Finance theory continues to develop beyond the traditional
academic precincts of rational expectations and efficient
markets, with the latest innovations coming from psychology, neuroscience and
J. Doyne Farmer is making his mark by thinking outside the
box as well. Farmer is a professor of mathematics at the
University of Oxford and a director of the program on
complexity economics at the Institute for New Economic
Thinking. Additionally, he is an external professor
at the Santa Fe Institute, a private, nonprofit research
organization. But Farmer was originally trained as a
physicist. In the 1980s he worked at the Los Alamos National
Laboratory, home of the Manhattan Project, where scientific
luminaries like J. Robert Oppenheimer and Richard Feynman once
plied their trade. At Los Alamos Farmer founded the Complex
Systems Group in the theoretical division.
In 1991, he co-founded the Prediction Company, a
quantitative trading firm about which University of Chicago
professor and architect of the Efficient Market Hypothesis
Eugene Fama said: Prediction Companys chance of
success is not zero, but close to it. The firm would
prove Fama wrong, however, and was purchased by Swiss banking
giant UBS in 2005.
At the Santa Fe Institute, Farmer is looking for laws in
financial markets that can help explain what drives the
stability and performance of the banking system.
Over his career Farmer has collaborated with prominent
academics such as MIT Sloan School of Management finance
professor Andrew Lo whose Adaptive Markets Hypothesis
attempts to reconcile Famas hypothesis with the realities
of human behavior through the application of evolutionary
biology and Yale University economics professor John
Geanakoplos, a noted contributor to the general equilibrium
branch of economics.
Currently, Farmer is working on a different method of
understanding market behavior: agent-based modeling
simulations of financial market participation by investors.
This work requires gathering and coding an enormous amount
of data and creating a library of information of sorts from
which to simulate interactions of agents in financial markets.
Farmers background in dynamical systems theory,
time-series prediction and chaos theory coupled with his newer
work with complex systems and financial economics has aided him
in his effort to simulate and analyze the individual actions of
and interactions between agents on a marketwide basis.
Farmer is also the scientific coordinator of CRISIS, a
large-scale European Union project to build an agent-based
model of the banking system and the real economy.
Institutional Investor Reporter Ben Baris spoke
with Farmer about his work and agent-based modeling.
Institutional Investor: You were trained as
a physicist. How did you come to study financial
Farmer: The basic principles of how you
make a good model are not that different in economics or
biology. Time-series forecasting gave me a lot of exposure to
real-world problems in a lot of different domains, so it means
I know a little about a lot.
What is the problem with the way most economists
They are still very closed to any way of thinking about theories for the economy that dont
satisfy the straitjacket of the neoclassical approach. A lot of
things need to be brought in from outside economics
ironically, a lot of things from physics. Physics is a very
empirical field, where any crazy idea can be proposed, and if
it works, it will get respect; whereas in economics,
theres a view that all theories have to come from a
certain set of basic postulates. If your theory isnt
about individuals selfishly maximizing their preferences, then
its not even considered a theory in economics. I think an awful
lot of what happens in the economy doesnt have anything
to do with individuals selfishly maximizing their
Assuming this is where complex systems come into
play, can you explain what they are and how they
What we call complex systems the brain, society,
immune system, ecosystems are composed of many small
parts that interact with each other. The key thing that makes a
complex system is that it displays emergent behaviors that
arent obvious from the behaviors of the components
themselves. A neuron is maybe in some sense a complex system
all by itself, but to really understand how a brain works you
have to really understand what happens when you have billions
of interacting neurons and have to understand the emergent
properties of those interacting neurons.
So similarly, complex systems are relevant in thinking about
what the economy does, as an emergent property of the
interaction of all the actors.
The second thing about complex systems is that as a
discipline, its the belief that there are common
properties to complex systems that exhibit themselves in very
different arenas. So maybe an economist can learn something
from listening to a neuroscientist, and Im not just
talking about people who are trying to understand how traders
get nervous, but from thinking about the way the brain is
self-organized. Maybe that tells us something about the way the
economy is self-organized.
But what is there to learn if markets are
I think efficient markets is a subtle question
thats misunderstood by both sides. The way I would put it
is that markets are efficient at first order and have to be
inefficient at second order, and the idea behind that goes all
the way back to [noted University of Chicago economist] Milton
Maybe put differently, if there were just slam-dunk
investment strategies, everyone would just pile in and they
would disappear. And so what happens is there are none. There
are strategies that involve thought and processing, skill,
intuition, hard work, data gathering, computer processing.
Its not free money beating the market.
The mistake that finance has made is only looking at that
first order and almost neglecting the question about who are
those investors that are taking advantage of these
inefficiencies and how does the nature of that game affect the
way markets behave, because a lot of things that markets do
have to do with deviations from efficiency.
What then would make a market
In finance you have to differentiate between informational
efficiency making profits and arbitrage and
allocative efficiency; that is, is the market fair, is it
creating as much public welfare as it could? Its pretty
darn inefficient in that regard but fairly efficient in the
other. Under general equilibrium theory those two are the same,
but thats under a whole bunch of totally unrealistic
Successes are out there, and I think they illustrate this
paradox because while firms make a lot of money, they do so
typically in a situation where the statistical fluctuations are
still significant, or they, in modern times, do something like
high frequency trading where they have to put a really big
investment and a lot of serious effort into computer
What do you think about the work done by the
behavioral finance field?
I completely agree with everything theyve done in
pointing out whats wrong, but I think theyre stuck
in trying to understand how to fix it. The problem is the
theories they set out to make are too close to theories that
people doing rational expectations make.
A lot of economics is driven by structure rather than
strategy. If you want to think about a limit-order book
its important to think about how the limit-order book is
set up and not imagine that you have a rational being
thats interacting with it. The problem is rationality is
hard its hard to understand what a rational person
would do. On the other hand, if you go look at what people
really do, you can make simulation models that simulate the
real world. Thats one of the main thrusts of agent-based
models we think agent-based models are the natural
partner of behavioralism, and that to make [behavioralism] into
a positive theory, the behaviorists need to completely change
their approach to building models and take advantage of the
freedom that you have once you can encapsulate behavior as a
computer algorithm and take a bunch of agents who have
different behaviors and just simulate their interaction.