time and the book was James Gleeks book on Chaos.
And it has a whole chapter on the dynamical systems collective Santacruz, of which Darwin was one of the one of the members.
This was an extraordinary group of people in the late seventies.
And you all before you eyes then weren't you who it just expanding work and bringing forward the field of complexity science?
I have to say I find it deeply depressing because I was just doing just out of my Ph.D. myself,
and no one was going to write a chapter on what I had done in my day.
So that was one of the founders of the field of complexity science and has gone on to do very many major things at Los Alamos.
And then most recently in Oxford, which he joined nearly 10 years ago and then today is the co-director of the Oxford Martin programme on Post Carbon
Transition and a principal investigator in the Oxford Martin programme on 10 Technological and economic change.
He's also a professor in the Mathematical Mathematics Institute here in Oxford and external professor at the famous Santa Fe Institute.
And then going to be talking this evening on how complexity can resolve the crisis in economics.
Then. So thanks, everybody for coming.
When I was invited to give a lecture on an evolving economic thought,
I couldn't resist trying to impart what I've learnt in my twenty five years
or so of working in economics as a physicist who sits in a maths department,
who does economics, who's also dabbled in biology and a few other areas.
And and so I'm trying to to sort of distil the main things I've learnt during those twenty five years.
This is also, by the way, sort of a I'm writing a popular book.
And so this is the first test run to try and present what I'm trying to say in the book.
So economic thought changes through time. You know, when we're in high school,
we're taught the scientific method and the retarded as though it's just cast in stone and everybody does it that way.
But in my experience, that's not at all true.
It's vastly different in different disciplines, and it evolves through time and economics is evolving through time.
It's becoming more empirical science. I've really seen that in the last decade,
but it's experiencing tension because the changes it's finding are not completely compatible with the theoretical framework that's been set up.
It's also had some problems where there have been voices of protest that the 2008 financial crisis,
where the models were completely abandoned during the crisis because it was clear they weren't relevant to what was going on,
and there were a lot of criticism that they should have done a better job of being ready for something like that.
Inequality, actually, we were at the OECD a few weeks ago, and Gabriela Ramos said that bad economics led to bad politics.
So we're experiencing, you know, a period where people are sceptical about experts because they feel like they got
bad advice from them or the experts in many areas don't agree with each other,
which never looks good. And and we're, you know, we're experiencing the consequences of layers of society.
We never thought we were going to see them from are significantly caused by bad
economic models and bad economic policies being put into practise as a result.
And then the big one is climate change, which is, after all, caused by the economy, and we need to figure out how to fix it.
So we really need guidance from economics, and I would argue we're not getting the guidance we need.
So this is these are these are the factors that led me to say that there's a crisis.
But even more specifically,
there is there is a cognitive dissonance within economics because there's a theoretical machinery that's based on rational expectations.
Behavioural economics says this is wrong. OK.
You may not know what this means. I'll explain in a bit. Can adding friction save it or a bigger change is needed?
So I'm going to try and discuss this during the course of my talk. I'll come back and explain what these words mean now.
But before I do,
I want to talk a little bit about what the economy is because I think if we if we if we think the right way about what the economy is,
then we set the scope and the goals of what we're trying to do differently.
And that affects the way we talk about things. Now I'm going to argue that if I had to describe the economy in three words,
it's the metabolism of civilisation and that it converts natural resources and human effort into goods and services.
It provides us with things we need and don't need, but it provides us with all the stuff that processes a lot of information to make that happen.
And it's very much like a metabolism in a biological organism, which takes some form of food,
breaks it down into different components, and then reassembled it in the stuff that we need.
That's just what the economy is doing. The difference is, instead of chemicals,
it's happening in a much larger scale level with material stuff and that the
economy is a truly remarkable thing that I don't think we properly appreciate.
It coordinates, and I will argue it coordinates and amplifies the activities of ecologies of specialists.
Now what do I mean to that? Well, we're all specialists. OK. I am a physicist.
I'm working in economics.
I'm sure if we went around the room, we'd see lots of different specialities and what people are doing and you go more broadly.
I mean, there's there's tens of thousands of occupations and skills, and we all know how to do a specific thing.
But we're all interacting with each other, just like, say, in a biological ecology animal specialise in doing a certain kind of activity.
And then ecology is about the study of how those interactions happen in a more holistic way and how those interactions take place.
It allows us to do things that we could never do on our own. Imagine that each of us had our little plot of land.
We divided the Earth up. We all got our little plot of land. The rule was You can interact with anybody.
You've got to survive. How many of us would survive? Probably close to none.
You know, we owe our lives to the planet. Could never support the population has not by a factor of a thousand or 10000.
Even if we didn't have the economy to boost what we're doing now,
just to give a little spot a view of richness of just one thing take the laptop that I have sitting here in front of me.
Here is the physical supply chain of a laptop is constructed by some fellow named Leo, and a lot of hard work went into this.
And you can see with the little numbers and the arrows showing how the basic raw materials get taken out of the ground.
They get shipped around the world to other places and processed. They get smelted assembled into larger parts.
Those parts could ship to new places. Those get assembled a laptop, get shipped somewhere, you know, and the tax bill goes to Ireland.
And so. So it's a remarkably complex thing.
And if you think about it, compare it, say to a human birth, which is a remarkable process.
I mean, the closest thing we have to a miracle. But it does all happen inside of a fairly small place takes place in about nine months.
This takes a couple of years from the stuff getting dug out of the ground, the laptop getting assembled,
and it's coming from all over the world and miraculously coming together in one place.
Now imagine we had a Google economics.
So what do I mean by that that instead of just having, you know, map you could like,
take your phone and you can point pointed at a building and it would tell you about the economic activity that was going on there.
Imagine that you could see the flow of the goods and services in and out of the building.
So, you know, if it was the Apple headquarters, you'd see some some part of this picture.
You can see the stocks and flows of capital because of course, to make all this stuff,
you've got to have money flowing in because money is like 8p in biology.
It's the energising thing that makes stuff happen.
People that have money can just wave their wand and people will start working and doing stuff.
There's a web of contracts that is to produce all this stuff.
All of these providers have contracts for their workers, have contracts with each other.
These contracts link their activities together and and tell us, interact through time because contracts have conditions.
And if this happens, then that happens. There's demography and occupational capabilities.
There's a heterogeneous population on the globe that's doing very different activities to make this happen.
It tells us it would tell us about wealth and poverty. Who's getting rich?
Who's getting poorer? Tell us about ownership. Well, we know it's very concentrated.
It could tell us about the ecology of innovation. Who are the thinkers that are that are influencing the people that are doing this stuff?
It would tell us about the physical and environmental impacts, how much carbon is getting emitted, how poorly are they doing at recycling materials?
It would tell us about the regulatory constraints that are shaping what's going on, which is also an essential part.
And I put Typekit because this is just the beginning of the story. Now, how complicated is it?
Or at least let's put some numbers on how big it is. Well, the production network.
So if we really look at this for all the production of everything on the globe, we're talking about the order of 50 50 million firms.
Those firms have billions of physical links to each other because each firm, as we see in this picture, is getting inputs from the other firm.
So each firm is profiting from the innovations the other firms making the whole thing's very closely linked together.
There's there's a network of households, the order of two billion of them, the order of three and a half million workers.
There's trillions of links to the things they consumed, the people they work for, the contracts they make.
And there's a web of contracts. There's probably the order of a trillion contracts. They're active on the Earth at this point in time.
And it's a dynamic contract expires. A new contract gets made.
It might be different, OK, the economies heterogeneous.
So this is a picture of how prices have changed for various goods in the United States over the last 20 years.
So at the top, you look at hospital services, which have gone up by a factor of two hundred and twenty five percent or so in the US during 20 years.
And you look at the bottom and television sets that have dropped in price so much that it's
actually hard to tell just how close they were getting to being free from this picture.
And there's a lot of stuff in between. So it's a very heterogeneous process. Take.
So let me just say so if you read in the newspaper that inflation is two per.
Well, what does that really mean? That means that for hospitals, it's a whole lot more than two percent, and for televisions, it's minus 15 percent.
So it's a very heterogeneous number, and the average inflation actually tells you only a very crude thing about what's actually going on.
Take another figure. Like unemployment, you read that unemployment is five percent.
What does that mean? Well, here's a histogram down there of in the lower left corner of unemployment rates for different occupations.
So if you divide the world into, say, four hundred and fifty or so occupations, what are the unemployment rates?
Well, if you're a nurse, the unemployment rate's really low.
And if you're an actor or a boilermaker, the unemployment rate's, you know, twenty five percent or more.
And the network picture that we've constructed over there on on the right is showing how if you
organise occupations based on similarity of their tasks to each other and or organise them,
alternatively, we could have organised on job transition probabilities between occupations.
Then you see, there's a very clear structure.
There's some occupations down in the construction industry in the lower left of the picture with very high unemployment rates.
There's occupations in the health industry and the right with very low unemployment rates,
technological progress, essential thing driving economic growth.
If you make a histogram of of of the rate of improvement of different products, you see, it's again extremely heterogeneous and very skewed.
That is a kind of a bell curve in the middle.
And you can see if you look at the x axis, which is showing the average annual growth rate over a close to fifty year period.
Then you're seeing that some technologies are.
Most of them are in the middle of this bell curve. But there are some technologies out on the tail like things relating to computers that have grown
at much faster rates than and have improved at much faster rates than the rest of the economy.
And if you guided even to a more fine grained level, and here are the pictures changed, it's a little you have to.
We're representing things that are in a different way because the y axis is now the improvement rate.
So you start on the left side with optical telecom, which is improving at an astounding sixty five percent per year.
And on the other side, you've got a more, let's say, basic technology like milling machine or electric motor,
things that have been around now for hundreds of years that are improving at rates of a few percent per year.
So there's a huge. Disparity and heterogeneity and these improvement rates.
Now finally, people are heterogeneous, and we have to remember when we think about the economy, we're all different and we think differently.
We have different needs. And that's another important factor that needs to be taken into account whenever we think about economics.
Now there's a caricature of economics as being a counting plus behaviour.
This leads some things out. Maybe come back and say a bit about it, but let's just start there.
And so the accounting is, in a sense, the easy part because of accountings.
Accounting is mechanical. And you know, every household, at least conceptually has a balance sheet.
And on that balance sheet, as the assets and liabilities, the things they own, the things they owe to other people.
And as I already said, there's the order of two billion balance sheets in the world and there are thousands of entries,
I would guess, on the average balance sheet. Certainly, people in the developed world have thousands of entries on our balance sheets,
and that means that when you add everything up, we have, you know,
what's already said,
there's trillions of contracts and our balance sheets are interlinked because my liabilities or somebody else's assets in general.
So every time you have an asset liability relationship, those balance sheets get linked together every time you have a contract.
Those balance sheets are linked together. So you have this vast network with two billion links and lots of with two billion nodes,
trillions of links and a complicated interacting structure that's changing dynamically in time.
So accounting is pretty complicated thing, but at least it's straightforward.
It's clear, it's like physics because there's conservation laws, they're very clear rules about how things work.
Now behaviour. Now we get to the sticky part because behaviour is harder to understand.
I guess I don't know if you can read the caption down there.
The This is a Gary Larson cartoon. Yes, they're all fools.
Yes, they're all fools, gentlemen. But the question remains What kind of fools are they?
No, actually, that's not quite what how economist model behaviour, but I couldn't resist putting it in there the way they model behaviour.
At least the typical model is in terms of rational expectations.
So the concept is there's a rational agent who can compute everything that is a rational agent.
Can you know how well you might? You might or might not have all the information or whatever information they have.
They can do all the computations they need to decide the consequences of that information.
And now, in modern macro and the rational expectations model began in a circa 1960 with John Meuthen had some antecedents before that.
But in modern macro, rational expectation is layered on with frictions.
Now what does that mean? It means that. And by the way, I'm going to I might refer to this as constrained rationality.
So it means that each agent has a utility function that tells them what they like and don't like in a macroeconomic model.
That would mean always that what they want to do is maximise their discounted consumption.
So I want to consume as much as possible so the rational agent will maximise the utility taking others into account.
But the world imposes constraints which are called frictions, which make this harder to do and make that the,
you know, full rationality of this in a sense, less complete.
But it actually makes the problem even harder because it just means there are more constraints they have to take into account.
They can still do all the computation they need to do everything they need to do, and you then call this an equilibrium.
If you're in the state where everybody has maximised their utility,
so everybody finds this place and they go to that state and they stay there, and that would be the equilibrium.
But then stuff happens. So things come in from the outside world economies that the equilibrium zus flows down
a lightning bolt and hits the economy with a shock and knocks it away from equilibrium.
And it's like this rocking horse that sets the horse rocking and it starts to move back towards equilibrium,
but then just throws another shock and knocks it away.
So the world is constantly, never quite at equilibrium because it's constantly getting hit by these shocks.
And now if you look in a production macro model like, say, the nuts and bolts model,
which is one central banks used to think about the economy of you pick a country, they actually had seven different kinds of shocks.
So zoo sector has seven different colours of lightning bolts he can throw out so he can say,
Let's change labour productivity now, let's change the risk perception. Let's change technologies, wages, prices, spending and monetary policy.
Now these are shocks in the sense that no one can know them in advance.
They come from somewhere else outside. It's not the economists job to understand where these come from and this kind of model.
Now, so what's the research programme of modern mainstream macro?
It's basically you take one of the core models, you add a new friction, you solve that model with a new friction, which is hard.
Then you test does that improve the match to empirical facts and then you go back and do it again?
And each one of these steps might take you a year or something. Now what are the problems, in my view and this approach?
First of all, experiments don't support the foundational assumption. You know, they show beyond a shadow of doubt that people aren't very rational.
It's been difficult to match empirical facts. I mean, I've said they are getting better at it.
There's been a lot of work that's gone into this and matched to experimental facts is getting closer.
Still not quite there. And there are debates about whether the frictions that have to be put in or sensible in order to get those matches to happen.
A lot of people think they're not. But in a sense, an even bigger problem is these models are really hard to solve.
And that's not surprising because rationality is a hard thing to do if if you
want to be completely rational and understand everything that can happen to you.
And and what the right thing to do would be in response to those things then and think about everybody else and what they're thinking.
Then you you really have to solve a pretty serious problem.
So what does it mean? It means you have to simplify the assumptions of the model.
You take those two billion balance sheets and you crunch them down into one balance sheet, or maybe a few balance sheets.
But there's no way you can deal with the complexity of the real world in that kind of a setting because it's just too hard to do the calculations.
The other thing that I'll come back to and say more about is rationality suppresses endogenous dynamics.
That is, the reason that the framework is set up this way is because that's what you see emerge in these models.
These models want to sit at rest. Now there are examples of models that have that spontaneously change, but those models are not the norm.
Ninety nine point nine percent of models you find are more are models that sit at risk.
So if you want to explain the dynamics of the economy because the economy is changing all the time, you have to assume that it's coming from shocks.
And finally, it doesn't take advantage of 21st century technology now.
I already said it's hard. I mention that you simplify things down to only looking at a few balance sheets.
So you take all these people on the left and you say, Well, it's too hard to think about all their balance sheets.
So let's do something like make a representative agent like the one on the right.
That's sort of a polyglot mixture of everybody and assume that's the only person in the world or, if you will,
there's seven billion clones of that person and they work for one firm and that
one firm makes one product and that would be the economy and a baseline model.
Now you start to build it back up and the frontier and modern market.
This was, I have to say, the state of the art 30 years ago. The models from 30 years ago have been put into operation of central banks.
Central banks are still using models that are like this.
But if you open, you know, top economics journal and look at the major activity now to try and build models that have heterogeneous agents,
though you also might get a little nervous because those heterogeneous agents sort
of tend to look like more or less educated versions of the guy on the right or,
you know, richer and poorer versions of the guy on the right.
It's a kind of monochromatic notion of diversity, and you might put in a diversity of households,
but you're not going to have a diversity of firms and products and all the other stuff in that same model.
Or you might have a model with some diversity of firms and products, but you're not going to have a diversity of of agents.
So it's still and it's because it's so hard to solve these models. Technology is tough.
Now, this leads to a kind of catch 22, which is I hope I convinced you in the first part that the economy is complex.
It's a complicated two. It's evolving, so it's changing in time all the time.
And that means that data from the distant past isn't very useful from the data in the present.
And that means historical time series or sure, it is also true that it's not very well recorded as you go further back.
So we don't have that much data. In fact, I estimate that the data that goes into calibrating one of these models is about five hundred bytes.
I notice I didn't say megabytes or even kilobytes bytes, not much data that's determining this is the state of the whole world over a 50 year history.
I estimate there's about five hundred bytes of useful information and in those time series.
So there's very little data. So of course you can't. Any statistician knows you can't then estimate a complicated model.
You have too many free parameters. So that means you can only have a simple model, but the economy is complex, so it's a catch 22.
You know, you're damned if you do and damned if you don't. Now, I also mentioned that the standard macros based on old technology,
I've showed a picture there of the computers that existed at the time as basic machinery was getting set up.
So, you know, the data wasn't being collected by the computer is being collected by hand,
and the whole process that set up in national accounting is oriented around
the thinking that was happening like during World War Two and shortly after.
And. And if you look at the way these models are run now, I mean, OK, the computers,
this giant thing in the background, by the way, is vastly less powerful than my iPhone here.
But you know, you can run most macro models on a laptop, and the computing that's actually done is all spin and find this equilibrium.
It's not tracking all the flows of stuff around the globe and the interactions, so it's not staying up to date with technology.
Now what's the alternative? I would argue it's thinking about the economy as a complex system, which has emergent behaviour.
So that's the definition of a complex system, meaning that you have qualitative,
qualitatively different behaviour than that of the individual components.
The classic example would be the human brain, which is made up of neurones actually so many different kinds of neurones,
but each neurone is still a pretty simple device,
and you would never say, Oh, this neurone is conscious or this neurone is, you know, capable of appreciating a poem or something like that.
Somehow, these eighty six billion neurones interact with each other.
And, you know, many trillions of synapses and we end up with thought.
So there's a clear qualitative difference between the lower level component, my level.
You can show that that a necessary condition for that to happen is nonlinear behaviour, the whole it.
That's what allows the whole not to be equal to the sum of the parts.
And you've got to have that if you you're going to get emergence and across lots of fields.
If you want to understand emergence, you have to you have to model on a small scale to see how that emergence comes up for the larger scale.
Now what's the emergence in the economy? That's what I was saying before. You know,
we collectively act in ways that remarkably amplify our ability to do things and and that I would argue the traditional models are not not capturing.
We're not even equipped to talk about in a sensible way. After all, if you're rational when you have to bother.
Evolving and everything else now completes the economics, which is a field I'm working on now, applies complex systems to and methods to economics.
The conceptual model we would argue for is bounded rationality that is saying that reasoning capabilities are limited
and that's very different than constrain rationality because we're not saying that we're perfectly rational.
We're just constrained by stuff. We're saying, actually, there's a limit to how much we can.
We can solve problems and when we're really limited by that and we have to really think collectively about how we collectively solve problems.
Especial is taking that into account, and I'm showing a picture of Herb Simon, who was one of the first people to articulate that idea.
Now I played a bit of a trick on you and my earlier slide when I said I said rationality and I showed you a picture of Bobby Fischer.
Now, in addition, the fact that if you know anything about chess, you know, Bobby Fischer is a nutcase.
But but chess players are, by definition, guardedly rational.
Why? Because nobody can foresee everything in chess, not deep blue.
Not Bobby Fischer. Not anybody. Because chess is too hard. I think there's a lesson there now.
But on the other hand, there are situations where, say, older children say playing the game of knots and crosses can become rational.
Like, I remember when I was about 10 OK, in America, we call it Tic TAC Toe.
We were playing Tic TAC Toe. It seemed like a lot of fun. You know, when sometimes it was really fun.
And then I figured out that there was a strategy that I could always at least get a draw.
And then my friends figured it out, maybe a day later and it became hopelessly boring and we quit.
Now what happened? We found an equilibrium. We became rational.
We learnt enough because the game simple to actually be rational with respect to this game.
So there are situations where rationality makes sense to explain things.
But the problem gets harder. It doesn't happen. Now I'm going to give an empirical test, so I'm going to ask you all to play a beauty contest game.
So the idea is to get a number between zero and 100.
That's two thirds of the average guess. And I expect there's a few wise guys that understand that.
So keep your mouth shut. But I want you to all think about this because I want everybody to get the number.
So thing I'll remember. Everybody's going to pick a number between zero and 100,
and the winner will be the one that gets two thirds of the average guess of everybody else in the room.
Now the little bit, while you think about it for a minute, little picture on the side is say this is inspired by Canes,
who pointed out that the stock market is like a beauty contest because in that day there were beauty contests where you would enter a lottery.
You would have to pick one of these women as the most beautiful,
which actually means the one you think other people will think is going to be the most beautiful.
And that's what's important if you want to win the contest. So it's not beauty.
That's there's no objective standard for that. It's a question of understanding what people perceive as beautiful.
So it's a subjective game. Now everybody have their guests. All right.
Now I'm going to show the next slide so you can see what a large group of people tend to do on this.
And this was a survey done by a Danish newspaper. And so you see on the x axis the frequency of the various guesses of 19000 respondents
to this daily Danish newspapers query had more time to think about it than you did.
And so you can see the guesses that everybody made.
Now, when you look at this, you'll notice some disturbing things like if you go over here, there's a bunch of people that just more than two thirds.
But just think about this for a minute. Let's suppose everybody in the room had just one hundred.
What would the and what would the winner be? It would be sixty six.
OK, so the answer's got to be less than that. All right.
No. If everybody guessed it random, so everybody just picked a number out of the hat.
Then the average would be 50. And so the winner would be a third.
And actually, you see the most people voted for a third, so people aren't.
The reasoning here? Right. More than six percent of the people voted for a third.
But then there were those people who said, Let's go one more level because I'll bet that,
you know, let's go to level two because if they're guessing a third.
Then let's take two thirds of a third. So they just said, What is that two nights?
And so they guessed two nights or so. And in fact, that's the winner.
But then it goes down and there's the people here who just 0.2 percent of the people just rule, which if you think about it,
if you'd kept carrying that process through, you would have ended up at zero, which is the Nash equilibrium for this game.
And because this set of decisions that everybody had gone to zero,
then if everybody's a winner at that point and if anybody deviates, they're going to, they're not going to be a winner anymore.
So that's the whole idea of a national government. Equilibrium economics tend to have this flavour now.
OK, so how useful was that idea? So the game theorists and economists just zero with a losing get right is a bad model of human behaviour.
Now, of course, the sceptic could say What if we kept playing this game?
Let's play it again and again. Well, in fact, it would start moving towards zero because you can see here already people are, you know,
there's enough intelligence in the room that there are a lot more maths on this side of the histogram than on that one.
And so if we played this game again and again, your guesses would start going down and we might actually well converge on zero.
But what about other games?
So I devoted the last ten or not all of it, but off and on for 10 or 15 years with some really great collaborators who are listed there.
And we've written about four or five papers studying this and lots of other kinds of games.
We've actually now exhaustively studied normal form games, and we've shown which is a particular kind of game.
You don't need to worry about it here. But we've shown that equilibrium becomes unlikely when the games are complicated and competitive.
That is, as soon as you have more than two players and more than a few possible actions.
And when the incentives of the players aren't lined up so that if I win, you tend to lose and vice versa,
then you don't go to equilibrium anymore under, you know, well, substantiated learning algorithms.
And instead, I'm sorry, everybody can't see danger of two screens.
But as the game gets more complicated when the game's competitive, we quickly go up to a place where we will almost never go to equilibrium.
And instead, what do we see? We see chaotic dynamics happening.
So right now complex the economics, we try to take behavioural economics seriously.
Now I'm embarrassed to say that Sanjay Dummy was giving us the talk today,
and he kind of said, Well, look, you guys aren't really taking it seriously. Well, we at least intend to.
And me too. And I think I'll come back in a minute to say why.
I think that maybe isn't as good as it could be, you know, but we do believe in models where the agents reason and in a empirically justifiable way,
they follow heuristics, they do some myopic reasoning, just like the people who won that game.
Did, they update the heuristics that recently are working well and they may or may not converge to equilibrium?
Now, in order to do this, we have to simulate the world,
meaning we actually use a computer to simulate what people are doing because it's too complicated to do.
Using standard, you know, close to maths like you get taught in in a maths course in college.
And so we mimic the world on a computer and.
Bounded rationality is actually essential for us because it means we can still simulate things in complicated situations,
just like real people can make decisions in complicated situations.
It's not true for constrained rationality. There's also a difference in philosophy because in complexity economics, we we reject as if reasoning.
I mean, even if rationality with frictions isn't literally what people do,
maybe it's the economy behaves as if they did to say, Well, actually, let's just use the principle of Earth similitude.
Let's try and make things kind of like the real world and at least the essential features and are.
And so we should have assumptions that are plausible and that we can empirically verify following,
of course, Einstein's dictum that everything should be made as simple as possible, but no simpler.
And now I don't have a lot of time left, and I want to make sure I leave plenty of time for discussion, but I'm going to give you.
So I first want to give an example of how you immediately get endogenous dynamics as soon as you deviate from the standard model.
But we're going to start with a standard model. And these are my collaborators at the top led by Ukiah Sano.
I think I over there. And and so we take a standard model with a representative household.
This guy in the picture, we got a household that has to make one decision.
That is, how much should I save and what doesn't get saved,
what gets saved then gets invested in producing stuff for the economy and the rest gets consumed.
So you can either eat your earnings or you can reinvest them to make more stuff.
So you have something the next period and you've got to find the right compromise.
Now in the standard model, and you know, you may not follow this because particularly since I can't point at both things at once,
but there is a there's a this is showing the flow of how things flow around your.
Let's suppose you're making a certain you're using a certain savings rate.
How much capital do you do you have accumulate versus how much are you consuming at each time step?
And how do you move around the space as you make these rational decisions that this agent is doing?
And what you see is that first of all, everything flies off to infinity and there's only one point here, and that's a fix.
Well, there's a fixed point here.
There's a fixed point down here where the whole economy collapses and there's a fixed point here where things don't move, but it's unstable.
Any little perturbation away from that point will set it off.
So how does this work in the standard economics model you assume this household is doing here is so smart that they managed to sit at that place.
And then you can OK, the rest of the model proceed to get hit by a shock.
They move back to the place, gets hit by a shock and move back now.
We changed it to say, well, let's suppose we have a bunch of households and they have some kind of social network.
And what happens is once in a while a household wakes up, looks around at his neighbours or her neighbours and says,
Oh, that neighbour is the one who's consuming the most because everybody's been conspicuous about their consumption.
And and I'll just consume whatever they're consuming. I'll actually know.
I will choose their savings rate because it worked for them. So we do that.
And what happens then? Well, take this picture here.
Instead of sitting at the fixed point, OK, in some cases, we see it sitting at a fixed point that's highly inefficient.
As we lengthen the period out, it gets more efficient, meaning more stuff gets produced and households better off.
But then finally, what happens when it starts oscillating? So here you see an orbit where the the colour is, the savings rate.
You see, we're orbiting around that fixed point now.
So you're getting spontaneous dynamics that's coming out of nowhere because all these agents are doing is waking up and looking at each other.
There's no shocks in the normal sense here, other than a little bit of randomness and when the agents wake up to look at the world.
It just comes out of the endogenous dynamics of this model.
And you see that we get something that looks an awful lot like a business cycle because we see, you know,
the output of the economy now spontaneously fluctuates in time as the average savings rate of all these agents fluctuates.
And as each of them makes their decisions, each agent actually is having their savings rate fluctuate.
Now, one way to think about what's going on here is there's something, I think, a better model than the rocking horse.
It's actually a pull balancing. In the old days, I would have had a pointer that I could use.
But if you imagine trying to balance a pole in your hand, it turns out of the poles shorter than about three feet.
You can't do it. As soon as the pole gets long enough, you can do it.
You can just hold it more or less upright, but it won't sit exactly upright.
And now how does a traditional macro model model this?
This guy assumes it stays exactly vertical unless it gets hit by a shark, which would knock the pole balance or his hand.
The whole the pole balance would make the optimal movement to set the pole back up to being vertical again until the next shock you.
But of course, that's not the way this works. The way it works is that you make a correction.
The corrections imperfect pole overreacts, you overreact.
So there's constantly indigenous populations that are chaotic in nature, just as we saw in our modified model.
Now I'm got to run out of time, so I'm going to go quickly here.
Now I'm just going to give some examples to demonstrate that economics can be done without equilibrium going to fly through these at blinding speed.
The purpose is just to show it so I can make my my final remarks and it will go on.
So these are all done with no utility functions, no rational agents and no perfect maximiser.
So we made a model of the crisis of 2008, where we just model the behaviour that we know that people were doing.
They were following value at risk.
They were using historical average of volatility to compute the risk and adjusting their leverage in the market accordingly.
And we see a spontaneous oscillation come out of our model that has a period of about 10 years,
with the prices in the stock market going up and down and leverage going up and down.
We have models of this financial stability of the European banking system,
where we're literally taking the balance sheets of the 100 most systemically important banks in
the UK and looking at the way they interact when they do things to try and do their risk control,
which can then set up large scale oscillations due to systemic effects.
A team of us are looking at unemployment, making agent based model of job transitions,
where we can predict the effect of an automation shock and and think about questions like which professions are
safest because it might be even though your profession is not likely to get automated or is likely to get automated.
Some professions may have an easy time transitioning other ones that are and vice versa.
And if you come in two weeks, Penny may well at least say something about automation in the job space.
Thinking about technological change and taking advantage of these facts, I have you.
I showed you things that are highly heterogeneous, but I don't know if you noticed this in this picture,
but there are amazingly persistent once you go down to the level of individual technologies.
It's not like hospital services got really more expensive one year and then less expensive the next year.
For 20 years, they got more expensive every year. Televisions got cheaper every year, and these patterns were amazingly persistent.
But to see that persistence, you have to get down to the micro level, and normal models are not doing that.
This allows us to do things like forecast technological progress,
and we can even forecast how good our forecast is and we can put all these forecasts
together and think about how the different components in that picture I showed you are
interacting with each other and how technological progress in one domain is is percolating
over to technological progress in other domains and use this to predict growth.
We can bring in ideas from ecology to organise firms and industries into ecological structures that that use a
totally literal analogy to trophic levels in ecology that actually turns out to be very closely related to growth.
OK, we can make predictions without and and we can use this to compute the cost of the green energy transition.
And one of the results that we hope to release a paper with in the next month is that actually the green energy transition is not just cheap,
it's probably actually when net present value is taken into account at just about any discount
rate likely to be cheaper than the alternative of just sticking with business as usual.
And again,
we can do that because we get down to the fine grain level where we can really think about the individual technologies and put them together.
Now, I also think we can solve a Catch 22 macro by going to what I'm calling global microeconomics that is letting macro
emerge from micro taking advantage of heterogeneity rather than viewing it as something that makes things hard.
Taking advantage of the fact that there's a lot more data micro scales that allows
us to get better statistical significance and deal with the Catch 22 of macro,
I think we will naturally see the emergence of more endogenous dynamics.
We can then model that emergence and because we're modelling in a finer scale,
we have more things to predict, which means we have more ways to validate our model.
Now, just to wrap up, I mean, complexity economics is yawn.
So I'm in that last slide. I'm throwing out a super ambitious programme.
But you know, I estimate there have been the order of five hundred person years of work that's gone into this field.
In contrast to, say, the 50000 that have gone into the mainstream approach, a lot of work remains to be done.
We need to develop new methods. We need to fit models to a time series better.
We need to do better parallelism so we can run big models.
We need to create standard software libraries to gather better data sets.
We need to pay more careful attention to behavioural economists. There's a huge amount to be done, but I think there's a lot of promise now.
The mainstream is resisting this. I think for several reasons.
One is it requires abandoning foundational assumptions that have been used in use since the middle of the 20th century.
So it's a pretty big shift in what has to happen, requires a very different toolkit,
a different set of skills, and it requires a different attitude about science.
It's a different scientific method.
And anybody who wants to learn more, I give the URL for my website where you can see the draught of the introduction of my book.
It's still very hot and in flux.
But if you want to take a look, you can look there.
And if you want to see any details about the papers I flew through at blinding speed, you can look on our website there.
Thank you very much. So that's a real tour de force, other questions for day.
We'd like to go first, Tim, if you could wait for the microphone.
I also remind you that this is being webcast, so when you ask a question, just be aware of that.
Don, I wanted to ask. I just actually picking up on your question whether there is any similarity between your and my field in the following sense.
You know, so I'm a climate physicist, climate modeller.
If you want to know if you know, to model the warming of the planet as a whole due to increase in carbon dioxide,
you can use very simple analytic models, which essentially are looking for new equilibrium.
It's a kind of and it's a reasonably elegant and simple approach.
But if you want to know what's happening regionally, in my view,
there is no substitute than actually modelling the whole climate system with all its complications and interactions and so on.
But there is a there is a subset of my field that views that to be somehow inelegant brute force.
It doesn't have that mathematical panache, that low water, you know,
semi analytic models have an I try to argue to these people that you that that view is very misguided and there is a role for elegant mathematics,
but it's not him. So I'm just wondering, does that play that just sort of sociology?
Does that play a role in your field as well as places to rest like brute force?
It plays a huge role. I mean, I think actually, if I, I should probably added that on here that there's if you if you grow up doing
mathematics and the whole thing you learn how to do is to solve equations,
then somebody says, well, actually forget about that and go to stimulate stuff on a computer.
OK. Fermi might have been, you know, Fermi was one of the first people to reprogramme the first computers,
but but most it's a whole different kind of ballgame.
I think there is an important question of how far can you get within our genius type, you know, aggregate model.
And I don't want to say that you can't do anything with an aggregate macro model, but if there are nonlinear interactions, then you may miss that.
So for example, if it turns out, if it had turned out that we're in serious danger of reversing the Great Atlantic salt current,
then our genius prediction might have been really, really wrong.
Because when you start looking at the structure, you have this big nonlinear effect that sends the system into a whole different state.
And so I think we have to worry about that.
And I think actually in the economy, we're seeing oscillations happening, the system that are going on all the time.
So it's a little bit like, you know, studying business cycles as opposed to saying climate.
I mean, business cycles might be more like weather as opposed to climate.
And so for weather, you really can't use an aggregate model, right?
Weather is all about simulating and fine scale. And as you know, it's limited by how good your computer is and how much data you have.
So but it's a very, very important point. Thank you.
How do you judge? I'm sorry if you could just wait for the microphone.
How do you judge whether or not your models are successful? Yeah.
So I'm a firm believer in empirical judgement of success.
Now that said, you, you often need an incubation period to really get success to happen.
And and there's a question of how much success can you get with small models doing this kind of thing?
I think you can get some and I think I mean, I could have, you know, had a bunch of slides of the things I would rate as successes.
And if you read my book, I spent a lot of time talking about that. But I think there's still relatively small successes.
We still have no model that can step in and replace the DSG model for, say, modelling the British economy or the world economy.
But so ultimately, it needs to be empirical and it should be asking, what can we predict and how what we do we predicted.
But to get there, we may need an incubation period where we say, Well,
this looks promising and that looks promising to come back to whether, you know, the first physical weather forecast on a computer.
We're done in 1950. But it was 30 years before they can beat subjective weather forecasters who just spoke their periods and said,
This is what I think the weather will do tomorrow. Now, once they beat them, they rocketed past them.
And weather forecasting is now far, far better than it was in 1980.
But it took 30 years of hard work and billions of dollars of investment to get the question there.
And then they. Thank you. I appreciate the question.
Looking at ecological and economic modelling together, what about political modelling?
For example, you might say a macroeconomic trend is income inequity.
And so these representative people that you're creating are going to be more bipolar.
So can you can you model an income inequity into into this complex?
You know what, one of the places that I, to be honest, I don't think this approach is not realised.
Its potential is in thinking about inequality. So it's very easy to just go back to the, you know,
and have as many different groups of people as you want to have match it all up against empirical data.
And now you have to have a model of how those people are going to behave as they as time marches along.
But you can simulate, you know, the economy and the feedbacks and and what different what effect different policies will have on inequality.
And I think this is one of the major unexploited strengths of this approach.
But unexploited because we really haven't done that very well. They're very simple tinker toy models that do show immediately.
I mean, you automatically in these models get inequality. You distort some agents off.
You let them go, boom. You get it. You get a distribution actually even looks more or less right.
You get a log normal out more or less right away. You can even get the tail on the log normal.
But but then if you really want to set it up so you can take a policy and say, what will the policy do to that?
And how will this distribution chain feed back into the global GDP and so forth?
That's a much harder problem and hasn't been done. OK, thank you.
I just wondered what what they might be able said something about the role of technology, because it seems to me that in your graph,
where you have areas of consistent increase in prices or cost, that in actual fact, the more the less you're able to code and automate knowledge.
So the more tacit the knowledge and the more interactive the area, the more you're likely to see increasing prices.
What are your thoughts on that? Well, I don't know.
OK. There is there is a long known fact that, you know, was called balls disease disease kind of named almost at the top of,
you know, what's the long term improvement rate of services versus, you know, manufacturing?
And and it is true that services generally improve at a slower rate than the more or high tech ends of manufacturing like computers.
So that seems to be an empirical regularity. It's interesting to try and understand why.
I don't think there are really good explanations for that, although, you know,
people kind of understand why Moore's law would have a 40 percent per year improvement rate for so long.
But but in general, it's this is a big unknown about how do these different technologies?
Why are televisions improving so much faster than hospital services?
And you can tell individual stories about some of these.
But but actually having a broad theory that tells us about this is one of the objectives of what we're trying to do in our group.
And I'd be lying if I said we understood at this point.
Thank you. I'd say you alluded to the idea that complexity economics recognises that the economy is more networks
than conventional economic approach is a bit like a local social network around investor decisions,
for example. So I was wondering if you could talk a little bit about the extent to which I understand you network dynamics is up to
scratch or what you would like to see in terms of development and network understandings that advance complex economics.
So can I sort of lived through the revolution in networks? You know, when I was a graduate student, there was a field called graph theory.
And, you know, I spent some time staring at books and graph theory, and it looked really cool.
And then people started realising less well. So the sociologists first, actually.
But then physicists jumped in. I'm good friends with several of them and began saying, Look, let's apply this to the real world.
We really need to take this stuff out of the mathematical textbooks and put it to data and develop techniques for things like understanding,
clustering and, you know, getting a clearer picture of descriptive picture of networks.
Then the sort of next step is to start thinking about dynamics on the networks,
like the example I showed you with yuki's model with, you know, the there are there.
We have thousands of households that are forming a network and there are some dynamics on that network.
Very simple because they just copy each other and adjust your savings rate.
But there's a dynamics and agent based modelling as it gets more serious, is essentially just getting more realistic about those processes.
Causation These models typically live on networks because you have a bunch of agents who are the nodes and the links or their interactions,
then they have several different kinds of links and they may move around in time and so on.
But that's really what's going on in an agent based model. And really,
what we're talking about as we develop the whole field is going from just the descriptive network stuff that was developed 15 years ago or so,
20 years ago now to actually dealing with dynamic models that live on these networks.
Now I actually have to mention that I built a model back and started in 86,
and it got published in about 1990, where we literally did have a dynamic network.
We had dynamics on the network. We call this mother dynamics. It was the origin of life.
And so I did put a pole in the ground deck there a long time ago.
The field has come a long way since then. Sorry, but we're going to have to bring things to an end with that, so I apologise for that.
This is the first and several in five talks we're having in this series called Evolving Economics or our next speaker,
which you've heard about several times, is Penny Mealy, and that will be in two weeks time.
And penny, we'll be talking about navigating knowledge, new new tools for the economy.
So please, could you join me for something we're thinking going again for a really?