Hello everybody. Today we’re talking to Doyne Farmer.
Doyne is Professor of Mathematics at Oxford
University in England and he co-directs the program
on complexity economics at the Institute for New Economic Thinking, also at Oxford.
In addition, Doyne is external professor at the Santa Fe Institute.
Doyne is a prominent scientist in so many fields that I’ve lost track.
But perhaps most prominently, he was a pioneer
in the study of dynamical systems and chaos, and in time series
analysis and in applying ideas from complex systems
to economics and also to studying the evolution of technology. And I’m sure I left something out.
But anyway, welcome Doyne. Thank you.
So let me ask, you’ve been studying economics for a long time, even though your background
is in theoretical physics.
In this unit, we’re studying self-organization in various systems
in nature and in technology. Would you say that the economy is a
self-organizing system? Definitely.
I would actually say that the thinking about self-organizing systems
came first from economics, when Adam Smith
wrote The Wealth of Nations, he marveled at
the invisible hand, such that people
could each pursue their own selfish purposes, and yet
somehow, things that are good for everybody emerge.
He didn’t put it in exactly those words, but that’s the basic idea.
And that was 1776.
In a certain sense, he was the first person to really articulate
what a self-organized complex system was about.
Do you think that
economics has anything in common with other systems in nature that we
look at as self-organizing systems? I know you’ve studied
the immune system and a number of other systems in nature.
What are the commonalities? Well, the commonalities are
that you have entities
interacting through interactions with each other
and blah blah blah, so
at that easy level there’s commonalities, but I think you can
view what happens in an economy
as a type of learning, very much like people in
machine learning often use -- it’s a self-organized kind of learning
where each individual is processing his or her information
and taking a certain set of actions
and communicating signals to other people through
crises, through consumption, through production
supply and demand, and the forces of those things are
in turn, altering the variables that everybody sees
and feeding back and so
I think that there’s actually
quite a lot of commonality. In fact, in simple
in some simple models in economics you ended up
with quantities like somebody’s wealth looking
exactly like probability in a Bayesian
updating algorithm. Dmitriy Cherkashin
and I wrote a paper on a simple model for
a kind of very, very simplified economy where exactly that happens. You
start out with players. Each of the players is making a bet on whether --
what the state of the world is
going to be in the next round. What is going to be the outcome
of a horse race or a coin flip, to make it really really simple.
It turns out that the wealth updating dynamics under a classic
paramutual betting algorithm
is exactly like that, with Bayesian updating.
So the system converging to a final state is just
like what you’d expect from some model
of distributed learning under a Bayesian
algorithm. So we’ve looked at some
ant colonies and we’ve looked at some
immunology a little bit, and in all those kinds of systems
biological systems, there’s adaptation, where we have the individual
components, but the whole system in some sense adapts
or improves to foster the
evolution of the entity, so does that happen
in economics? Does the whole system adapt in some sense?
Yeah. In the example I just gave you, the whole system is adapting because
the agent
with the strategy that most closely matches reality
is the one that, in the little game that I mentioned with all
the money -- in a more realistic model of ecnomy,
certainly the economy is adapting through time
We see it decade by decade.
As the economy changes, the set of goods we produce changes
and the set of the things we consume changes, constantly adapting
the conditions that are there and
in a co-evolving self-organized way, just like
biological systems. I think it was in
1990 that I wrote a paper called A Rosetta Stone
for Connectionism, where I discussed
in that paper models for autocatalytic networks
immune system, classifier system, neural networks, I kind of
waved my hands at game theory at the time because frankly I didn’t
understand game theory as well as I should have back then.
But were I to update that paper, I would definitely add game theory
in, and it fits -- in fact it can be viewed as
the general framework in which all those things sit.
And game theory is at the bottom
of most economic models, where you have actors that are
going about their own purposes, trying to do a little better
either competitively or cooperatively. There’s a
whole branch of game theory that’s cooperative game theory that says okay, let’s assume we’re playing on
the team, how do we best cooperate?
Within the confines of either kind of game theory, I think you could
very much build on those analogies and
those analogies in turn relate back to things like
everything from the origin of life to the immune system
at some level ant colonies and so forth, if you made them sufficiently elaborate
Okay, so let me switch gears a little bit and ask
how did you as a physicist end up studying economics?
And also, how can physics, or ideas from physics, contribute to
economics?
Well, I can answer that question in two different ways. One is, like many things
the course of my career is driven largely by chance and circumstance
When I was a graduate student, my friend
___ said hey, I bet we could figure out how to predict roulette using the laws of physics
and that set me off on a whole course for my career
where I became somebody whose career
has been based on challenges of trying to predict things that people say I shouldn’t
be able to predict. When I was doing time series
analysis I would give talks on time series
results we had on ice ages and sunspots and fluid
And some clown would always say, have you tried applying this
to the stock market? I got kind of sick of hearing that
and so I quit my job at Los Alamos, and started a company to predict the stock market
and I did that for eight years until we showed we really could predict the stock market
although not really with the same techniques I was using at the time
for ice ages and sunspots, but that’s a long story
and then when I emerged from my eight years in the wilderness
of commercial activity, I decided to go back in
to complex systems, and I figured I should make use of the domain knowledge
I’d learned about financial markets and economics that was
stimulated by the fact that I was reading papers on economics
which I’d gotten into deeper as a result of doing that I realized
I didn’t really believe the paradigms they were using at all, so using
Steven Weinberg’s statement that one should swim for
open water, that is, go to places where the problems aren’t solved
I thought, well, I need to do -- I want to do
complex systems, but in a domain where I can take advantage
of domain knowledge I used during my time at the prediction
company and so I went into economics.
Not quite realizing what fierce
social counterpressure
I would get from economists as a result of trying to suggest
that they should use different methodologies and ask different questions
and go about asking those questions in a different way than what they were doing already.
So what do you think physics can contribute to economics?
Well, I think it contributes mainly a different point
of view, a different epistemology. A different notion of
what an interesting question is, how
one should go about solving the question, and what a good answer to the question looks like
when you’e done. And I’ve been struck
in my now more than a decade of
interacting with economists
sometimes very cordially and sometimes a bit confrontationally
I’ve been just struck at what a difference
anthropology of the field makes.
How much I realized I got brainwashed to think
in a certain way when I was in graduate school and those guys
you know, that went to economic graduate school got
brainwashed in a different way, or maybe it’s also partly
a self-selection process, that
they selected themselves to do economic graduate school, I selected myself
to do physics. But I do think that
I do have several collaborations with economists, they’re very
productive -- I think thinking about the world differently is a good thing
and allows you to contribute providing you get other people to
listen to what you say when you get done.
What is this thing called complexity economics?
So complexity economics is using
the mindset, methods, and
point of view that we have in complex systems and applying it to economics.
And while it’s true that Adam Smith started the whole thing,
the intellectual evolution of the field
has substantially diverged from
that that is most represented in complex systems, which I think
is some kind of mixture of physics
computer science, biology, and cognitive
science, and other stuff. But economics is
not well represented.
In complex systems, we have a view
of the world that is you need to think about
coming up with rules for what the low-level interactions are
You need to think about how the entities interact with each other. You need to
capture the crucial elements of
the structural relationships between the entities, so
if you’re talking about an ecosystem
the nature of the environment, the nature of
the way the organisms live in that environment really matters
and you have to put enough realism in there to make it relevant to the real world.
And you shouldn’t be afraid of simulation.
Okay, that was my next question. Yeah, economists are
have not, in my opinion in general,
even come close to realizing the power of simulation
and because of the emphasis on
mathematical proof and
they’ve made in general excessively
stylized models that fail to capture key elements
of the real world. So that's
the sort of difference in key elements
at least to the difference in intellectual viewpoint that I think I have
with most of them. Not all of them, of course. There are many economists who are
a minority of economists who think differently. The 95%
that follow the mainstream.
So you’ve argued strongly for the use of agent-based modeling
in economics. Can you say a little bit about why you think that’s a good approach?
I think just for the reasons I gave
It’s at the end of the day imperative that
the economic agents be allowed
to exist in a world that contains the key institutional features
that are so important in the real world.
So if we want to understand the crisis
of 2007-8 and on, that we’re still kind of floundering
in in some way, we really have
to have institutions like banks
that can fail, can default
we have to have housing markets, we have to have those
things in there. The dynamic stochastic equilibrium
models that were prevalent in 2007-8 did not have those
features. There are -- it’s a
very serious effort in economics now to put them in, but
and while I think
those models may play a useful role, I think to really make those models
realistic in key ways, we really have to go with agent-based modeling.
So do you see agent-based modeling as
kind of a placeholder for more advanced mathematics
we’ll eventually be able to do without having to use simulation, or is it something that
is just essential, will always be essential?
I think it will always be essential, but
let me try and clarify my answer. First of all,
existing economics uses quite fancy mathematics. So it’s not about
how fancy the mathematics is. It’s about whether
the mathematics that we’re able to do is really relevant for the key features
of the world that we’re trying to capture. Okay, so replace
what I said with more relevant mathematics.
I think there’s a cycle that one goes through, and you should go through, where
you alternate between making
agent-based models that may have some level of realism
in them, and then stepping back and then asking what are the key features
of these models, can we make simpler models that capture
the essential features that give us a clearer
understanding of what happens. I think one should go back and forth
between those viewpoints. You may, in the course of formulating
your simplified model, realize something that you’re missing in your agent-based model
and put it in there, or put it in there in a different way. You may
extract the essence of what’s going on and change the way you think about what’s going on in your agent-based model.
It’s a little bit like if you look at
the study of fluid turbulence today. Fluid
turbulence is a field where there’s a lot of alternation between
experiements, simulations, and mathematical
simplified mathematical models. And all three of those
fields learn from each other. We, by the way, are
also trying to incorporate experiments as part of what we do
in the software that we’ve written in the
crisis project, we have the ability to run economic
experiments and unplug any of the algorithmic agents
in the model, replace that algorithmic agent with a person or
a set of people, and see what decisions they make, and
in the context build all the other algorithmic agents in our model
setting. So we have a group in Amsterdam that’s actually running experiments
along those lines, led by ____.
So we’re trying to do everything
we can try to calibrate the models in the most realistic
way possible.
One critique that has come up of very detailed agent-based models
is that you can keep throwing stuff in, more and more, until
you match the data, the empirical data
and that you get kind of hypercycle
not hypercycle, what do you call the
Right. Simulation that represents reality
and detail, but doesn’t really give you any clear abstractions
about what’s really going on. Well,
let me just say, if we could do that for the economy, I would say hallelujah
glory, brother, I would be tickled.
Because we’re far from being able to do that, and if we could do that
then we could run things at double speed and
run policy studies and make conditional
forecasts that we can’t do right now, and it would be extremely useful.
Secondly, if we were able to achieve
…. in 2001, and asking what are the key features
of our model that really give us the essence of the results
and in the course of uplugging those components we would learn a
hell of a lot about the real economy. And we could
replace things with stylized, dumbed-down versions, we could do all
kinds of experiments. So if we could ever achieve that
start point, then we would be most of the way there
in my view. Okay. Let me ask one last question, which is what are you
working on that you are really excited about these days?
I think the thing that, if you’re talking about
this month or these days,
Probably the thing I’m most excited about
is a project that I’m working on with James McNerney and Francesco Caravelli
where we’re trying to think about the
economy as a -- to
let’s say carry over a lot of ideas from ecology
and think about the structure of the economy
and try to understand something that we've
seen anecdotally, which is that some things
are -- don’t seem to be getting systematically cheaper
over time, like natural resources. Coal, oil, natural gas
iron, copper,
lots and lots of things that we’ve looked at that
are natural resources cost about the same now that they did
in 1900. In contrast
computers as we know are way, way cheaper,
by a factor of a billion. Than they were in 1900?
Than they were in 1900.
Not to mention performance.
Photovoltaic cells have dropped by a factor of
100 in price since they were introduced in the 50s
in a very steady and systematic way
Agriculture, food has even
gotten dramatically cheaper.
But some of these things have stubbornly, like oil
have stubbornly stayed at high prices. We have a theory for that
that we think it’s driven by the fact that the economy
grows by adding trophic levels
and we should really think of them like, much like food chains
in ecology and in some areas of economy these
trophic levels are quite deep, and so we
have an idea, we have a theory -- maybe I shouldn’t give away the details --
but we think that using those ideas we may be able to
explain why some things like computers improve a lot
faster than other things like oil prices
and we think that this is important in thinking about
how we ought to deal with global warming, because
it suggests that some energy technologies like photovoltaics
are on a trajectory where their prices are getting cheaper
and cheaper, whereas others like coal or
nuclear power
for reasons that we don’t understand,
coal we understand. Nuclear power we don’t. Don’t seem to be on such a trajectory.
So we think that if we’re placing bets on the future, we should place heavier bets
than people are placing on solar power.
Alright well, great. Thank you. This has been a really wonderful
conversation. Good. Thank you, Melanie. I had fun, too.