Inside Baseball: Weather you like it or not, models are useful.

As a theorist, I write models.  (There is a distinction between “types” of theorists in political science.  It is casually and superficially descriptive: all theorists write models, just in different languages.)

One of the biggest complaints I hear—from both (some) fellow theorists and (at least self-described) “non-theorists”—is the following equivalent complaint in different terms:

  1. Theorists: …but, is your model robust to the following [insert foil here]
  2. “Non-theorists”:  …but, your model doesn’t explain [insert phenomenon here]

It is an important point—perhaps the most (or, only) important point—of this post that these are the same objection. I have been busy for the past month or so, and in the interest of getting those phone lines lit up, I thought I would opine briefly on what a social science model “should” do.  Of course, your mileage may vary, and widely.  This is simply one person’s take on an ages-old but, to me at least, underappreciated problem.

Models necessarily establish existence results. That is, a model tells you why something might happen.  It does not even purport to tell you why something did, or will, or did not, or will not, happen.  (Though I have a different take on a related but distinct question about why equilibrium multiplicity does not doom the predictive power of game theory.)

Put it another way: a model is a (hopefully) transparent, complete (i.e., “rigorous”) story or narrative offering one—most definitively not necessarily exclusive—explanation for one or more phenomena.  I regularly (co-)write models of politics (recent examples include this piece on cabinets and other thingsthis piece on the design of hierarchical organizationsthis piece on electoral campaigns, and this forthcoming book on legitimate political decision-making).  All of them are “simply” arguments.  None of them are dispositive.  The truth is, reality is complicated.

Politics is a lot like meteorology.  We all know and enjoy, repeatedly, jokes along the lines of “hell, if I could get a job where you only need to be right 25% of the time….,” but the joke makes a point about models in general.  Asking any model of politics to predict even half of the cases that come to you after reading the model is like asking the meteorologist to, say, correctly and exactly predict the high and low temperature every day at your house.  No model does that.  Furthermore, it is arguable that no model should be expected to, perhaps, but that’s a different question.  More importantly, no model is designed to do this…because it defeats the point of models.

Running with this, consider for a moment that a lot more is spent on meteorological models than political, social and economic ones (e.g., the National Weather Service budget is just shy of $1 billion and that of the Social and Economic Sciences at the NSF is approximately 10% of that). Models are best when they are clear and reliable.  Sometimes, reliability means—very ironically—“incomplete in most instances.”  Consider a very reliable “business model”:

“Buy low, sell high.”

This model, setting aside some signaling, tax, and other ancillary motivations (which I return to below), IS UNDOUBTEDLY THE BEST MODEL OF HOW TO GET RICH. 

However, it is incomplete.  WHAT IS LOW? And you can’t answer, “anything less than `high,'” because that merely pushes the question back to WHAT IS HIGH?

Of course, some people will rightly say that this indeterminacy is what separates theory from praxis.  The fact is, even the best good models don’t necessarily give you “the answer.”  Rather, they give you an answer.  One can reasonably argue, of course that a model is “better” the more often its basic insights apply.  But that is a different matter.

Returning to the “buy low, sell high” model, consider the following quick “thought experiment.”  Suppose that Tony Soprano approaches you and says, “please buy my 100 shares in for $10,000.”  Should you?  According to the model, the answer is clearly no: shares in are worth nothing—and never will be worth anything—on the “open market.”

But, running with this, Tony has approached you for “a favor.”  Let’s not be obtuse: he bailed you out of that, ahem, “incident” in Atlantic City back in ’09, and you actually have two choices now: pay $10,000 for worthless shares in or have both of your kneecaps broken. (Protip: buy the shares.)

The right choice, given my judicious/cherry-picking framing, is to buy shares high and “sell them low.”  Well, this proves the model wrong, right?  No.

It simply change the definition of “value when sold.”  It reveals the incompleteness of the theory/model.

This is basically my point: no model is truly “robust,” even to imaginable variations and, conversely, it is certainly the case that smart people like you can come up with examples that at least seem to suggest that the model doesn’t describe the world.

It’s kind of an analogy to a foundation of empirics and statistics: central tendency.  Models should indicate an interesting part of a phenomena of interest.  In this sense, a good model is an existence proof, sort of like the Cantor Set: it demonstrates that things can happen, not necessarily that they do.  The fact that those things don’t always happen doesn’t really say much about the model, just like you read/watch the weather every day even while making those jokes about the meteorologist.

And with that seeming non sequitur, I push forward and leave you with this.