http://physicsoffinance.blogspot.com/2013/04/what-you-can-learn-from-dsge.html
It looks like they're finally beginning to penetrate the fog of DSGE.
"Does anyone out there know if there is research exploring this matter of when or under what conditions the representative agent approximation is OK because people DO act independently? I'm sure this must exist and it would be interesting to know more about it. I guess the RBC crowd must have an extensive program studying the empirical limits to the applicability of this approximation?"
I lol'd
I am thrilled to learn that a GMU PhD student is the first to consider alternatives to rational expectations in macro!
I am also glad that the true experts in human behavior (physicists) are on the case and developing macro models with psychologically realistic agents, such as we see in... econophysics.
However, the implication that physicists or other natural scientists would never deploy the analytic equivalent of a representative agent when studying physical processes is not quite correct.
Mean Field Theory:
In physics and probability theory, mean field theory (MFT also known as self-consistent field theory) studies the behavior of large and complex stochastic models by studying a simpler model. Such models consider a large number of small interacting individuals who interact with each other. The effect of all the other individuals on any given individual is approximated by a single averaged effect, thus reducing a many-body problem to a one-body problem.
The ideas first appeared in physics in the work of Pierre Curie[1] and Pierre Weiss to describe phase transitions.[2] Approaches inspired by these ideas have seen applications in epidemic models,[3] queueing theory,[4] computer network performance and game theory.[5]
A many-body system with interactions is generally very difficult to solve exactly, except for extremely simple cases (random field theory, 1D Ising model). The n-body system is replaced by a 1-body problem with a chosen good external field. The external field replaces the interaction of all the other particles to an arbitrary particle. The great difficulty (e.g. when computing the partition function of the system) is the treatment of combinatorics generated by the interaction terms in the Hamiltonian when summing over all states. The goal of mean field theory is to resolve these combinatorial problems. MFT is known under a great many names and guises. Similar techniques include Bragg–Williams approximation, models on Bethe lattice, Landau theory, Pierre–Weiss approximation, Flory–Huggins solution theory, and Scheutjens–Fleer theory.
In another post Buchanan cites a recent working paper with many authors of whom the leads are John Geanakoplis and Robert Axtell, with some of the GMU PhD students mentioned in this one on board. This is not just some GMU grad students or nobodies at the SF Fed.
As for the claim that DSGE models have had hetereogeneous agents for some time, this is technically true, although the ways that this has been done have been extremely simplistic, essentially minor extensions of rep agent models.
The physicist's book.
Now open to nationwide ridicule.