I’ve joked before with people that I liken social science models to rock songs.  My actual mapping is horribly incomplete.  So I’ll set that chatter to the side.

That said, the practice of modeling, in my experience, is a lot like rock ‘n roll.

You give me a topic, and I’ll think for a minute, make an awkward joke to stall, and then say, “well…I think we can throw in a bit of Romer-Rosenthal, maybe a touch of Crawford & Sobel, plus a flourish of valence, and Voilà! … We have a model.” (Participants at EITM 2013 can vouch for this…for better or worse.)

But….I’m serious. Modeling is a delicate balance of divine insight and practice.  And, given the relative and regrettable scarcity of divinity in practice, more practice than insight.

Modeling requires balancing (1) a substantive question, (2) generality, (3) the finitude of time. It lies at the heart of both what are putatively purely-empirical and purely-theoretical enterprises (the only class of social science theory that is “not-putatively-but-actually-purely-and-absolutely-theoretical-and-therefore-unambiguously-correct-and-applicable” is social choice theory.)  Methodologists, game theorists—they all rightly make assumptions to get to the point of their argument.

This is ROCK AND ROLL: YOU HAVE TO FIGHT FOR YOUR RIGHT TO MODEL.

If I said, “tell me how to make a yummy dish,” you’d ask “what’s yummy?” If I, being as obstinate and/or distracted as I usually am, did not answer—you’d have to make some assumptions about what I might like. If you assumed that I liked what everybody else liked, you’d probably hand me “Joy of Cooking.”  On the other hand, if you assumed I asked because I’d looked in the Joy of Cooking and not found what I liked, you would appropriately presume that I wanted something other than “the normal,” and you’d then be  seen by the outside world as playing punk. You’d probably (rightly) take off-the-shelf tools, utilize standard analogies, and leverage structure that threatens few to provide me with a new conclusion. That’s punk.

[During the perhaps overly-artsy bass solo, let me confess that not all punk is good. But all punk is, tautologically, punk.]

…Cue big build, drum crescendo, and….harmonic ending that sends crowd into rhapsodic frenzy…

Ok, What I’m saying is a short thing: good (formal/stat/etc) modeling is punk: it takes “old” tools, “expected” tricks, and combines them to “make the house rock,” or “get the message across.”  (Lucky are those situations when “the house rocks to the message.”)

Does the Pixies anthem “Gouge Away” address every possible situation?  Is it robust to every ephemeral, existential robustness barrage one might throw at it?

Hell no. That’s why the phrase “holy fingers” is so haunting. After all, “holy fingers” are rare unless you count Chicken Fingers ™.

So, when you want to say “well, your explanation for that is just an example, I’ll just say `Get Over It.’ … And then I’ll be gone, making more noise pop, playing a flying Fiddle to the Quotidian.

With that, I leave you with this.

I just finished writing this meandering post about sequential rationality. Subsequently, I thought of these better examples that come from different angles.

First, a classic example (mostly) of failures of sequential rationality: food challenges. In most cases, most people who think they can eat that amount of food can eat that amount of food. They just end up, part of the way through the challenge, not wanting to do so.

A so-so story about motivation that, upon reflection, suggests an interesting take on harnessing “nonsense” intrinsic motivations to overcome sequential rationality/commitment problems: Jerry Seinfeld’s “don’t break the chain” tip for writing. (But, how do I motivate myself to bother writing the “X” on the calendar? I’ll just remember, right?  HAHAHA.)  In other words, it’s usually more fun to not work than work (because, hey, somebody‘s got to bang on that drum ALL DAY), but it also sucks to think about breaking a streak.  Well, at least a streak that is endorsed by Jerry Seinfeld.

It’s a new year, the ground is covered with more-than-ankle-deep snow, and I told a friend tonight that it seems like, over the past couple of weeks, the notion of a “day of the week” has lost all meaning.

This is how winter break winds to a quiet close.

It is an opportunity to take stock, and think about what we do as faculty and researchers.  In a sense, it is the quintessential time for “well, time to get BACK AT IT.”  After all, the spring semester lurks right around a single left-click on the google calendar, and both students and journals are inexorably rising from a temporary slumber, bringing the holiday respite to a close.

The details of my own thinking are ephemeral even to me (though you never know when hindsight might prod one to think such classifications naive or at least short-sighted).  But the classic math of politics point that keeps coming back to me during this thinking is

Don’t start what you won’t want to finish.

Notice it’s not can’t finish…this is about incentives/motivations/desires, not ability. In the end, time is finite and, ironically, fleeting.  A project you’re not “into” has a very small chance of being finished.  In game theory, this idea is captured by what is referred to as sequential rationality. In a nutshell, a sequence of actions or decisions is sequentially rational if, at every opportunity one has to revisit and revise the sequence, continuing the sequence as originally prescribed is still in the decider’s interest.  In other words, it is a sequence of decisions that the decision-maker never has significant regret about continuing.

As one example, I (re)designed two syllabi this week.  I am teaching Intro to American Government, a course that I have always wanted to try, and graduate game theory, a course that I have taught for years, but never perfectly.  In both courses, there are topics I like more than others.  In both courses, there are topics I really don’t find relevant…at least for the way I think about “the course.”  However, many of these topics are de rigueur.

So, should I cover these topics?  I can stomach the claim that I should cover them well. But, to be blunt, both sequential rationality and experience—the two of which pair quite nicely, if you think about it for a minute—suggest that that fortuitous combination is not likely.  Rather, my real choice is between covering them as well as I will or simply omitting them.  (I’ll set aside the possibility of minimizing them for the purpose of maximizing clarity.)

On the one hand, it is likely (at least in my mind) that the time spent covering these topics in a subpar fashion could be better spent on other topics I feel more strongly about.  So, covering these topics comes at a cost.  On the other hand, not covering the topics, in addition to potentially leaving gaps in the student’s knowledge,[1] sends a signal about my own tastes/abilities/dedication.  Setting aside the “gaps in knowledge” worry, as none these topics couldn’t be covered by students on their own at least as well as I would cover them, given my lack of interest in them, the comparison is even more stark.

Specifically, if one presumes that my interest in/dedication to the topic—i.e., the sequential rationality of me “committing” to a syllabus where I will talk for 3 hours about the topic[2]—is positively associated with quality of the teaching, then an earnest student would, prior to the course beginning, wish that I would allocate my teaching of the various topics roughly in proportion to my interest in them.  But, I have an interest in being seen as “teaching well.”  Or, less admirably, I have a latent but abiding interest in avoiding scrutiny.  If I teach what I think most appropriate according to the logic of sequential rationality, and for some reason a student complains about the course, I would potentially have to explain why my course omitted some de rigueur topics.  My argument could, of course, be that I don’t find those topics as interesting, relevant, and/or important as others.  But, to be both clear and hyperbolic, this would be arguing against the discipline.

Now, luckily for me, it doesn’t really matter right now.  But, this is a classic and subtle point about accountability, discretion, and commitment.  My hypothetical earnest student is arguably also a plausible benchmark for the hypothetical earnest parent and/or university administrator.  But, once the “fire alarm oversight” of a disgruntled student (or, to be fair, many students) is “pulled,” the sequential rationality of even the hypothetical earnest parent and/or university administrator to stand up for “well, the syllabus was ex ante efficient given Professor Patty’s own sequential rationality constraints” is suspect.

In other words: the road to commitment problems is paved with unrealistic intentions.

With that, I leave you with this.

_____________________

[1] To be clear, in some cases, I might be preserving the students’ knowledge by not covering a topic.

[2] Arguably commensurate and definitely countervailing to the dropping of worries about the gaps in students’ knowledge, I am also setting aside the potentially deleterious effects of proffering a syllabus that is ultimately not followed. That’s the more traditional portrait of a failure of sequential rationality: the plan is ultimately “not followed.”  A piece of advice for those who have yet to teach your own course: varying from the syllabus is the surest way to lower your course’s evaluation without significantly risking your own dismissal. Trust me on this. I have plenty of evidence, all of which I regrettably “manufactured.”

I am a game theorist.  I love thinking about situations as strategic interactions.

As a game theorist, I make assumptions all the time. (And I assume you do, too, in whatever you do. THAT’S META.)

In political science, there are, broadly defined and in my estimation, four categories of theory.  Game theory (which includes mechanism design) is one, of course.  The oldest of the four is typically simply called “political theory” or, more precisely, normative political theory within the discipline, and—while it is a decidely and wonderfully big tent—essentially can be described as work focused on theoretical concepts such as justice, fairness, & democracy, and how they should be measured.  (That “normative” political theory is about measurement is my take—the usual framing of the research is “this concept implies that one should do the following.” If you think about it for a second, the implication of accepting such a conclusion is “adherence to this concept implies that you will do the following,” by which you can measure (from behavior, for example) how much someone values, or at least adheres to, this concept.)

The third category is known as social choice theory. This category includes studies of what is possible to achieve. There are a lot of ways this inquiry can be carried out.  Examples include “can we always find a fair choice,” “is it possible to design a voting system that always rewards honesty,” or “can we combine people’s preferences to make a sensible public decision?”  This field has been declared dead by more than a few social scientists, ironically due to a handful of noteworthy (and Nobel prize-winning) achievements, including Arrow’s (im)possibility theorem.  That is not, in fact, dead is proven by much current research, including the forthcoming book authored by Maggie Penn and myself, Social Choice and Legitimacy: The Possibilities of Impossibility.

Finally, a fourth category has emerged over the past forty years or so (beginning with the works of Schelling, Axelrod, and others).  This area, computational modeling, develops and analyzes mathematical models of behavior and institutions to derive empirical predictions about political phenomena.

While open hostilities are luckily rare, the four categories do not “get along,” generally. This is both silly and a shame.  It’s a shame because the four areas have a lot of opportunities for profitable and though-provoking collaboration.  It’s silly because they have a lot in common (independent of their extant potential synergies).  In particular, “all four are theory.”  That is, the best work in each of the four values and embodies clarity and, to the degree available, parsimony.

In the interest of parsimony I have drawn a picture of (at least some of) their connections. I have put game theory/mechanism design at the middle of the graph because, even if that’s not right, it instantiates the old adage that “the blogger draws the maps.”

This is a picture I drew…OF THE WORLD OF THE MIND.

Very quickly, why the distinctions between the four categories?  Well, I have been thinking about them—and I am comfortable describing myself as “actively” working in all four areas—and I thought it might be useful (if only for me) to work through a quick description of the lines in the picture.

Normative political theory is, by and large, completely concerned with internal consistency.[1] In this sense, it is linked with game theory and mechanism design and social choice theory: all three categories highly value what is known as a “fixed point,” which describes a solution to a problem that, when put forward as the solution to the problem as described by the theory, does not then lead to a different solution being produced.

Social choice theory is concerned with generality and, relatedly, agnosticism.  (There are important exceptions to this, such as in this article by Maggie Penn, Sean Gailmard, and myself.) That is, much of social choice theory tackles the tough questions of what can be achieved/guaranteed with as minimal restrictions on “who may show up” or “what people might want/believe” as possible.  The link between social choice theory and normative political theory is hopefully clear: much normative political theory either in essence argues for what one should want or believe or works out how one should act given what one wants or believes, while most social choice theory finds what can happen when we can’t or don’t want to restrict what people want or believe.[2]

Normative theory and social choice theory are each less concerned with the details of the specific situation: they are more general than game theory.[3]

On the other end of the generality spectrum is (the vast majority of) computational models. Most computational models require specification of the parameters of a particular situation to generate (often very precise) predictions.  If one has this specification at hand, (the right) computational models offer a direct path to the desired outcome: a prediction and the ability to start tinkering.

Part of the power of computational modeling is its deliberate setting aside of what I call epistemological concerns.  Put another way, a good computational model is not required to be internally consistent with respect to individual or group motivations.  That is, and this is often properly trumpeted as a strength of computational modeling but, in my opinion, for the wrong reasons, computational models do not necessarily yield what a game theorist would call “equilibrium outcomes.”  In other words, there is no reason to suspect that a computational model of a given political situation would remain accurate once the model was shown to one or more reflective agents involved in that situation.[4]

I’ll conclude now by restating my point bluntly: each of these are political theory. And, yes, game theory is at the middle: it requires specification of more details than social choice and normative theory (which I would argue, if pressed, truly differ only in their languages) and it requires “more of itself” in terms of internal consistency than is (appropriately) the norm in computational modeling.

Like I’ve said before about other analogous “comparisons,” asking which of these is the best is like asking whether a screwdriver is better than a hammer.  It depends on whether you want to screw someone or simply hit them over the head.

With that, I leave you with this.

____________

[1] There are lots of great normative arguments that encounter the dirty feet of reality, but that is to the field’s credit, as opposed to a defining quality, in my opinion.

[2] Note that this illustrates an important category of empirical implications of social choice theory: social choice results inform one about the degree to which one can infer anything about the preferences and/or beliefs of a group of individuals from the decisions and choices one observes the group make.

[3] Mechanism design is “in the middle,” as Austen-Smith and Banks make clear in their incredible treatises (volume 1 and volume 2).  That is, mechanism design asks, in essence, what general goals/outcomes can a group achieve through the proper design of a specific institution (i.e., “game”).

[4] I won’t discuss it here, but the dotted line in the figure connecting social choice and computational models reflects a very active area of research that asks when, how, and whether such reflective individuals might be able to change their behavior to make themselves better off.

Most of my research starts with the presumption that individuals are rational.  By this, I mean that they know the rules of the game, and they also know that the other players are rational.[1]  Simple empirical observation indicates the inherent contestability of this presumption.  So, why do I continue to adopt it? Well, I have written before about this point in a number of posts, but (or, “accordingly”) I’ll say the presumption of rational behavior establishes the robustness of the phenomena that the model aims to “predict/explain/mimic.”  That is, presuming rationality makes my life harder as a theorist. If I did not have rationality as a stake for the tent I’m putting up, it would be pretty darn easy to place that tent wherever I want: as a theorist, I can explain anything if you let me bring irrational behavior to the table. Setting that to the side, however, there’s another point I’ve been thinking about lately that I wanted to drone on about a bit.  If we step away from presuming rationality, we need to put something in its place.  There is a huge and growing literature on deviations from rationality in social sciences.[2] In a quick form, my challenge to those who would have models incorporate these findings is as follows:

What are the “parameters” of irrationality?

A related version of this is “which varieties of irrationality should I put in my model?” There are various forms of irrational choice: those that deal with

1. intertemporal choice (e.g., overconfidence in your own patience in the future),
2. evaluating risk (e.g., is it a gamble over winnings or losses?),
3. updating on information (e.g., favorable vs. unfavorable information?)
4. …

So, this leads to two questions.  First, which of these is more important to include?  Common sense (and the quest for causal identification, if that’s your cuppa tea) suggests that throwing them all in will yield something unmanageable.

Second, and more precisely, even if I choose one to include (for example, to see how the availability heuristic affects how voters evaluate incumbents and challengers, and how this affects campaign decisions), how do I parameterize/represent this bias?

Put another way, most (formal and informal) models of behavioral biases rely on at least an implicit notion of perception: in a nutshell, most biases in choice can be described as some “label” or “feature” of the decision problem that the decision-maker should not actually take into account actually bearing on the decision-maker’s ultimate choice(s).

The question, and conclusion of this post (in search of feedback), is how do we model the world of “features?”  That is, how does one make an even plausibly “general” theory of a phenomenon that includes behavioral biases?  After all, most models are designed to be generally applicable—where “generally” means across time, or space, or something.  Traditional models of choice/behavior presume that people have a “general” feature known as a preference ordering (or, essentially equivalently, a utility/payoff function).  But the (both formal and informal) “behavioral” models have been built largely on noting a series of deviations from the predictions of the classical choice model in a specific (often laboratory) setting.  This isn’t an objection that the findings aren’t externally valid—it’s an honest question as to how one extends the findings to general (i.e., external) settings.

So, what ingredients should I throw into the pot if I want to cook up a model of the mind to deploy in my models of political behavior and institutions?

With that, I call upon a favorite, and leave you with this.

___________

[1] There’s even more in here, as I wrote about here. I will set the issue of common knowledge of rationality aside for the purpose of this post, as it merely amplifies the point I am trying to make.

[2] I won’t bother to cite any of it, because it would be merely gratuitous and possibly lead to erroneous inferences about what I intend to be inferred by the inevitable omissions from such a list.  That concern, if you think about it, is meta. (What? Oh, okay… start here.)