Agent-Based Modeling: Promises and Pitfalls
By Arnold Kling
Which of the following impediments to economic adjustment do you believe to be the most important?
a) the cost of establishing a new enterprise
b) the cost of integrating new workers and equipment into an existing enterprise
c) the cost of adapting physical and human capital to new circumstances
d) the cost of whiting out an old price list (menu) and updating it with new prices
If you answered (d), then congratulations–you have shown your New Keynesian bona fides. If you answered anything else, then congratulations–you have shown common sense.
Which brings me to agent-based modeling. In the paradigm of Patterns of Sustainable Specialization and Trade, an important element of economic activity is what I call “discovery.” Entrepreneurs and workers constantly have to discover how best to adapt to changing circumstances.
I have suggested that this is not well described by a Walrasian system of equations. The Walrasian system, or the “monetary Walrasian” system that adds a money demand function, is like the proverbial lamppost that the proverbial drunk looks under to find a watch that he dropped somewhere else.
So, is agent-based modeling, in which you set up a computer simulation of individuals in the economy, a way of shining light on the place where the watch is likely to be? Below the fold, I will describe what excites me about ABM, what concerns me about it, and how I would recommend going about it.What excites me about ABM is that you are not constrained to model only those dynamic elements that yield close-form solutions using standard mathematical techniques. In some sense, ABM still boils down to a set of equations, but the equations can be ones that cannot be solved without the help of a computer.
What concerns me is that simulated results can be difficult to communicate and to understand. When you create a simulation, you can pick arbitrary rules. For example, you could leave out prices altogether. That is what the “Club of Rome” did in the 1970’s, when they simulated a doomsday scenario based on resource exhaustion.
I suppose that when you do standard formal mathematical modeling, the rules are also arbitrary. But because the assumptions are more visible, I think that you understand them better.
My concern with ABM is getting a result and not really knowing why you got it. Alternatively, if you really understand how the ABM is getting its result, you should be able to show how to get that results in a simple model, perhaps with a numerical example.
My dissertation adviser, Robert Solow, would recommend always starting with a simple numerical example before trying to generalize. Of course, back when I was in graduate school, you generalized by doing formal math, not by doing computer simulations. But I think that the same advice applies. Get the result in a form where you clearly understand it. Then go about trying to generalize it.
My intuition is that a combination of (a) – (c) is what accounts for fluctuations in the aggregate amount of economic activity. A computer simulation using agent-based modeling is probably necessary to bring all three into the picture. But first, I would play with simple numerical examples.