With the financial crisis and the blame being heaped onto models I wonder if there was any future for models or what future models might look like.
One possiblity might be the advance of agent based modeling which has not had made much headway against DSGE models or even heterogenous agent DSGE models. Leigh Testfatsion has been a contributor to this field for a long time. Tyler Cowen maligns it:
What's the important innovation behind intelligent agent modeling? To introduce lots of arbitrary assumptions about behavior? Greater realism? Complexity? Considerations of computability? Learning? We already have enough "existence theorems" as to what is possible in models, namely just about everything. The CGE models already have the problem of oversensitivity to the initial assumptions; in part they work because we use our intuition to calibrate the parameters and to throw out implausible results. We're going to have to do the same with the intelligent agent models and the fact that those models "sound more real" is not actually a significant benefit.
What can be done will be done and so people will build intelligent models for at least the next twenty years. But it's hard for me to see them changing anyone's mind about any major outstanding issue in economics. What comes out will be a function of what goes in. In contrast, regressions and simple models have in many cases changed people's minds.
But Alex Tabarrok is more optimistic:
I see bringing experimental economics and I-A modeling closer as an important goal with potentially very large payoffs. Here, for example, is my model for a ground-breaking paper.
2) I-A replication of experiment (parameterization)
3) I-A simulation under new conditions
4) Experiment under the same conditions as 3 demonstrating accuracy of simulation
5) I-A simulation under conditions that cannot be tested using experiments.
I am also more optimistic since reading about swarm models. (See an old post.) I'd complement Alex's approach with the advent of greater amounts of data that is becoming more available. For instance, the following claims are made via Andrew Gelman:
1. More data beats better algorithms (Some agreement.)
2. The End of Theory: The Data Deluge Makes the Scientific Method Obsolete (Dissent and agreement within link.)
3. Some convergence in using priors (intuition), large databases, visualization and modeling language.