Play, games, science and bureaucracy

In the titular essay of his 2015 book The Utopia of Rules, David Graeber argues for a distinction between play and games. Play, according to Graeber is free, creative, and open-ended, while games are rigid, repetitive, and closed-off. Play underlies art, science, conversation, and community, while games are the preferred method of bureaucracy. This idea really resonated with me, partially because I’m someone who doesn’t really like games, but also because I think it’s perfectly consonant with something I’ve written about previously: the distinction between theoretical and descriptive science. In this post, I’ll explore that intuition, and argue that theoretical scientific research tends to center play, while descriptive research tends to center games.

The key distinction between games and play, according to Graeber, is rules. While both are leisure activities done for sheer enjoyment, games are defined by their rules. These rules can be rather simple (e.g., checkers), fiendishly complex (e.g., Settlers of Catan), or something in between, but whatever they are, the rules make the game. What’s more, Graeber argues, it’s the rules that make games an enjoyable respite from the ambiguities of real life. At any given point in a game, there are a finite number of possible moves and a fixed objective. If only we had that same certainty when navigating interactions with neighbours, co-workers, and romantic interests!

If games are defined by their rules, then play is defined by it’s lack of rules. The best example—one used by Graeber—is that of children playing. There are no rules to how children play. In fact, as Graeber observes, a good portion of play between children involves negotiating the rules. What’s more, there’s no winning at play, only enjoyment. Play is open-ended—set a group of children to play and there’s no knowing what they’ll come up with.

Yet, play is not random. It follows principles—such as the innate social instincts of children—which are a different sort of thing from the rules that govern games. Rules must be explicit, determinate, and preferably compiled in some centralized place so that, in a well-designed game, disputes can be always be settled by consulting some authority, usually a rule-book. Principles are often implicit—no one teaches kids how to play—can be quite vague—a main principle of improv is “Listen”—and are arguably somehow internal—if there are principles to playing a musical instrument, they come from the laws of physics, the form and material of the instrument, and our own innate sense of melody, harmony, and rhythm.

As this description might suggest, rules and principles, games and play, are often in conflict with each other. Taking a playful activity, and turning it into a game can eliminate some of the enjoyment. Take, for instance, flirtation—a playful activity, for which an anthropologist might be able to discover some principles. People who treat flirtation as a game understandably tend to be judged as creepy. Understandably, because gaming assumes a determinate rules—if I do X in situation Y, then Z will happen—and no-one likes to be treated like a robot. Or, consider figures like Chelsea Manning or Edward Snowden. Each was faced with a conflict between the external rules of an institution and their internal principles, and chose the latter.

This conflict, however, need not be an overall negative. Any art form at any given time follows a number of rules and conventions that, at their best, defines the space in which an artist can play. Eventually, though, the rules and conventions of a given art form or genre become too fixed and end up stifling the playfulness of the artists. I remember my cousin, who was a cinema studies major in undergrad talk about watching Citizen Kane for a class. The students were confused—this is widely lauded as one of the greatest films ever made, but they couldn’t see what was so special. The instructor explained that Citizen Kane was groundbreaking when it came out, it broke all the rules, but it ended up replacing them with new ones. Now those new rules are so commonplace, that they are completely unremarkable. While I don’t think we could develop an entire theory of aesthetics based solely on the balance between games and play, the opposition seems to be active in how we judge art.

But what does this have to do with science? Well, thus far I’ve suggested that games are defined by external rules, while play is guided by internal principles. This contrast lines up quite nicely with Husserl’s definitions descriptive and theoretical sciences respectively. Descriptive sciences are sets of truths grouped together by some externally imposed categorization, while theoretical sciences are sets of truth which have an internal cohesion. If I’m on the right track, then descriptive sciences should share some characteristics with games, while theoretical sciences should share some with play.

Much as games impose rules on their participants, descriptive sciences impose methods on their researchers. Often times they are quite explicit about this. Noam Chomsky, for instance, often says of linguistics education in the mid-20th century, that it was almost exclusively devoted to learning and practicing elicitation procedures (read: methods). The cognitive revolution that Chomsky was at the center of changed this, allowing theory to take center-stage, but we are currently in the midst of a shift back towards method. Graduate students are now expected or even required to take courses in “experimental” or quantitative methods. Job ads for tenure-track positions are rarely simply for a phonologist, or a semanticist, but rather, they invariably ask for experience with quantitative analysis or experimental methods, etc.

The problem with this is that methods in science, like rules in games, serve to fence in possibilities. When you boil it down to its essences, a well run experiment or corpus study is nothing but an attempt to frame and answer a yes-or-no question. What’s more, each method is quite restricted as to what sort of questions it can even answer. Even the presentation of method-driven research tends to be rather rigidly formatted—experimental reports follow the IMRaD format, so do many observational studies, and grammars, the output of field methods, tend to start with the phonetics and end with the syntax/semantics. So when someone says they’re going to perform an eye-tracking study, or some linguistic fieldwork, you can be fairly certain as to what their results will look like, just like you can be certain of what a game of chess will look like.

Contrast this with theoretical work, which tends to start with sometimes horribly broad questions and often ends up somewhere no-one would have expected. So, asking what language is yielded results in computer science, inquiring about the motion of the planets led to a new understanding of tides, and asking about the nature of debt reveals profound truths about human society. No game could have these kinds of results—if you sat down to play Pandemic and ended up robbing a bank, it probably means you read the rules wrong at least. But theory is not like a game, it’s inherently playful.

Now anyone who has read any scientific theory might object to this, as the writing produced by theorists tends to be rather abstract and inaccessible, but writing theory is like retelling an fun conversation—the fun is found in the moment and can never be fully recreated. The playful nature of theory, I think, can be seen in two of the main criticisms leveled at theoretical thinkers by non-theorists: that theoreticians can’t make up their minds and that they just make it up as they go along. These criticisms, however, tend to crop up whenever there is serious theoretical progress being made. In fact, many advances in scientific theories are met with outright hostility by the scientific community (see, atomic theory, relativity, the theory of grammar, etc), likely, i think, because a new theory tends to invalidate a good portion of what the contemporary community spend years, decades, or centuries, getting accustomed to, or worse yet, a theoretical advance might appear to render certain empirical results irrelevant or even meaningless.

Compare this to children playing. If children make up some rules while playing, only a fool would take those to be set in stone. Almost certainly, the children would come up against those rules and decide to toss them by the wayside.

As I mentioned, Graeber discusses games and play as a way of analyzing bureaucracy and our relationship to it. Bureaucracy—be it in government, corporations, or academia—is about creating games that aren’t fun. They are also impersonal power structures, what Hannah Arendt calls “rule by no-one”. And just as games are, bureaucracies are designed to hem in playfulness, because allowing people to be playful might lead them to realize that a better life is possible without those bureaucracies.

Within science, too, we can see bureaucracies being aligned with strictly methodical empirical work and somewhat hostile to theoretical work. We can see this in how the respective researcher organize themselves. Empirical work is done in a lab, which does not just refer to a physical space, but to a hierarchical organization with a PI (primary investigator), supervising and directing post-docs and grad students, who often in turn supervise and direct undergraduate research assistants—a chief executive, middle management, and workers. Theoretical work, on the other hand, is done in a wide array of spontaneously organized affinity groups. So, for instance, neither the Vienna Circle, in philosophy, nor the Bourbaki group, in mathematics, had any particular hierarchical structure and both were quite broad in their interests.

The distinction can even be seen in how theoretical and descriptive sciences interact with time and space. Experimental work must be done in specially designed rooms, sometimes made just for that one experiment, and observational work must be done in the natural habitat of the phenomena to be observed—just as a chess game must be limited to an 8×8 grid. Theoretical work, can be done almost anywhere: in a cafe, a bar, on a train, in a dark basement, or spacious detached house. The less specialized the better. In fact, the only limiting factor is the theorist themself. As for time, nowhere is this clearer than in the timelines given by PhD candidates in their thesis proposal. While not all games are on a clock, all games must account for all of their time—each moment of a game has a purpose. This is what a timeline for a descriptive project looks like: “Next month I’ll travel to place X where I’ll conduct Y hours of interviews. The following month I will organize and code the data…” and so on. It’s impossible to provide such detail in the plan for a theoretical work for several related reasons: The time spent working tends to be unstructured. You never know when inspiration or some kind of moment of clarity will strike. You can’t possibly know what the next step is until you complete the current step. and so on. Certainly, the playful work of theory can sometimes benefit from some structure, but descriptive work, like a game, absolutely depends on structured time and space.

This alignment can also be seen with how theory and method interact with the superstructures of scientific research, that is, the funding apparatuses—granting agencies and corporations. Both sorts of structures are bureaucratic and tend to be structurally opposed to theoretical (read: playful) work. In both cases, funders must evaluate a bunch of proposals and choose to fund those that are most likely to yield a significant result. Suppose you’re a grant evaluator and you have two proposals in front of you: Proposal A is to do linguistic fieldwork on some understudied and endangered language focusing on some possibly interesting aspect of that language, and Proposal B is to attempt to reconcile two seemingly contradictory theoretical frameworks. Assuming each researcher is eminently qualified to carry out their respective plans, which would you fund? Proposal A is all but guaranteed to have some results—they may be underwhelming, but they could be breakthroughs (though this is very unlikely)—a guarantee that’s implicit in the method—It’s always worked before. If Proposal B is successful, it is all but guaranteed to be a major breakthrough, however there is absolutely no guarantee that it will be successful—if the researcher cannot reconcile the two frameworks, then we cannot draw any particular conclusion from it. So which one do you choose? The guarantee, or the conditional guarantee? The conditional guarantee is a gamble, and bureaucrats aren’t supposed to gamble, so we go with the guarantee.

So, bureaucratic funding structures are more inclined to fund methods-based research, that’s fine as far as it goes—theoretical research is dirt cheap, only requiring a nourished mind and some writing materials—but grants aren’t just about the money. Today, grants are used as a metric for research capability. If you can get a lot of grants, then you must be a good researcher. Set aside the fact that virtually any academic will tell you that grant-writing is a particular skill that isn’t directly related to research ability, or that many researchers delegate grant-writing to their post-docs, the logic here is particularly twisted: Granting agencies use past grants as an indication of a good researcher, so do hiring committees. This makes sense—previous success in a process is a good indicator of future success—provided everything stays more or less the same. Thus the grant system and other bureaucratic systems are likely to defend the status quo, by funding descriptive rather than theoretical work.

If my analysis is correct, then the sciences are being held back by the bureaucracies that are supposed to enable them such as university administration and funding agencies. They’re also held back by their own mythology—the “scientific method”—which promises breakthroughs if only they keep playing the game. This should not be too surprising to anyone who considers how bureaucracies hold them back in their day-to-day lives. What’s frustrating about this though, is that academia, more than any sector of modern society, is supposed to be self-organized. University administrators (Deans, Presidents, Provosts, etc.) are supposed to be drawn from the faculty of that university, and funding organizations are supposed to be run by researchers. So, unlike the bureaucracies the demean the poor and outsource jobs, the bureaucracies that stifle academics are self-imposed. The positive side of this is that, if academics wanted to, they could dismantle many of their bureaucracies tomorrow.

Instrumentalism in Linguistics

(Note: Unlike my previous posts, this one is not aimed at a general audience. this one’s for linguists)

As a generative linguist, I like to think of myself as a scientist. Certainly, my field is not as mature and developed as physics, chemistry, and biology, but my fellow linguists and I approach language and its relation to human psychology scientifically. This is crucial to our identity. Sure our universities consider linguistics a member of the humanities, and we often share departments with literary theorists, but we’re scientists!

Because it’s so central to our identity, we’re horribly insecure about our status as scientists. As a result of our desire to be seen as a scientific field, we’ve adopted a particular philosophy of science without even realizing it: Instrumentalism.

But, what is instrumentalism? It’s the belief that the sole, or at least primary, purpose of a scientific theory is its ability to generate and predict the outcome of empirical tests. So, one theory is preferable to another if and only if the former better predicts the data than the latter. A theory’s simplicity, intelligibility, or consistency is at best a secondary consideration. Two theories that have the same empirical value can then be compared according to these standards. Generative linguistics seems to have adopted this philosophy, to its detriment.

What’s wrong with instrumentalism? Nothing per se. It definitely has its place in science. It’s perfectly reasonable for a chemist in a lab to view quantum mechanics as an experiment-generating machine. In fact, it might be an impediment to their work to worry about how intelligible QM is. They would be happy to leave that kind of thinking to the theorists and philosophers while they, the experimenter, used the sanitized mathematical expressions of QM to design and carry out their work.

“Linguistics is a science,” the linguist thinks to themself. “ So, linguists ought to behave like scientists.” Then with a glance at the experimental chemist, the linguist adopts instrumentalism. But, there’s a fallacy in that line of thinking: Instrumentalism being an appropriate attitude for some people in a mature science, like chemistry, does not mean it should be the default attitude for people in a nascent science, like linguistics. In fact, there are good reasons for instrumentalism to be only a marginally acceptable attitude in linguistics. Rather, we should judge our theories on the more humanistic measures of intelligibility, simplicity, and self-consistency in addition to consistency with experience.

What’s wrong with instrumentalism in linguistics?

So why can’t linguists be like the chemist in the lab? Why can’t we read the theory, develop the tests of the theory, and run them? There are a number of reasons. First, as some philosophers of science have argued, It is never the case that a theoretical statement is put to the test by an empirical statement, but rather the former is tested by the latter in light of a suite of background assumptions. So, chemists can count the number of molecules in a sample of gas if they know its pressure, volume, and temperature. How do they know, say, the temperature of the gas sample? They use a thermometer, of course, an instrument they trust by virtue of their background assumptions regarding the how matter, in general, and mercury, in particular, are affected by temperature changes. Lucky for chemists, those assumptions have centuries worth of testing and thinking behind them. No such luck for generative linguists, we’ve only got a few decades of testing and thinking behind our assumptions, which is reflected by how few empirical tools we have and how unreliable they are. Our tests for syntactic constituency are pretty good in a few cases — good enough to provide evidence that syntax traffics in constituency — but they give way too many false positives and negatives. Their unreliability means real syntactic work must develop diagnostics which are more intricate and which carry much more theoretical baggage. If a theory is merely a hypothesis-machine, and the tools for testing those hypotheses depend on the theory, how can we avoid rigging the game in our favour?

Suppose we have two theories, T1 and T2, which are sets of statements regarding an empirical domain D. T1 has been rigorously vetted and found to be internally consistent, simple, and intelligible, and predicts 80% of the facts in D. T2 is rife with inconsistencies, hidden complexities, and opaque concepts, but covers 90% of the facts in D. Which is the better theory? Instrumentalism would suggest T2 is the superior theory due to its empirical coverage. Non-dogmatic people might disagree, but I suspect would all be uncomfortable with instrumentalism as the sole arbiter in this case.

The second problem, which exacerbates the first, is that there’s too much data, and it’s too easy to get even more. This has resulted in subdisciplines being further divided into several niches each devoted to a particular phenomenon or group of languages. Such a narrowing of the empirical domain, coupled with an instrumentalist view of theorizing, has frequently led to the development of competing theories of that domain, theories which are largely impenetrable to those conversant with the general theory but uninitiated with the niche in question. This is a different situation from the one described above. In this situation T1 and T2 might each cover 60% of a subdomain D’, but those 60% are overlapping. Each has a core set of facts that the other cannot, as yet, touch, so the two sides take turns claiming parts of the overlap as their sole territory, and no progress is made.

Often it’s the case that one of the competing specific theories is inconsistent with the general theory, but proponents of the other theory don’t use that fact in their arguments. In their estimation the data always trumps theory, regardless of how inherently theory-laden the description of the data is. It’s as if two factions were fighting each other with swords despite the fact that one side had a cache of rifles and ammunition that they decided not to use.

The third problem, one that has been noted by other theory-minded linguists here and here, is that the line between theoretical and empirical linguistics is blurry. To put it a bit more strongly, what is called “theoretical linguistics” is often empirical linguistics masquerading as theoretical. This assertion becomes clear when we look at the usual structure of a “theoretical syntax” paper in the abstract. First, a grammatical phenomenon is identified and demonstrate. After some discussion of previous work, the author demonstrates the results of some diagnostics and from those results gives a formal analysis of the phenomenon. If we translated this into the language of a mature science it would be indistinguishable from an experimental report. A phenomenon is identified and discussed, the results of some empirical techniques are reported, and an analysis is given.

You might ask “So what? Who cares what empirical syntacticians call themselves?” Well, if you’re a “theoretical syntactician,” then you might propose a modification of syntactic theory to make your empirical analysis work, and other “theoretical syntacticians” will accept those modifications and propose some modifications of their own. It doesn’t take too long in this cycle before the standard theory is rife with inconsistencies, hidden complexities, and opaque concepts. None of that matters, however, if your goal is just to cover the data.

Or, to take another common “theoretical” move, suppose we find an empirical generalization, G (e.g., All languages that allow X also allow Y), the difficult task of the theoretician is to show that G follows from independently motivated theoretical principles. The “theoretician,” on the other hand, has another path available, which is to restate G in “theoretical” terms (e.g., Functional head, H, is responsible for both X and Y), and then (maybe) go looking for some corroboration. Never mind that restating G in different terms does nothing to expand our understanding of why G holds, but understanding is always secondary for instrumentalism.

So, what’s to be done?

Reading this, you might think I don’t value empirical work in linguistics, which is simply not the case. Quite frankly, I am constantly in awe of linguists who can take a horrible mess of data and make even a modicum sense out of it. Empirical work has value, but linguistics has somehow managed to both over- and under-value it. We over-value it by tacitly embracing instrumentalism as our guiding philosophy. We under-value it by giving the title “theoretical linguist” a certain level of prestige. We think empirical work is easier and less-than. This has led us to under-value theoretical work, and view theoretical arguments as just gravy when they’re in our favour, and irrelevancies when they’re against us.

What we should strive for, is an appropriate balance between empirical and theoretical work. To get to that balance we must do the unthinkable and look to the humanities. To develop as a science, we ought to look at mature sciences, not as they are now, but as they developed. Put another way, we need to think historically. If we truly want our theory to explain the human language faculty, we need to accept that we will be explaining it to humans and designing a theory that another human can understand requires us to embrace our non-rational qualities like intuition and imagination.

In sum, we could all use a little humility. Maybe we’ll reach a point when instrumentalism will work for empirical linguistics, but we’re not there yet, and pretending we are won’t make it so.