So my paper is about computer simulations for Austrian insights and there is, I before I start I want to make just a fast question for you because every time I try to present a paper I really have no idea on how many people in the room are confident with these agent based simulations so is there anybody who knows what they are?
Yes, it's pretty good, okay.
So, and recently there's been quite a vivid debate
if evolutionary game theory and game theory
and these kind of simulations fit
with the methodological principles of Austrian economics.
Although the default answer, of course,
is no for, I'm sure you know most of the reasons,
but we can still give it a try, I think.
And despite all this debate, I think there,
or maybe there are, but I don't know them,
there are no actual agent-based simulations
of made just for the ostriches,
like some kind of custom simulations for them.
So I tried to make one, and I chose to model
and the other theory of Carl Menger on the origins of money,
because I think it's quite simple in a way
and it's really, and it's interesting and fun.
So first, what are these, what are these models?
And they're also known as artificial societies
or bottom-up models.
And they, basically they, what you do,
So you simulate the interaction of autonomous individuals.
They are used both in the natural sciences
and in the social sciences, like in biology,
but also in physics, and of course in economics.
And so they're interesting when you want to study complexity.
So if we think, this is, I don't know if you can see them,
and the schooling of fish, which is complex behavior,
which emerges from, of course, fishes are not that smart
and there is no benevolent dictator
who makes the fish swim like that.
And of course, they do it and it's an advantage for them
to defend against predators.
The fishes are not aware of what they're doing, they just swim around according to some very simple behavior rule, and what emerges is a complex behavior, like schooling.
And you can use a simulation to study all these kind of complex things like traffic jams or how a crowd will behave.
So whenever you have a lot of individuals,
and you want to see what emerges from their interaction.
So how do these models work?
And well, basically, what they are, according to me,
if I have to tell you in one minute,
there are a way of doing thought experiments.
There is nothing mysterious about them.
And I think that thought experiments
are one of the main tools in economics,
and especially they're very precious
for the Austrian economics.
And I think, for example, even the rotating economy of Mises,
that is a thought experiment.
See, ask us to imagine what happens in such a kind of economy.
So if we go to an easier example,
and we go to Adam Smith,
how the division of labor came into existence.
He tells us to imagine a small village
in which people have different skills.
So when we read this passage,
we really try to imagine this village, I think,
and one guy is better at fishing,
another guy is better at hunting,
but we don't really imagine a big city, yeah?
We can't, we can't say, okay, now I imagine 10,000 people
or 100,000 and each of them is better doing something else
and then we can't deduce the consequences in our mind.
So maybe, what do the agent-based models
is just to help our thought experiment
with a computer.
We could do them with paper and pencil
and that's how Thomas Schelling did it, in fact,
back in the 70s.
So some appealing features of these kind of models
are that agents are heterogeneous.
Okay, so we can make each agent behave differently
and have different characteristics.
and then agents are not olympically rational
and it's not interesting if we make a simulation
which agents are gods and like ants or fish
and it just doesn't make sense
and it's probably very hard if not impossible
for the computer to manage all these god-like agents
and then the interaction and knowledge of agents are local
They only know what's going on around them.
And what is also very interesting, I think,
for Austin Economics is that the causal mechanism
can be followed step by step,
meaning that the focus is on the process
rather than on the final equilibrium.
So for example, here we have ants, and as you know,
ants find out the shortest path to the food,
and they don't do it because they are very smart
or because they are aware of what is the shortest
but they just, it's an emerging pattern from there.
So it's like a sort of swarm intelligence.
So all these features I've been talking about
should be, I think, appealing for Austrian economics
and I think they really fit into
at least some of von Hayek insights.
Well, first we have a very strong commitment
to Methodological Individualism.
So we can, there is nothing but the individuals
in these kind of models.
And when we run a model, we usually aim at some kind
of rational reconstruction, which will be
some conjectural history, or principle explanation.
We don't end up with a quantitative result.
We will not say that we need 115 ants,
which go after two bagels to have something interesting.
Well, we can say that we have some interesting emerging
pattern given this simple behavior rule, for example.
And they are deductive, yes.
There is nothing but what we put inside the model.
The point is as we're not, they are interesting
because we put what we want inside,
but it's very hard for us to predict the outcome
because we have a lot of agents interacting.
And so they are strictly deductive
and I think that fits with Austrian economics.
And if we take a step forward
forward and we imagine how a system like that evolves.
I think that also fits with the Austrian economics
and the thought of von Hayek,
as we don't have ultra-rational individuals,
individuals act conforming to norms,
for example, social norms,
and these norms are the result of
a complex evolutionary process.
and we can model that easily in a simulation.
So I chose to give it a try with Menger's Origins of Money
and it's good that we talked about it yesterday.
Makes the talk easier.
So Menger thought that money emerges spontaneously
in society and it was not created by the state.
And his explanation goes on like this, first he notices how different goods are saleable in different degrees.
And suppose that you go to the market and you want to sell one good, which is not highly saleable.
So what is reasonable to suppose you do? First you try to sell it and to get what you want.
But as your good is not highly saleable, maybe you can't, and if you can't, you might look for a good which is more saleable than yours, so you can exchange it for it, and then once you have this highly saleable good, you can look for what you want, because it's very likely that the guy who has what you want, wants what you have.
so it makes the double coincident of interest much easier and so if you if
this is a plausible strategy and I think it is then we can we can imagine a chain
reaction which is triggered because as is it clear so far yes okay so as more
Whenever individuals perceive a good highly saleable, this good is going to be more traded,
but this enhances the saleability of the good.
So Menger says after a while, one or more goods are going to be universally accepted
and they're going to be money, they're going to be a medium of exchange.
These people are going to accept this good in exchange for their goods, despite the fact
that they like it or not, and it is a matter of fact that money emerges spontaneously in
many occasions.
For example, we all know that in prison camps cigarettes have been money, and even in post-World
War II Germany, cigarettes, chocolate and tobacco were money on the black market for
years.
and so this is some kind of empirical validation of Menger's insight.
And okay, so how does simulation work?
Very fast.
We have a bunch of agents, they're all different.
They bring to the market some goods and they have different preferences over them.
And there are no utility functions.
That is why the simulation runs so slow.
So, there are no functions, they only have a string of preferences in their mind.
And they try to exchange them, and we will see what happens.
So during each round of simulations, agents pick a partner and propose a deal.
So they will first try to sell what they value least in their stock, and they will ask for
for the goods they want most.
And, well, if their partner has what they want
and wants what they have, the exchange takes place,
no problem, but what happens if it doesn't occur?
For example, the other agent might not have
the good we want, then if we employ Menger's strategy,
we will ask, instead of what we want,
a good which is highly saleable.
And in the simulation, I modeled that as the good
which is most traded in agent sites.
So if agent site is two squares, they're going to,
the agents keep track of all the exchanges that take place
and they're going to ask for the most traded good
locally, okay, not on the overall population.
And okay, so we might have a look at the simulation
very fast.
So, first, we might see what happens if we don't employ mangers inside.
We just look for what we want most and try to get rid of what we don't want.
So agents cannot trade in the whole population.
They can just trade with their neighbors.
Okay, so now we start, and we see now there are 121 agents I think, because otherwise we wouldn't have time to see what happens really, and we see that they can't really trade, they don't, the double coincidence of interest is not met, so they're pretty sad, huh?
So what happens now if we trigger on what Menger suggests us, so now the color of the patches in the black square which, okay, colors indicate what agents think is the most traded goods, so sameness of color means the sameness of perceived most traded goods.
So, pink agents all think that pink wood is the most traded and are going to accept it in exchanges.
So, as the simulation goes on, we see we have a lot of exchanges going on and locally we see areas growing with agents thinking pretty much the same thing.
It obviously, money is a good thing for making business,
but we have to coordinate and agree on which good is money,
otherwise it doesn't work.
If we all disagree on what is money,
then simply doesn't work.
So agents now can only trade with their immediate neighbors
and it wouldn't make sense for them
to coordinate all on one good,
simply because maybe in the right hand corner,
there is no such good.
So if I think that dollars are the medium of exchange,
and there are no dollars where I live,
and I keep asking for dollars in exchange of my goods,
I don't make very much business, okay?
So now, after a few ticks, well, they did pretty good.
Almost 5,000 exchanges took place.
Okay, now we, the other insight of Menger was about how this behavior will evolve, so social norm should outperform the basic mechanism asking what you want.
So now, red patches will be people behaving in a direct barter way, and the blue one will
be agents who employ Menger's strategy.
So looking for the most traded goods and then exchanging it afterwards.
And we see that pretty fast, they take over.
So agents change behavior, they look around them, and if the agent next to them is doing
better than them, they change their mind, they imitate his strategy.
And very last thing, now we allow agents to trade not only with their immediate neighbor,
we just extend their range of trade a little bit, and we see what happens.
and now they're all in play in Menger's strategy and we see that very fast, we have big areas where agents use the same good as money, okay?
And one of my hopes coming here was, I think, why should we do this kind of stuff with the agents on Austrian economics?
I think first it's fun, and yeah, I have to be honest.
They're fun.
And I think they add some strength to the arguments.
Like thought experiments typically, it is deductive,
but we're still not sure what is the outcome
of a thought experiment.
And then I think they can help discussion among scholars.
Because if this is a neutral language, we can say,
that can be employed by mainstream economics and if it also fits with Austrian economics
then we can really discuss and confront theories, okay?
Thank you for your attention.