David, a variable that can never be tested might create a falsifiability problem with respect to THAT variable. But, if we are looking at complex relationships out there in the world with many, many interactions — some observable and some not (*never* can be tested, lets pretend we would know that) — making assumptions about one allows me to more rigorously examine the rest of the relationships involved. This is a primary feature of modeling. I can get MORE testable implications out of the model by doing this.

I realize none of this answers your question, and I certainly dont know much about political science modeling. But, I could not resist defending modeling as a useful tool against Frank’s critique.

My concerns (directed at political scientists, rather than economists) are more prosiac. I could see someone concluding that the thesis “because of stare decisis, political insulation, and the hokum/magesty of judicial decisionmaking, delegating decisions to courts are difficult to control in the near term, but are stable in the long term” being, if anything, easier to consume than Stephenson’s modeled conclusion, and just as falsifiable. If not more so. I wonder if the modeling limited the sort of conclusions he could draw while also costing readers.

But I don’t want to overstate the concerns. I’m interested in learning more about modeling because that’s what the rat choice crowd does, for better or worse (better, if their success in the academy is any indication).

Tried to post this yesterday, but comments closed, maybe because of spam? Lest any of you think that I’m ever far from my trusty laptop.

Let me expand on Nate’s point. Nate is right that one of the primary reasons to model is to identify relationships and their implications. These models predict how a change in one variable of interest impacts others, and sometimes tell us something about the magnitude of the effect. Sometimes we can test all of the relationships we would like because we have the data. Sometimes we dont have that data and can only test some of the hypothesis we can derive from the model. One of the main contributions of modeling, though, is that it forces one to be precise about identifying those relationships and the underlying assumptions driving those relationships. This is true even if we cannot “quantify” the variable.

Here is a boring example from my own research: I want to model competition of firms for shelf space and its impact on consumers. I need a model of consumer behavior. I believe, and am willing to assume, that consumers value product location on the shelf but dont know how to “quantify” this effect because I only observe whether the consumer purchases the product or does not. By assuming in my model that premium shelf space increases consumer willingness to pay (and openly stating as much), I have held this interesting part of the problem constant in a reasonable manner such that I can look at interesting relationships between manufacturer and retailer behavior. Perhaps I can even derive some previously unexplored relationships from my model that ARE testable with data. I realize shelf space is not everybody’s thing, but it should not be difficult to think of an example that does the trick for you. The point is that modeling might tell us something (maybe even something important) about certain observed behaviors and their impacts even though not every variable in the model is measurable in the sense you seem to believe is necessary.

What is more, modeling compels the researcher to explicitly set out the assumptions and forces that are driving those relationships. If the assumptions are unwarranted, or do not truly support the outcome predicted by the author, such errors can be exposed. Legal arguments have long included assumptions about both the empirical state of the world and the behavior of economic agents on variables of interest. Frequently, scholars build on these assumptions and make predictions about variables of interest and predicted effects. Increasing the level of precision in specifying those assumptions and deriving conditions under which certain relationships or results hold (or do not hold) seems like a good thing to me.

Of course, excessive formality, i.e. using a fancier model than necessary to make the point, may be counterproductive. A model should start simple, and add features as neccessary. There is a good deal in economic modeling that is not helpful and overly formal. But this is not properly construed as an attack on modeling itself.

But fine, fine … I’ll only mildly note that though some don’t like administrative law, for others, it’s the only kind of public law worth taking seriously. I imagine that latter group includes most political scientists, who have, for better or ill, never been fans of traditional constitutional or public international law.

The real tragedy of Matthew Stephenson’s work is that such a great mind is being frittered away in the barren wasteland of administrative law.

It’s no surprise that Farber’s response to Stephenson says “the empirical evidence does not support the model.” See

http://www.harvardlawreview.org/forum/issues/119/feb06/farber.shtml

But I doubt a group of political scientists could even find a way to code the evidence in order to see if the the theory was true or false. So perhaps it fails the first criterion of (Popperian) science: falsifiability.

In any event, I would hate to see legal scholarship dominated by “fancy math” just because it has the patina of rigor. This happened to poli sci for a while, with unfortunate consequences. Here are a few sources who share my insight/bias:

Shapiro & Green, The Pathologies of Rational Choice Theory (1994). (This was written in response to rational choice theorists’ aggressive colonization of political science departments. They won at Harvard, lost at Yale, and still have enormous influence in the profession.)

Ian Shapiro, The Flight from Reality in the Human Sciences (2005). (critiquing mystification-via-mathematicization)

George Steinmetz, ed., The Politics of Method in the Human Sciences: Positivism and Its Epistemological Others (2005) (wonderful collection of essays, some of which discuss the way mathematicization functions as a strategy for consolidating authority in the social construction of expertise)

Oz Shy, Introduction to Network Economics (wryly commenting on the lack of utility of a great deal of mathematical modeling in economics).