DON’T PANIC. Theory and Empirics Are Both Alive & Well…at least in political science.

Paul Krugman recently wrote a post about how/why formal theory has fallen behind empirical work in prestige/prominence in economics.  I agree with Krugman that the decline (if one thinks it has occurred) is not due to behavioral social science (Kahneman & Tversky’s voluminous body of work being the most notable of this field).  Krugman argues that this can’t be because people had long known that the axioms of decision-making that undergird much of formal theory in the social sciences:

“…anyone sensible had long known that the axioms of rational choice didn’t hold in the real world, and those who didn’t care weren’t going to be persuaded by one man’s work.”

Well, I agree with this statement (for example, Adam Smith was famously well-aware of this (see The Theory of Moral Sentiments).  But I disagree that this is why behavioral economics did not “cause” the decline of theory.  Mostly, this is because behavioral economics (and behavioral economists) have been looking for a theory to unify their disparate findings.  For example, Kahneman & Tversky are arguably most famous for prospect theory. That is, Kahneman & Tversky were not merely throwing hand grenades—they were at least partially occupied with the classical task of inductive theorizing.

I don’t have any dog in the fight about the relative position of theory and empirics in economics.  And by that, I mean, I am not even sure that dogs are involved in the skirmish or even if there is skirmish worth keeping tabs on.  And, in many ways, I’m an economist.  Well, I am an economist to those who distrust economists and “just maybe an economist” to economists.  (See what I did there?)

In political science, which I proudly call my home, theory is definitely not “dead” (Krugman’s title is “What Killed Theory?”).  Rather, I like to think that, most days of the week, theory and empirics reside quite amicably side-by-side in our big tent of a discipline.  Sure, theorists make jokes about empiricists and empiricists make (typically funnier) jokes about theorists, but this is simply incentive compatibility: every empiricist chose not to be a theorist, and every theorist chose not to be an empiricist.  (Of course, many political scientists are a little bit of both, but rarely at the same time, if only because the jokes become oh so much more poignant.)  As a theorist, I (honestly) love empirical work—particularly descriptive and qualitative work that gives me fodder for new models, but also “causal” findings and quantitative conclusions that I can “get all contrarian on.”[1]

What has happened in political science during the last 20 years is a decline (in terms of number of articles published) of what one might call “pure,” or “technical,” theory.  In a nutshell, I—and others—think of social science theory as being usefully broken into two categories: pure and applied.  Pure theory (tends to) focus on the technical aspects of the model and accordingly ask more “general” questions.  The “purest” theory is inherently “untestable” outside of the theory itself: Arrow’s theorem, the Gibbard-Satterthwaite theorem, Nash’s theorem, May’s theorem, etc. all reach very general conclusions about a theoretical construct (Arrow’s theorem describes all aggregation rules (for 3 or more alternatives), Gibbard-Satterthwaite describes all choice functions (for 3 or more alternatives), Nash’s theorem describes all finite games, and May’s theorem describes all social choice functions between two alternatives, etc.).  This type of theory is hard in a specific sense: useful/explicable results are notoriously hard to obtain.  A fundamental reality of theorizing is that the expected number of results one can obtain from a model is proportional to the number of assumptions one makes.  Without belaboring the point, this difficulty is part of the reason such theory has become less prevalent in political science.  (However, as a “shameless” plug, I will note that Elizabeth Maggie Penn, Sean Gailmard, and I recently published just such a theory, entitled “Manipulation and Single-Peakedness: A General Result,” in the American Journal of Political Science (ungated version here).)

Applied theory, on the other hand, involves making more assumptions and, as a price, exerting the effort to motivate the model as descriptive or illustrative of something that either does or “could” happen.  I’ve talked about my view of the proper role of theory before.  I’ll keep it brief here and say that this type of theorizing is very much alive in political science.  Because “applied” sounds pejorative, I like to refer to this practice as “modeling,” which sounds sexier (and the Brits spell it “modelling,” possibly because their models tend to involve “maths” rather than “math”).

Relevant to Krugman’s point at least as it might be extended to political science, modern political models include some that are “behavioral” in spirit (bounded rationality, etc.) and some are more classical (common knowledge of the game, rationality, etc.) To me at least, that ‘s a distinction without a difference: the quality of a theory/model is per se independent of its assumptions.  Rather, the quality is based on what it teaches me or makes me see in new ways.  This is why “rational actor” models are useful: for example, some rational actor models can explain apparently irrational behavior.  This is important for those who see the “irrational” behavior in question and start to make conclusions about policy and institutions based on their potentially flawed inference that people are irrational per se.  Similarly, behavioral models can generate predictions and possibility results that outperform (and/or are more easily understood than) rational actor models of the same phenomenon.  I like to think of rational actor models as being like the Cantor Set (or perhaps the Banach-Tarski paradox) and behavioral models as being like Taylor polynomials.

In other words, and regardless of whether you are comparing behavioral and rational actor models or pure and applied theory, neither is better or worse than the other from an a priori perspective, just like it is nonsense to assert that a hammer is “better” than a screwdriver: it depends on whether you need to smack something or twist it.  (AND WHAT IF YOU NEED TO DO BOTH? A.K.A. “A Theory of Revise & Resubmits.”)

As a final note, it is important to note (as we are seeing in the “big data” revolution—inter alia, here and here) that the process of “…theory with empirics with theory…” is one of complements, not substitutes: the value of new theory/data increases as data/theory gets ahead of it, and conversely, the value of additional data/theories declines as theory/data lag behind.  Krugman sort of tells this story in his post, but at least doesn’t explicitly extend to the conclusion: theory and empirics have tended, and will probably continue, to cycle “in and out of fashion.”  I am fortunate that, at least right now, the big tent of political science includes active work in both areas.

With that, I leave you with this.



[1] I used to do empirical work, until a court (of my peers) ordered me, in the interests of both society and data everywhere, to cease and desist.