I'm here with Doyne Farmer,
who is a SFI external faculty,
and he's also a professor at Oxford University in England.
Doyne, can you tell us a little bit about what your position in Oxford is?
Well, I'm in the mathematics,
I'm a professor of mathematics,
and I'm also the director of complexity economics
for the Institute for New Economic Thinking
at the Oxford Modern School.
Okay, that's good!
So, tell me about how you got into the
field of complexity, what brought you here.
Well, complexity is something I was
thinking about when I was in college, and
you know, couple of friends of mine and I,
other physics majors, would go out and sit on the lawn after physics class,
and instead of talking about physics,
we were really talking about complexity,
which didn't have a name at the time,
and we didn't think anybody could study
stuff like that, and
then when I was in graduate school,
studying physical cosmology,
I learned about chaos and dynamical systems,
which seemed like the closest
thing you could get at that time,
so I dropped cosmology for that,
and then I discovered that there were
other people who were interested in
thinking about similar things, and I
ended up at Los Alamos, where one could
think about such stuff, ironically,
a weapons lab is a place for intellectual
freedom to cross disciplinary boundaries
and think about complex systems.
And then things evolved, I actually
got asked in 1988, to start a group,
in the theoretical division, the group was basically for the people that didn't
belong in all the other groups because they were doing funny things,
and we at the time thought about calling it the Likened Intelligence Group,
but we decided that was too constraining,
and so, we decided complex systems
was a good, you know, general purpose
thing where we could do what we wanted to,
and we were very interested in emergent
phenomena, and so on.
And it also happened around when the
Santa Fe Institute got going, and the name caught on.
Okay, so your background was in physics,
you did chaos, dynamics, and so on,
how did you get into economics?
Well, so that's another curious coincidence,
one of the things I always viewed as my
day job was my ability to predict stuff.
And I got into that because of Roulette
when I was a graduate student in physics at UC Santa Cruz,
I teamed up with Norman Packard,
and we used physics to beat the game of Roulette.
And then chaos always seemed like,
well you should be able to take advantage of chaos to make better predictions,
even though it's about not being able to predict.
And so, in 1987, I wrote a paper with
"Sid" Sidorowich, on basically,
building nonlinear models to make
better predictions in the presence of low-dimensional chaos.
And, as a result of that, we started predicting lots of stuff,
like transition fluid flows, and ice ages,
and sun spots, so we had a collection of
things where we were making pretty good
predictions. And everytime I would give
a talk about this, some clown would say,
"Well have you tried applying this
to the stock market?"
I got tired of hearing that, and it happened that
I'd also been at Los Alamos for almost 10 years,
and they were going to give me a nut dish
to commemorate my 10 years of service,
and that freaked me out, and then I decided I needed to leave,
so I quit and started a business
to predict the stock market.
So that got me into reading, into actually
trying to understand more about finance,
which has to do with economics,
and then after 8 years, I had a prediction company,
again with Norman Packard.
Once I met my financial goals,
and I felt like, okay,
what am I going to do now?
So I decided to merge my background in
complex systems, with what I'd learned,
my domain knowledge about financial markets
and economics, and then I came here
for 12 years as a faculty,
and worked on that here.
And then went to Oxford to do Economics And then I went to Oxford to continue
so, I'm actually doing the same thing for more than a decade now.
Wow. So, I don't know if you remember,
but when you first got into the field
of economics, and started like,
looking into those models and so on,
is there anything that particularly
struck you as surprising, or weird, or...
Well, I think what struck me as surprising
or weird is just how differently
people from different disciplines,
think about the world, and that the
paradigms are different, the whole middle
constructs, that underlie, that are viewed
as the foundational way people view the world, are different.
And really, this Could you give an example?
Yeah, you know a complex systems person would naturally start thinking about,
"Okay, let's break this thing up into its pieces,
and let's do a computer simulation,
and let's look for emergent phenomena."
That's sort of the paradigm of complex systems now, I would say,
or at least one way to view it.
In economics, it's, "Well let's think about,
let's try and understand what the beliefs
are of all the agents, let's try and
understand what their goals are,
their utility, their preferences, and then
we'll make a model in which we assume
that they optimize their preferences,
subject to their beliefs, and we'll assume
that there's a fixed point where that happens.
So that's the fundamental model in economics.
It's very different than the one in complex systems.
So the one in complex systems doesn't
assume a fixed point Doesn't assume a fixed point, it assumes
that simulating stuff on a computer is just fine,
if you can get some analytic results, great,
Economics, if you want to publish in a top journal,
you'd better get some analytic results,
at least if you're doing theory,
so it's just a different mindset,
and a different notion about
what questions you should ask,
how you should go about answering them, what kind of answers are acceptable,
So what counts as true science Yeah, what counts as good science
So you know, you said you've been doing this field,
which I think a lot of people
refer to as "Complexity Economics" now
for a long time, Yeah
Have you seen that shift that those views
I think it is shifting,
I think it is shifting.
Economics is changing,
economics is a field where the
scientific method is evolving in time,
and I've seen it evolve in my own lifetime,
in the seventies, the hot stuff
would have been models with perfect rationality,
and by the nineties,
people were beginning to start to think about behavioral effects,
and take some psychology into account,
and even do experiments.
Early experiments back in the sixties,
but that was very low-brow stuff at the time.
Those things that broken through,
so economics is changing, it's becoming much more empirical,
which I think is a really good thing,
and there's a little, some cracks in the
door towards openness, toward things like
agent-based modelling,
though so far, I would say that
it's almost impossible to get a paper
based on an agent-based model,
in a top economics journal. Still, Still.
What effect did you think the
financial crisis of 2008 have on the field of economics?
So it's had a big effect on the field of economics, since 2008.
It shook the profession up.
You know, Duncan Foley, another SFI
external faculty, and I, wrote a piece
in Nature in 2009, where we were being
a bit snoddy, I'd have to say.
But we said that the existing set of
theoretical models were not even good enough to be wrong.
And what we meant was that,
if we look at the dynamic stochastic general equilibrium models
that several banks were using to try and forecast
what was going on through the crisis,
those models didn't have the banking
system in, the possibility of default was not there,
all of the machinery that the
crisis was about was not even modelled,
you weren't even testing the theory
to see if it was right or not.
Now since then, things have really changed.
People are struggling to try and create
models that have these elements in them.
I'm still critical of what's being done,
because the models continue to be,
in my opinion, too simple structurally,
to capture the key elements of the economy
that need to be captured.
Networks weren't part of the modelling.
They aren't actually in the production of models, though
though they have been studied more theoretically
by economists. And there are lots of other
structural things that are not present
in the models, and that I think are going
to be very difficult to put into
the current kind of models.
And I think the fundamental assumption of equilibrium,
that's built into existing economic models,
is simply inappropriate.
You have to let go of that
to accurately capture the collective
oscillations that happen in the economy
and the endogenous movements that I think drive things
like the business cycle.
And so there has to be a key change that has to happen
in the fundamental way models are put together,
in my view, before they can start to really go beyond
what's being done now.
So what scientific questions really grabs you these days?
What are you focusing on?
Well, I'm really focusing on two things.
One is trying to make agent-based models
that do give us more accurate representation to the economy,
and trying to do that in a very quantitative way.
We're in the process of putting together an open-source project.
We're still trying to finalize an aim for.
I'm sort of leaning towards an economic simulation platform,
but my colleagues and I are still debating this,
so we're going to have an open-source project,
where anybody can put their agent-based models,
anybody can contribute to building new models,
we're going to try and build the right set of lego blocks
to put together economic agent-based models.
And we're trying to do this in a very
structured way with a lot of focus on
calibration and validation of the models.
The other thing I'm really interested in these days is
the evolution of technology,
and in particular in creating a evolutionary model of technological change.
Evolutionary in the sense of Darwinian evolution?
In the sense of descent with variation selection,
which I think is the fundamental driver
that's shaping the way technology is,
the broad principles, I think,
are shared with biological evolution, in the sense of Darwin,
the details are all different.
So, I think it's a wide, open field
where a lot remains to be done.
You know, technology is
an ever larger part of our world,
that I think is just crying out for more fundamental understanding,
both for practical reasons,
because we're facing something like climate change,
well, technology made the problem,
technology is our only way out of the problem.
And the right technological bets
are going to make a big difference.
And so I'd argue that nuclear power
for example, is not the right technological bet,
okay, not just because of all those safety considerations and so on,
but because there's no history for progress in nuclear power.
Cost of nuclear power now is the same as it was
in the 1960s, or higher actually.
Whereas other technologies like
solar energy have been dropping quite fast,
and there are underlying reasons for that
that we need to understand better,
there's very interesting regularities
in how these changes happen,
all have policy relevance.
So that's one side, and the other side is,
I think it's fascinating to,
for the unity of knowledge to try and put it together with Darwinian evolution,
and try and understand, other similarities and differences.
So, going back to your focus on prediction, Yeah
do you think it's possible to develop a predictive model of technological evolution?
Well, we have a paper in research policy,
called how predictable is technological progress,
and we show in there that just simple extrapolation models
provide predictions whose accuracy can be quantified,
that actually are good enough
to be quite useful for making policy judgements.
Like, for instance, if you want to ask the question,
if you say, "Well solar energy has been improving over the last 40 years,
what's the likelihood it will continue to improve at a similar rate,
over the next 20 years?"
At which point, it should be quite a bit cheaper than coal,
in terms of electricity price.
And, so we can put a quantitative answer on that question,
and know that the odds are that it will be more expensive in 20 years than it is now
are a few percent.
The odds for nuclear power,
from the same analysis,
are about 50 percent.
And so, it's just using statistics.
I think you could also,
I think you could do better, ultimately, by having a proper theory
where you really understand why things work.
The prediction I just mentioned is made by just analyzing lots of technologies
and looking at their patterns and improvement
So it's more like looking at data to build statistical models,
rather than having a real model of the mechanisms.
That's exactly what it is.
But I think one can go beyond that,
to make better models,
and it will also give,
by giving you a more fundamental understanding of what's happening,
provide deeper insight in lots of other ways.
Alright, well, that's really interesting.
Thanks a lot, appreciate it. You're welcome.
Thank you. Thank you.