science and academia

The mental health crisis in academia

pnrj personal, science and academia , , , , , , , , , , ,

Apr 30 JDN 2460065

Why are so many academics anxious and depressed?

Depression and anxiety are much more prevalent among both students and faculty than they are in the general population. Unsurprisingly, women seem to have it a bit worse than men, and trans people have it worst of all.

Is this the result of systemic failings of the academic system? Before deciding that, one thing we should consider is that very smart people do seem to have a higher risk of depression.

There is a complex relationship between genes linked to depression and genes linked to intelligence, and some evidence that people of especially high IQ are more prone to depression; nearly 27% of Mensa members report mood disorders, compared to 10% of the general population.

(Incidentally, the stereotype of the weird, sickly nerd has a kernel of truth: the correlations between intelligence and autism, ADHD, allergies, and autoimmune disorders are absolutely real—and not at all well understood. It may be a general pattern of neural hyper-activation, not unlike what I posit in my stochastic overload model. The stereotypical nerd wears glasses, and, yes, indeed, myopia is also correlated with intelligence—and this seems to be mostly driven by genetics.)

Most of these figures are at least a few years old. If anything things are only worse now, as COVID triggered a surge in depression for just about everyone, academics included. It remains to be seen how much of this large increase will abate as things gradually return to normal, and how much will continue to have long-term effects—this may depend in part on how well we manage to genuinely restore a normal way of life and how well we can deal with long COVID.

If we assume that academics are a similar population to Mensa members (admittedly a strong assumption), then this could potentially explain why 26% of academic faculty are depressed—but not why nearly 40% of junior faculty are. At the very least, we junior faculty are about 50% more likely to be depressed than would be explained by our intelligence alone. And grad students have it even worse: Nearly 40% of graduate students report anxiety or depression, and nearly 50% of PhD students meet the criteria for depression. At the very least this sounds like a dual effect of being both high in intelligence and low in status—it’s those of us who have very little power or job security in academia who are the most depressed.

This suggests that, yes, there really is something wrong with academia. It may not be entirely the fault of the system—perhaps even a well-designed academic system would result in more depression than the general population because we are genetically predisposed. But it really does seem like there is a substantial environmental contribution that academic institutions bear some responsibility for.

I think the most obvious explanation is constant evaluation: From the time we are students at least up until we (maybe, hopefully, someday) get tenure, academics are constantly being evaluated on our performance. We know that this sort of evaluation contributes to anxiety and depression.

Don’t other jobs evaluate performance? Sure. But not constantly the way that academia does. This is especially obvious as a student, where everything you do is graded; but it largely continues once you are faculty as well.

For most jobs, you are concerned about doing well enough to keep your job or maybe get a raise. But academia has this continuous forward pressure: if you are a grad student or junior faculty, you can’t possibly keep your job; you must either move upward to the next stage or drop out. And academia has become so hyper-competitive that if you want to continue moving upward—and someday getting that tenure—you must publish in top-ranked journals, which have utterly opaque criteria and ever-declining acceptance rates. And since there are so few jobs available compared to the number of applicants, good enough is never good enough; you must be exceptional, or you will fail. Two thirds of PhD graduates seek a career in academia—but only 30% are actually in one three years later. (And honestly, three years is pretty short; there are plenty of cracks left to fall through between that and a genuinely stable tenured faculty position.)

Moreover, our skills are so hyper-specialized that it’s very hard to imagine finding work anywhere else. This grants academic institutions tremendous monopsony power over us, letting them get away with lower pay and worse working conditions. Even with an economics PhD—relatively transferable, all things considered—I find myself wondering who would actually want to hire me outside this ivory tower, and my feeble attempts at actually seeking out such employment have thus far met with no success.

I also find academia painfully isolating. I’m not an especially extraverted person; I tend to score somewhere near the middle range of extraversion (sometimes called an “ambivert”). But I still find myself craving more meaningful contact with my colleagues. We all seem to work in complete isolation from one another, even when sharing the same office (which is awkward for other reasons). There are very few consistent gatherings or good common spaces. And whenever faculty do try to arrange some sort of purely social event, it always seems to involve drinking at a pub and nobody is interested in providing any serious emotional or professional support.

Some of this may be particular to this university, or to the UK; or perhaps it has more to do with being at a certain stage of my career. In any case I didn’t feel nearly so isolated in graduate school; I had other students in my cohort and adjacent cohorts who were going through the same things. But I’ve been here two years now and so far have been unable to establish any similarly supportive relationships with colleagues.

There may be some opportunities I’m not taking advantage of: I’ve skipped a lot of research seminars, and I stopped going to those pub gatherings. But it wasn’t that I didn’t try them at all; it was that I tried them a few times and quickly found that they were not filling that need. At seminars, people only talked about the particular research project being presented. At the pub, people talked about almost nothing of serious significance—and certainly nothing requiring emotional vulnerability. The closest I think I got to this kind of support from colleagues was a series of lunch meetings designed to improve instruction in “tutorials” (what here in the UK we call discussion sections); there, at least, we could commiserate about feeling overworked and dealing with administrative bureaucracy.

There seem to be deep, structural problems with how academia is run. This whole process of universities outsourcing their hiring decisions to the capricious whims of high-ranked journals basically decides the entire course of our careers. And once you get to the point I have, now so disheartened with the process of publishing research that I can’t even engage with it, it’s not at all clear how it’s even possible to recover. I see no way forward, no one to turn to. No one seems to care how well I teach, if I’m not publishing research.

And I’m clearly not the only one who feels this way.

What behavioral economics needs

pnrj cognitive science, core principles, critique of neoclassical economics, heuristics and biases, mathematical models, science and academia , , , , , , , , , , , , , ,

Apr 16 JDN 2460049

The transition from neoclassical to behavioral economics has been a vital step forward in science. But lately we seem to have reached a plateau, with no major advances in the paradigm in quite some time.

It could be that there is work already being done which will, in hindsight, turn out to be significant enough to make that next step forward. But my fear is that we are getting bogged down by our own methodological limitations.

Neoclassical economics shared with us its obsession with mathematical sophistication. To some extent this was inevitable; in order to impress neoclassical economists enough to convert some of them, we had to use fancy math. We had to show that we could do it their way in order to convince them why we shouldn’t—otherwise, they’d just have dismissed us the way they had dismissed psychologists for decades, as too “fuzzy-headed” to do the “hard work” of putting everything into equations.

But the truth is, putting everything into equations was never the right approach. Because human beings clearly don’t think in equations. Once we write down a utility function and get ready to take its derivative and set it equal to zero, we have already distanced ourselves from how human thought actually works.

When dealing with a simple physical system, like an atom, equations make sense. Nobody thinks that the electron knows the equation and is following it intentionally. That equation simply describes how the forces of the universe operate, and the electron is subject to those forces.

But human beings do actually know things and do things intentionally. And while an equation could be useful for analyzing human behavior in the aggregate—I’m certainly not objecting to statistical analysis—it really never made sense to say that people make their decisions by optimizing the value of some function. Most people barely even know what a function is, much less remember calculus well enough to optimize one.

Yet right now, behavioral economics is still all based in that utility-maximization paradigm. We don’t use the same simplistic utility functions as neoclassical economists; we make them more sophisticated and realistic. Yet in that very sophistication we make things more complicated, more difficult—and thus in at least that respect, even further removed from how actual human thought must operate.

The worst offender here is surely Prospect Theory. I recognize that Prospect Theory predicts human behavior better than conventional expected utility theory; nevertheless, it makes absolutely no sense to suppose that human beings actually do some kind of probability-weighting calculation in their heads when they make judgments. Most of my students—who are well-trained in mathematics and economics—can’t even do that probability-weighting calculation on paper, with a calculator, on an exam. (There’s also absolutely no reason to do it! All it does it make your decisions worse!) This is a totally unrealistic model of human thought.

This is not to say that human beings are stupid. We are still smarter than any other entity in the known universe—computers are rapidly catching up, but they haven’t caught up yet. It is just that whatever makes us smart must not be easily expressible as an equation that maximizes a function. Our thoughts are bundles of heuristics, each of which may be individually quite simple, but all of which together make us capable of not only intelligence, but something computers still sorely, pathetically lack: wisdom. Computers optimize functions better than we ever will, but we still make better decisions than they do.

I think that what behavioral economics needs now is a new unifying theory of these heuristics, which accounts for not only how they work, but how we select which one to use in a given situation, and perhaps even where they come from in the first place. This new theory will of course be complex; there’s a lot of things to explain, and human behavior is a very complex phenomenon. But it shouldn’t be—mustn’t be—reliant on sophisticated advanced mathematics, because most people can’t do advanced mathematics (almost by construction—we would call it something different otherwise). If your model assumes that people are taking derivatives in their heads, your model is already broken. 90% of the world’s people can’t take a derivative.

I guess it could be that our cognitive processes in some sense operate as if they are optimizing some function. This is commonly posited for the human motor system, for instance; clearly baseball players aren’t actually solving differential equations when they throw and catch balls, but the trajectories that balls follow do in fact obey such equations, and the reliability with which baseball players can catch and throw suggests that they are in some sense acting as if they can solve them.

But I think that a careful analysis of even this classic example reveals some deeper insights that should call this whole notion into question. How do baseball players actually do what they do? They don’t seem to be calculating at all—in fact, if you asked them to try to calculate while they were playing, it would destroy their ability to play. They learn. They engage in practiced motions, acquire skills, and notice patterns. I don’t think there is anywhere in their brains that is actually doing anything like solving a differential equation. It’s all a process of throwing and catching, throwing and catching, over and over again, watching and remembering and subtly adjusting.

One thing that is particularly interesting to me about that process is that is astonishingly flexible. It doesn’t really seem to matter what physical process you are interacting with; as long as it is sufficiently orderly, such a method will allow you to predict and ultimately control that process. You don’t need to know anything about differential equations in order to learn in this way—and, indeed, I really can’t emphasize this enough, baseball players typically don’t.

In fact, learning is so flexible that it can even perform better than calculation. The usual differential equations most people would think to use to predict the throw of a ball would assume ballistic motion in a vacuum, which absolutely not what a curveball is. In order to throw a curveball, the ball must interact with the air, and it must be launched with spin; curving a baseball relies very heavily on the Magnus Effect. I think it’s probably possible to construct an equation that would fully predict the motion of a curveball, but it would be a tremendously complicated one, and might not even have an exact closed-form solution. In fact, I think it would require solving the Navier-Stokes equations, for which there is an outstanding Millennium Prize. Since the viscosity of air is very low, maybe you could get away with approximating using the Euler fluid equations.

To be fair, a learning process that is adapting to a system that obeys an equation will yield results that become an ever-closer approximation of that equation. And it is in that sense that a baseball player can be said to be acting as if solving a differential equation. But this relies heavily on the system in question being one that obeys an equation—and when it comes to economic systems, is that even true?

What if the reason we can’t find a simple set of equations that accurately describe the economy (as opposed to equations of ever-escalating complexity that still utterly fail to describe the economy) is that there isn’t one? What if the reason we can’t find the utility function people are maximizing is that they aren’t maximizing anything?

What behavioral economics needs now is a new approach, something less constrained by the norms of neoclassical economics and more aligned with psychology and cognitive science. We should be modeling human beings based on how they actually think, not some weird mathematical construct that bears no resemblance to human reasoning but is designed to impress people who are obsessed with math.

I’m of course not the first person to have suggested this. I probably won’t be the last, or even the one who most gets listened to. But I hope that I might get at least a few more people to listen to it, because I have gone through the mathematical gauntlet and earned my bona fides. It is too easy to dismiss this kind of reasoning from people who don’t actually understand advanced mathematics. But I do understand differential equations—and I’m telling you, that’s not how people think.

Implications of stochastic overload

pnrj cognitive science, critique of neoclassical economics, heuristics and biases, mathematical models, personal, science and academia , , , , , , , ,

Apr 2 JDN 2460037

A couple weeks ago I presented my stochastic overload model, which posits a neurological mechanism for the Yerkes-Dodson effect: Stress increases sympathetic activation, and this increases performance, up to the point where it starts to risk causing neural pathways to overload and shut down.

This week I thought I’d try to get into some of the implications of this model, how it might be applied to make predictions or guide policy.

One thing I often struggle with when it comes to applying theory is what actual benefits we get from a quantitative mathematical model as opposed to simply a basic qualitative idea. In many ways I think these benefits are overrated; people seem to think that putting something into an equation automatically makes it true and useful. I am sometimes tempted to try to take advantage of this, to put things into equations even though I know there is no good reason to put them into equations, simply because so many people seem to find equations so persuasive for some reason. (Studies have even shown that, particularly in disciplines that don’t use a lot of math, inserting a totally irrelevant equation into a paper makes it more likely to be accepted.)

The basic implications of the Yerkes-Dodson effect are already widely known, and utterly ignored in our society. We know that excessive stress is harmful to health and performance, and yet our entire economy seems to be based around maximizing the amount of stress that workers experience. I actually think neoclassical economics bears a lot of the blame for this, as neoclassical economists are constantly talking about “increasing work incentives”—which is to say, making work life more and more stressful. (And let me remind you that there has never been any shortage of people willing to work in my lifetime, except possibly briefly during the COVID pandemic. The shortage has always been employers willing to hire them.)

I don’t know if my model can do anything to change that. Maybe by putting it into an equation I can make people pay more attention to it, precisely because equations have this weird persuasive power over most people.

As far as scientific benefits, I think that the chief advantage of a mathematical model lies in its ability to make quantitative predictions. It’s one thing to say that performance increases with low levels of stress then decreases with high levels; but it would be a lot more useful if we could actually precisely quantify how much stress is optimal for a given person and how they are likely to perform at different levels of stress.

Unfortunately, the stochastic overload model can only make detailed predictions if you have fully specified the probability distribution of innate activation, which requires a lot of free parameters. This is especially problematic if you don’t even know what type of distribution to use, which we really don’t; I picked three classes of distribution because they were plausible and tractable, not because I had any particular evidence for them.

Also, we don’t even have standard units of measurement for stress; we have a vague notion of what more or less stressed looks like, but we don’t have the sort of quantitative measure that could be plugged into a mathematical model. Probably the best units to use would be something like blood cortisol levels, but then we’d need to go measure those all the time, which raises its own issues. And maybe people don’t even respond to cortisol in the same ways? But at least we could measure your baseline cortisol for awhile to get a prior distribution, and then see how different incentives increase your cortisol levels; and then the model should give relatively precise predictions about how this will affect your overall performance. (This is a very neuroeconomic approach.)

So, for now, I’m not really sure how useful the stochastic overload model is. This is honestly something I feel about a lot of the theoretical ideas I have come up with; they often seem too abstract to be usefully applicable to anything.

Maybe that’s how all theory begins, and applications only appear later? But that doesn’t seem to be how people expect me to talk about it whenever I have to present my work or submit it for publication. They seem to want to know what it’s good for, right now, and I never have a good answer to give them. Do other researchers have such answers? Do they simply pretend to?

Along similar lines, I recently had one of my students ask about a theory paper I wrote on international conflict for my dissertation, and after sending him a copy, I re-read the paper. There are so many pages of equations, and while I am confident that the mathematical logic is valid,I honestly don’t know if most of them are really useful for anything. (I don’t think I really believe that GDP is produced by a Cobb-Douglas production function, and we don’t even really know how to measure capital precisely enough to say.) The central insight of the paper, which I think is really important but other people don’t seem to care about, is a qualitative one: International treaties and norms provide an equilibrium selection mechanism in iterated games. The realists are right that this is cheap talk. The liberals are right that it works. Because when there are many equilibria, cheap talk works.

I know that in truth, science proceeds in tiny steps, building a wall brick by brick, never sure exactly how many bricks it will take to finish the edifice. It’s impossible to see whether your work will be an irrelevant footnote or the linchpin for a major discovery. But that isn’t how the institutions of science are set up. That isn’t how the incentives of academia work. You’re not supposed to say that this may or may not be correct and is probably some small incremental progress the ultimate impact of which no one can possibly foresee. You’re supposed to sell your work—justify how it’s definitely true and why it’s important and how it has impact. You’re supposed to convince other people why they should care about it and not all the dozens of other probably equally-valid projects being done by other researchers.

I don’t know how to do that, and it is agonizing to even try. It feels like lying. It feels like betraying my identity. Being good at selling isn’t just orthogonal to doing good science—I think it’s opposite. I think the better you are at selling your work, the worse you are at cultivating the intellectual humility necessary to do good science. If you think you know all the answers, you’re just bad at admitting when you don’t know things. It feels like in order to succeed in academia, I have to act like an unscientific charlatan.

Honestly, why do we even need to convince you that our work is more important than someone else’s? Are there only so many science points to go around? Maybe the whole problem is this scarcity mindset. Yes, grant funding is limited; but why does publishing my work prevent you from publishing someone else’s? Why do you have to reject 95% of the papers that get sent to you? Don’t tell me you’re limited by space; the journals are digital and searchable and nobody reads the whole thing anyway. Editorial time isn’t infinite, but most of the work has already been done by the time you get a paper back from peer review. Of course, I know the real reason: Excluding people is the main source of prestige.

The stochastic overload model

pnrj cognitive science, mathematical models, personal, science and academia , , , , , , , ,

The stochastic overload model

Mar 12 JDN 2460016

The next few posts are going to be a bit different, a bit more advanced and technical than usual. This is because, for the first time in several months at least, I am actually working on what could be reasonably considered something like theoretical research.

I am writing it up in the form of blog posts, because actually writing a paper is still too stressful for me right now. This also forces me to articulate my ideas in a clearer and more readable way, rather than dive directly into a morass of equations. It also means that even if I do never actually get around to finishing a paper, the idea is out there, and maybe someone else could make use of it (and hopefully give me some of the credit).

I’ve written previously about the Yerkes-Dodson effect: On cognitively-demanding tasks, increased stress increases performance, but only to a point, after which it begins decreasing it again. The effect is well-documented, but the mechanism is poorly understood.

I am currently on the wrong side of the Yerkes-Dodson curve, which is why I’m too stressed to write this as a formal paper right now. But that also gave me some ideas about how it may work.

I have come up with a simple but powerful mathematical model that may provide a mechanism for the Yerkes-Dodson effect.

This model is clearly well within the realm of a behavioral economic model, but it is also closely tied to neuroscience and cognitive science.

I call it the stochastic overload model.

First, a metaphor: Consider an engine, which can run faster or slower. If you increase its RPMs, it will output more power, and provide more torque—but only up to a certain point. Eventually it hits a threshold where it will break down, or even break apart. In real engines, we often include safety systems that force the engine to shut down as it approaches such a threshold.

I believe that human brains function on a similar principle. Stress increases arousal, which activates a variety of processes via the sympathetic nervous system. This activation improves performance on both physical and cognitive tasks. But it has a downside; especially on cognitively demanding tasks which required sustained effort, I hypothesize that too much sympathetic activation can result in a kind of system overload, where your brain can no longer handle the stress and processes are forced to shut down.

This shutdown could be brief—a few seconds, or even a fraction of a second—or it could be prolonged—hours or days. That might depend on just how severe the stress is, or how much of your brain it requires, or how prolonged it is. For purposes of the model, this isn’t vital. It’s probably easiest to imagine it being a relatively brief, localized shutdown of a particular neural pathway. Then, your performance in a task is summed up over many such pathways over a longer period of time, and by the law of large numbers your overall performance is essentially the average performance of all your brain systems.

That’s the “overload” part of the model. Now for the “stochastic” part.

Let’s say that, in the absence of stress, your brain has a certain innate level of sympathetic activation, which varies over time in an essentially chaotic, unpredictable—stochastic—sort of way. It is never really completely deactivated, and may even have some chance of randomly overloading itself even without outside input. (Actually, a potential role in the model for the personality trait neuroticism is an innate tendency toward higher levels of sympathetic activation in the absence of outside stress.)

Let’s say that this innate activation is x, which follows some kind of known random distribution F(x).

For simplicity, let’s also say that added stress s adds linearly to your level of sympathetic activation, so your overall level of activation is x + s.

For simplicity, let’s say that activation ranges between 0 and 1, where 0 is no activation at all and 1 is the maximum possible activation and triggers overload.

I’m assuming that if a pathway shuts down from overload, it doesn’t contribute at all to performance on the task. (You can assume it’s only reduced performance, but this adds complexity without any qualitative change.)

Since sympathetic activation improves performance, but can result in overload, your overall expected performance in a given task can be computed as the product of two terms:

[expected value of x + s, provided overload does not occur] * [probability overload does not occur]

E[x + s | x + s < 1] P[x + s < 1]

The first term can be thought of as the incentive effect: Higher stress promotes more activation and thus better performance.

The second term can be thought of as the overload effect: Higher stress also increases the risk that activation will exceed the threshold and force shutdown.

This equation actually turns out to have a remarkably elegant form as an integral (and here’s where I get especially technical and mathematical):

\int_{0}^{1-s} (x+s) dF(x)

The integral subsumes both the incentive effect and the overload effect into one term; you can also think of the +s in the integrand as the incentive effect and the 1-s in the limit of integration as the overload effect.

For the uninitated, this is probably just Greek. So let me show you some pictures to help with your intuition. These are all freehand sketches, so let me apologize in advance for my limited drawing skills. Think of this as like Arthur Laffer’s famous cocktail napkin.

Suppose that, in the absence of outside stress, your innate activation follows a distribution like this (this could be a normal or logit PDF; as I’ll talk about next week, logit is far more tractable):

As I start adding stress, this shifts the distribution upward, toward increased activation:

Initially, this will improve average performance.

But at some point, increased stress actually becomes harmful, as it increases the probability of overload.

And eventually, the probability of overload becomes so high that performance becomes worse than it was with no stress at all:

The result is that overall performance, as a function of stress, looks like an inverted U-shaped curve—the Yerkes-Dodson curve:

The precise shape of this curve depends on the distribution that we use for the innate activation, which I will save for next week’s post.

Where is the money going in academia?

pnrj core principles, critique of neoclassical economics, inequality, personal, science and academia , , , , , , , , , , , , ,

Feb 19 JDN 2459995

A quandary for you:

My salary is £41,000.

Annual tuition for a full-time full-fee student in my department is £23,000.

I teach roughly the equivalent of one full-time course (about 1/2 of one and 1/4 of two others; this is typically counted as “teaching 3 courses”, but if I used that figure, it would underestimate the number of faculty needed).

Each student takes about 5 or 6 courses at a time.

Why do I have 200 students?

If you multiply this out, the 200 students I teach, divided by the 6 instructors they have at one time, times the £23,000 they are paying… I should be bringing in over £760,000 for the university. Why am I paid only 5% of that?

Granted, there are other costs a university must bear aside from paying instructors. There are facilities, and administration, and services. And most of my students are not full-fee paying; that £23,000 figure really only applies to international students.

Students from Scotland pay only £1,820, but there aren’t very many of them, and public funding is supposed to make up that difference. Even students from the rest of the UK pay £9,250. And surely the average tuition paid has got to be close to that? Yet if we multiply that out, £9,000 times 200 divided by 6, we’re still looking at £300,000. So I’m still getting only 14%.

Where is the rest going?

This isn’t specific to my university by any means. It seems to be a global phenomenon. The best data on this seems to be from the US.

According to salary.com, the median salary for an adjunct professor in the US is about $63,000. This actually sounds high, given what I’ve heard from other entry-level faculty. But okay, let’s take that as our figure. (My pay is below this average, though how much depends upon the strength of the pound against the dollar. Currently the pound is weak, so quite a bit.)

Yet average tuition for out-of-state students at public college is $23,000 per year.

This means that an adjunct professor in the US with 200 students takes in $760,000 but receives $63,000. Where does that other $700,000 go?

If you think that it’s just a matter of paying for buildings, service staff, and other costs of running a university, consider this: It wasn’t always this way.

Since 1970, inflation-adjusted salaries for US academic faculty at public universities have risen a paltry 3.1%. In other words, basically not at all.

This is considerably slower than the growth of real median household income, which has risen almost 40% in that same time.

Over the same interval, nominal tuition has risen by over 2000%; adjusted for inflation, this is a still-staggering increase of 250%.

In other words, over the last 50 years, college has gotten three times as expensive, but faculty are still paid basically the same. Where is all this extra money going?

Part of the explanation is that public funding for colleges has fallen over time, and higher tuition partly makes up the difference. But private school tuition has risen just as fast, and their faculty salaries haven’t kept up either.

In their annual budget report, the University of Edinburgh proudly declares that their income increased by 9% last year. Let me assure you, my salary did not. (In fact, inflation-adjusted, my salary went down.) And their EBITDA—earnings before interest, taxes, depreciation, and amortization—was £168 million. Of that, £92 million was lost to interest and depreciation, but they don’t pay taxes at all, so their real net income was about £76 million. In the report, they include price changes of their endowment and pension funds to try to make this number look smaller, ending up with only £37 million, but that’s basically fiction; these are just stock market price drops, and they will bounce back.

Using similar financial alchemy, they’ve been trying to cut our pensions lately, because they say they “are too expensive” (because the stock market went down—nevermind that it’ll bounce back in a year or two). Fortunately, the unions are fighting this pretty hard. I wish they’d also fight harder to make them put people like me on the tenure track.

Had that £76 million been distributed evenly between all 5,000 of us faculty, we’d each get an extra £15,600.

Well, then, that solves part of the mystery in perhaps the most obvious, corrupt way possible: They’re literally just hoarding it.

And Edinburgh is far from the worst offender here. No, that would be Harvard, who are sitting on over $50 billion in assets. Since they have 21,000 students, that is over $2 million per student. With even a moderate return on its endowment, Harvard wouldn’t need to charge tuition at all.

But even then, raising my salary to £56,000 wouldn’t explain why I need to teach 200 students. Even that is still only 19% of the £300,000 those students are bringing in. But hey, then at least the primary service for which those students are here for might actually account for one-fifth of what they’re paying!

Now let’s considers administrators. Median salary for a university administrator in the US is about $138,000—twice what adjunct professors make.


Since 1970, that same time interval when faculty salaries were rising a pitiful 3% and tuition was rising a staggering 250%, how much did chancellors’ salaries increase? Over 60%.

Of course, the number of administrators is not fixed. You might imagine that with technology allowing us to automate a lot of administrative tasks, the number of administrators could be reduced over time. If that’s what you thought happened, you would be very, very wrong. The number of university administrators in the US has more than doubled since the 1980s. This is far faster growth than the number of students—and quite frankly, why should the number of administrators even grow with the number of students? There is a clear economy of scale here, yet it doesn’t seem to matter.

Combine those two facts: 60% higher pay times twice as many administrators means that universities now spend at least 3 times as much on administration as they did 50 years ago. (Why, that’s just about the proportional increase in tuition! Coincidence? I think not.)

Edinburgh isn’t even so bad in this regard. They have 6,000 administrative staff versus 5,000 faculty. If that already sounds crazy—more admins than instructors?—consider that the University of Michigan has 7,000 faculty but 19,000 administrators.

Michigan is hardly exceptional in this regard: Illinois UC has 2,500 faculty but nearly 8,000 administrators, while Ohio State has 7,300 faculty and 27,000 administrators. UCLA is even worse, with only 4,000 faculty but 26,000 administrators—a ratio of 6 to 1. It’s not the UC system in general, though: My (other?) alma mater of UC Irvine somehow supports 5,600 faculty with only 6,400 administrators. Yes, that’s right; compared to UCLA, UCI has 40% more faculty but 76% fewer administrators. (As far as students? UCLA has 47,000 while UCI has 36,000.)

At last, I think we’ve solved the mystery! Where is all the money in academia going? Administrators.

They keep hiring more and more of them, and paying them higher and higher salaries. Meanwhile, they stop hiring tenure-track faculty and replace them with adjuncts that they can get away with paying less. And then, whatever they manage to save that way, they just squirrel away into the endowment.

A common right-wing talking point is that more institutions should be “run like a business”. Well, universities seem to have taken that to heart. Overpay your managers, underpay your actual workers, and pocket the savings.

I’m old enough to be President now.

pnrj current events, personal, science and academia , , , , , ,

Jan 22 JDN 2459967

When this post goes live, I will have passed my 35th birthday. This is old enough to be President of the United States, at least by law. (In practice, no POTUS has been less than 42.)

Not that I will ever be President. I have neither the wealth nor the charisma to run any kind of national political campaign. I might be able to get elected to some kind of local office at some point, like a school board or a city water authority. But I’ve been eligible to run for such offices for quite awhile now, and haven’t done so; nor do I feel particularly inclined at the moment.

No, the reason this birthday feels so significant is the milestone it represents. By this age, most people have spouses, children, careers. I have a spouse. I don’t have kids. I sort of have a career.

I have a job, certainly. I work for relatively decent pay. Not excellent, not what I was hoping for with a PhD in economics, but enough to live on (anywhere but an overpriced coastal metropolis). But I can’t really call that job a career, because I find large portions of it unbearable and I have absolutely no job security. In fact, I have the exact opposite: My job came with an explicit termination date from the start. (Do the people who come up with these short-term postdoc positions understand how that feels? It doesn’t seem like they do.)

I missed the window to apply for academic jobs that start next year. If I were happy here, this would be fine; I still have another year left on my contract. But I’m not happy here, and that is a grievous understatement. Working here is clearly the most important situational factor contributing to my ongoing depression. So I really ought to be applying to every alternative opportunity I can find—but I can’t find the will to try it, or the self-confidence to believe that my attempts could succeed if I did.

Then again, I’m not sure I should be applying to academic positions at all. If I did apply to academic positions, they’d probably be teaching-focused ones, since that’s the one part of my job I’m actually any good at. I’ve more or less written off applying to major research institutions; I don’t think I would get hired anyway, and even if I did, the pressure to publish is so unbearable that I think I’d be just as miserable there as I am here.

On the other hand, I can’t be sure that I would be so miserable even at another research institution; maybe with better mentoring and better administration I could be happy and successful in academic research after all.

The truth is, I really don’t know how much of my misery is due to academia in general, versus the British academic system, versus Edinburgh as an institution, versus starting work during the pandemic, versus the experience of being untenured faculty, versus simply my own particular situation. I don’t know if working at another school would be dramatically better, a little better, or just the same. (If it were somehow worse—which frankly seems hard to arrange—I would literally just quit immediately.)

I guess if the University of Michigan offered me an assistant professor job right now, I would take it. But I’m confident enough that they wouldn’t offer it to me that I can’t see the point in applying. (Besides, I missed the application windows this year.) And I’m not even sure that I would be happy there, despite the fact that just a few years ago I would have called it a dream job.

That’s really what I feel most acutely about turning 35: The shattering of dreams.

I thought I had some idea of how my life would go. I thought I knew what I wanted. I thought I knew what would make me happy.

The weirdest part it that it isn’t even that different from how I’d imagined it. If you’d asked me 10 or even 20 years ago what my career would be like at 35, I probably would have correctly predicted that I would have a PhD and be working at a major research university. 10 years ago I would have correctly expected it to be a PhD in economics; 20, I probably would have guessed physics. In both cases I probably would have thought I’d be tenured by now, or at least on the tenure track. But a postdoc or adjunct position (this is sort of both?) wouldn’t have been utterly shocking, just vaguely disappointing.

The biggest error by my past self was thinking that I’d be happy and successful in this career, instead of barely, desperately hanging on. I thought I’d have published multiple successful papers by now, and be excited to work on a new one. I imagined I’d also have published a book or two. (The fact that I self-published a nonfiction book at 16 but haven’t published any nonfiction ever since would be particularly baffling to my 15-year-old self, and is particularly depressing to me now.) I imagined myself becoming gradually recognized as an authority in my field, not languishing in obscurity; I imagined myself feeling successful and satisfied, not hopeless and depressed.

It’s like the dark Mirror Universe version of my dream job. It’s so close to what I thought I wanted, but it’s also all wrong. I finally get to touch my dreams, and they shatter in my hands.

When you are young, birthdays are a sincere cause for celebration; you look forward to the new opportunities the future will bring you. I seem to be now at the age where it no longer feels that way.

What is it with EA and AI?

pnrj cognitive science, computer science, ethics, futurism, inequality, public policy, science and academia , , , , , , , , , ,

Jan 1 JDN 2459946

Surprisingly, most Effective Altruism (EA) leaders don’t seem to think that poverty alleviation should be our top priority. Most of them seem especially concerned about long-term existential risk, such as artificial intelligence (AI) safety and biosecurity. I’m not going to say that these things aren’t important—they certainly are important—but here are a few reasons I’m skeptical that they are really the most important the way that so many EA leaders seem to think.

1. We don’t actually know how to make much progress at them, and there’s only so much we can learn by investing heavily in basic research on them. Whereas, with poverty, the easy, obvious answer turns out empirically to be extremely effective: Give them money.

2. While it’s easy to multiply out huge numbers of potential future people in your calculations of existential risk (and this is precisely what people do when arguing that AI safety should be a top priority), this clearly isn’t actually a good way to make real-world decisions. We simply don’t know enough about the distant future of humanity to be able to make any kind of good judgments about what will or won’t increase their odds of survival. You’re basically just making up numbers. You’re taking tiny probabilities of things you know nothing about and multiplying them by ludicrously huge payoffs; it’s basically the secular rationalist equivalent of Pascal’s Wager.

2. AI and biosecurity are high-tech, futuristic topics, which seem targeted to appeal to the sensibilities of a movement that is still very dominated by intelligent, nerdy, mildly autistic, rich young White men. (Note that I say this as someone who very much fits this stereotype. I’m queer, not extremely rich and not entirely White, but otherwise, yes.) Somehow I suspect that if we asked a lot of poor Black women how important it is to slightly improve our understanding of AI versus giving money to feed children in Africa, we might get a different answer.

3. Poverty eradication is often characterized as a “short term” project, contrasted with AI safety as a “long term” project. This is (ironically) very short-sighted. Eradication of poverty isn’t just about feeding children today. It’s about making a world where those children grow up to be leaders and entrepreneurs and researchers themselves. The positive externalities of economic development are staggering. It is really not much of an exaggeration to say that fascism is a consequence of poverty and unemployment.

4. Currently the main thing that most Effective Altruism organizations say they need most is “talent”; how many millions of person-hours of talent are we leaving on the table by letting children starve or die of malaria?

5. Above all, existential risk can’t really be what’s motivating people here. The obvious solutions to AI safety and biosecurity are not being pursued, because they don’t fit with the vision that intelligent, nerdy, young White men have of how things should be. Namely: Ban them. If you truly believe that the most important thing to do right now is reduce the existential risk of AI and biotechnology, you should support a worldwide ban on research in artificial intelligence and biotechnology. You should want people to take all necessary action to attack and destroy institutions—especially for-profit corporations—that engage in this kind of research, because you believe that they are threatening to destroy the entire world and this is the most important thing, more important than saving people from starvation and disease. I think this is really the knock-down argument; when people say they think that AI safety is the most important thing but they don’t want Google and Facebook to be immediately shut down, they are either confused or lying. Honestly I think maybe Google and Facebook should be immediately shut down for AI safety reasons (as well as privacy and antitrust reasons!), and I don’t think AI safety is yet the most important thing.

Why aren’t people doing that? Because they aren’t actually trying to reduce existential risk. They just think AI and biotechnology are really interesting, fascinating topics and they want to do research on them. And I agree with that, actually—but then they need stop telling people that they’re fighting to save the world, because they obviously aren’t. If the danger were anything like what they say it is, we should be halting all research on these topics immediately, except perhaps for a very select few people who are entrusted with keeping these forbidden secrets and trying to find ways to protect us from them. This may sound radical and extreme, but it is not unprecedented: This is how we handle nuclear weapons, which are universally recognized as a global existential risk. If AI is really as dangerous as nukes, we should be regulating it like nukes. I think that in principle it could be that dangerous, and may be that dangerous someday—but it isn’t yet. And if we don’t want it to get that dangerous, we don’t need more AI researchers, we need more regulations that stop people from doing harmful AI research! If you are doing AI research and it isn’t directly involved specifically in AI safety, you aren’t saving the world—you’re one of the people dragging us closer to the cliff! Anything that could make AI smarter but doesn’t also make it safer is dangerous. And this is clearly true of the vast majority of AI research, and frankly to me seems to also be true of the vast majority of research at AI safety institutes like the Machine Intelligence Research Institute.

Seriously, look through MIRI’s research agenda: It’s mostly incredibly abstract and seems completely beside the point when it comes to preventing AI from taking control of weapons or governments. It’s all about formalizing Bayesian induction. Thanks to you, Skynet can have a formally computable approximation to logical induction! Truly we are saved. Only two of their papers, on “Corrigibility” and “AI Ethics”, actually struck me as at all relevant to making AI safer. The rest is largely abstract mathematics that is almost literally navel-gazing—it’s all about self-reference. Eliezer Yudkowsky finds self-reference fascinating and has somehow convinced an entire community that it’s the most important thing in the world. (I actually find some of it fascinating too, especially the paper on “Functional Decision Theory”, which I think gets at some deep insights into things like why we have emotions. But I don’t see how it’s going to save the world from AI.)

Don’t get me wrong: AI also has enormous potential benefits, and this is a reason we may not want to ban it. But if you really believe that there is a 10% chance that AI will wipe out humanity by 2100, then get out your pitchforks and your EMP generators, because it’s time for the Butlerian Jihad. A 10% chance of destroying all humanity is an utterly unacceptable risk for any conceivable benefit. Better that we consign ourselves to living as we did in the Neolithic than risk something like that. (And a globally-enforced ban on AI isn’t even that; it’s more like “We must live as we did in the 1950s.” How would we survive!?) If you don’t want AI banned, maybe ask yourself whether you really believe the risk is that high—or are human brains just really bad at dealing with small probabilities?

I think what’s really happening here is that we have a bunch of guys (and yes, the EA and especially AI EA-AI community is overwhelmingly male) who are really good at math and want to save the world, and have thus convinced themselves that being really good at math is how you save the world. But it isn’t. The world is much messier than that. In fact, there may not be much that most of us can do to contribute to saving the world; our best options may in fact be to donate money, vote well, and advocate for good causes.

Let me speak Bayesian for a moment: The prior probability that you—yes, you, out of all the billions of people in the world—are uniquely positioned to save it by being so smart is extremely small. It’s far more likely that the world will be saved—or doomed—by people who have power. If you are not the head of state of a large country or the CEO of a major multinational corporation, I’m sorry; you probably just aren’t in a position to save the world from AI.

But you can give some money to GiveWell, so maybe do that instead?

How to fix economics publishing

pnrj core principles, personal, science and academia , , , , , , , , , ,

Aug 7 JDN 2459806

The current system of academic publishing in economics is absolutely horrible. It seems practically designed to undermine the mental health of junior faculty.

1. Tenure decisions, and even most hiring decisions, are almost entirely based upon publication in five (5) specific journals.

2. One of those “top five” journals is owned by Elsevier, a corrupt monopoly that has no basis for its legitimacy yet somehow controls nearly one-fifth of all scientific publishing.

3. Acceptance rates in all of these journals are between 5% and 10%—greatly decreased from what they were a generation or two ago. Given a typical career span, the senior faculty evaluating you on whether you were published in these journals had about a three times better chance to get their own papers published there than you do.

4. Submissions are only single-blinded, so while you have no idea who is reading your papers, they know exactly who you are and can base their decision on whether you are well-known in the profession—or simply whether they like you.

5. Simultaneous submissions are forbidden, so when submitting to journals you must go one at a time, waiting to hear back from one before trying the next.

6. Peer reviewers are typically unpaid and generally uninterested, and so procrastinate as long as possible on doing their reviews.

7. As a result, review times for a paper are often measured in months, for every single cycle.

So, a highly successful paper goes like this: You submit it to a top journal, wait three months, it gets rejected. You submit it to another one, wait another four months, it gets rejected. You submit it to a third one, wait another two months, and you are told to revise and resubmit. You revise and resubmit, wait another three months, and then finally get accepted.

You have now spent an entire year getting one paper published. And this was a success.

Now consider a paper that doesn’t make it into a top journal. You submit, wait three months, rejected; you submit again, wait four months, rejected; you submit again, wait two months, rejected. You submit again, wait another five months, rejected; you submit to the fifth and final top-five, wait another four months, and get rejected again.

Now, after a year and a half, you can turn to other journals. You submit to a sixth journal, wait three months, rejected. You submit to a seventh journal, wait four months, get told to revise and resubmit. You revise and resubmit, wait another two months, and finally—finally, after two years—actually get accepted, but not to a top-five journal. So it may not even help you get tenure, unless maybe a lot of people cite it or something.

And what if you submit to a seventh, an eighth, a ninth journal, and still keep getting rejected? At what point do you simply give up on that paper and try to move on with your life?

That’s a trick question: Because what really happens, at least to me, is I can’t move on with my life. I get so disheartened from all the rejections of that paper that I can’t bear to look at it anymore, much less go through the work of submitting it to yet another journal that will no doubt reject it again. But worse than that, I become so depressed about my academic work in general that I become unable to move on to any other research either. And maybe it’s me, but it isn’t just me: 28% of academic faculty suffer from severe depression, and 38% from severe anxiety. And that’s across all faculty—if you look just at junior faculty it’s even worse: 43% of junior academic faculty suffer from severe depression. When a problem is that prevalent, at some point we have to look at the system that’s making us this way.

I can blame the challenges of moving across the Atlantic during a pandemic, and the fact that my chronic migraines have been the most frequent and severe they have been in years, but the fact remains: I have accomplished basically nothing towards the goal of producing publishable research in the past year. I have two years left at this job; if I started right now, I might be able to get something published before my contract is done. Assuming that the project went smoothly, I could start submitting it as soon as it was done, and it didn’t get rejected as many times as the last one.

I just can’t find the motivation to do it. When the pain is so immediate and so intense, and the rewards are so distant and so uncertain, I just can’t bring myself to do the work. I had hoped that talking about this with my colleagues would help me cope, but it hasn’t; in fact it only makes me seem to feel worse, because so few of them seem to understand how I feel. Maybe I’m talking to the wrong people; maybe the ones who understand are themselves suffering too much to reach out to help me. I don’t know.

But it doesn’t have to be this way. Here are some simple changes that could make the entire process of academic publishing in economics go better:

1. Boycott Elsevier and all for-profit scientific journal publishers. Stop reading their journals. Stop submitting to their journals. Stop basing tenure decisions on their journals. Act as though they don’t exist, because they shouldn’t—and then hopefully soon they won’t.

2. Peer reviewers should be paid for their time, and in return required to respond promptly—no more than a few weeks. A lack of response should be considered a positive vote on that paper.

3. Allow simultaneous submissions; if multiple journals accept, let the author choose between them. This is already how it works in fiction publishing, which you’ll note has not collapsed.

4. Increase acceptance rates. You are not actually limited by paper constraints anymore; everything is digital now. Most of the work—even in the publishing process—already has to be done just to go through peer review, so you may as well publish it. Moreover, most papers that are submitted are actually worthy of publishing, and this whole process is really just an idiotic status hierarchy. If the prestige of your journal decreases because you accept more papers, we are measuring prestige wrong. Papers should be accepted something like 50% of the time, not 5-10%.

5. Double blind submissions, and insist on ethical standards that maintain that blinding. No reviewer should know whether they are reading the work of a grad student or a Nobel Laureate. Reputation should mean nothing; scientific rigor should mean everything.

And, most radical of all, what I really need in my life right now:

6. Faculty should not have to submit their own papers. Each university department should have administrative staff whose job it is to receive papers from their faculty, format them appropriately, and submit them to journals. They should deal with all rejections, and only report to the faculty member when they have received an acceptance or a request to revise and resubmit. Faculty should simply do the research, write the papers, and then fire and forget them. We have highly specialized skills, and our valuable time is being wasted on the clerical tasks of formatting and submitting papers, which many other people could do as well or better. Worse, we are uniquely vulnerable to the emotional impact of the rejection—seeing someone else’s paper rejected is an entirely different feeling from having your own rejected.

Do all that, and I think I could be happy to work in academia. As it is, I am seriously considering leaving and never coming back.

I finally have a published paper.

pnrj personal, science and academia , , , , , , , , , , , , , , , ,

Jun 12 JDN 2459773

Here it is, my first peer-reviewed publication: “Imperfect Tactic Collusion and Asymmetric Price Transmission”, in the Journal of Economic Behavior and Organization.

Due to the convention in economics that authors are displayed alphabetically, I am listed third of four, and will be typically collapsed into “Bulutay et. al.”. I don’t actually think it should be “Julius et. al.”; I think Dave Hales did the most important work, and I wanted it to be “Hales et. al.”; but anything non-alphabetical is unusual in economics, and it would have taken a strong justification to convince the others to go along with it. This is a very stupid norm (and I attribute approximately 20% of Daron Acemoglu’s superstar status to it), but like any norm, it is difficult to dislodge.

I thought I would feel different when this day finally came. I thought I would feel joy, or at least satisfaction. I had been hoping that satisfaction would finally spur me forward in resubmitting my single-author paper, “Experimental Public Goods Games with Progressive Taxation”, so I could finally get a publication that actually does have “Julius (2022)” (or, at this rate, 2023, 2024…?). But that motivating satisfaction never came.

I did feel some vague sense of relief: Thank goodness, this ordeal is finally over and I can move on. But that doesn’t have the same motivating force; it doesn’t make me want to go back to the other papers I can now hardly bear to look at.

This reaction (or lack thereof?) could be attributed to circumstances: I have been through a lot lately. I was already overwhelmed by finishing my dissertation and going on the job market, and then there was the pandemic, and I had to postpone my wedding, and then when I finally got a job we had to suddenly move abroad, and then it was awful finding a place to live, and then we actually got married (which was lovely, but still stressful), and it took months to get my medications sorted with the NHS, and then I had a sudden resurgence of migraines which kept me from doing most of my work for weeks, and then I actually caught COVID and had to deal with that for a few weeks too. So it really isn’t too surprising that I’d be exhausted and depressed after all that.

Then again, it could be something deeper. I didn’t feel this way about my wedding. That genuinely gave me the joy and satisfaction that I had been expecting; I think it really was the best day of my life so far. So it isn’t as if I’m incapable of these feelings under my current state.

Rather, I fear that I am becoming more permanently disillusioned with academia. Now that I see how the sausage is made, I am no longer so sure I want to be one of the people making it. Publishing that paper didn’t feel like I had accomplished something, or even made some significant contribution to human knowledge. In fact, the actual work of publication was mostly done by my co-authors, because I was too overwhelmed by the job market at the time. But what I did have to do—and what I’ve tried to do with my own paper—felt like a miserable, exhausting ordeal.

More and more, I’m becoming convinced that a single experiment tells us very little, and we are being asked to present each one as if it were a major achievement when it’s more like a single brick in a wall.

But whatever new knowledge our experiments may have gleaned, that part was done years ago. We could have simply posted the draft as a working paper on the web and moved on, and the world would know just as much and our lives would have been a lot easier.

Oh, but then it would not have the imprimatur of peer review! And for our careers, that means absolutely everything. (Literally, when they’re deciding tenure, nothing else seems to matter.) But for human knowledge, does it really mean much? The more referee reports I’ve read, the more arbitrary they feel to me. This isn’t an objective assessment of scientific merit; it’s the half-baked opinion of a single randomly chosen researcher who may know next to nothing about the topic—or worse, have a vested interest in defending a contrary paradigm.

Yes, of course, what gets through peer review is of considerably higher quality than any randomly-selected content on the Internet. (The latter can be horrifically bad.) But is this not also true of what gets submitted for peer review? In fact, aren’t many blogs written by esteemed economists (say, Krugman? Romer? Nate Silver?) of considerably higher quality as well, despite having virtually none of the gatekeepers? I think Krugman’s blog is nominally edited by the New York Times, and Silver has a whole staff at FiveThirtyEight (they’re hiring, in fact!), but I’m fairly certain Romer just posts whatever he wants like I do. Of course, they had to establish their reputations (Krugman and Romer each won a Nobel). But still, it seems like maybe peer-review isn’t doing the most important work here.

Even blogs by far less famous economists (e.g. Miles Kimball, Brad DeLong) are also very good, and probably contribute more to advancing the knowledge of the average person than any given peer-reviewed paper, simply because they are more readable and more widely read. What we call “research” means going from zero people knowing a thing to maybe a dozen people knowing it; “publishing” means going from a dozen to at most a thousand; to go from a thousand to a billion, we call that “education”.

They all matter, of course; but I think we tend to overvalue research relative to education. A world where a few people know something is really not much better than a world where nobody does, while a world where almost everyone knows something can be radically superior. And the more I see just how far behind the cutting edge of research most economists are—let alone most average people—the more apparent it becomes to me that we are investing far too much in expanding that cutting edge (and far, far too much in gatekeeping who gets to do that!) and not nearly enough in disseminating that knowledge to humanity.

I think maybe that’s why finally publishing a paper felt so anticlimactic for me. I know that hardly anyone will ever actually read the damn thing. Just getting to this point took far more effort than it should have; dozens if not hundreds of hours of work, months of stress and frustration, all to satisfy whatever arbitrary criteria the particular reviewers happened to use so that we could all clear this stupid hurdle and finally get that line on our CVs. (And we wonder why academics are so depressed?) Far from being inspired to do the whole process again, I feel as if I have finally emerged from the torture chamber and may at last get some chance for my wounds to heal.

Even publishing fiction was not this miserable. Don’t get me wrong; it was miserable, especially for me, as I hate and fear rejection to the very core of my being in a way most people do not seem to understand. But there at least the subjectivity and arbitrariness of the process is almost universally acknowledged. Agents and editors don’t speak of your work being “flawed” or “wrong”; they don’t even say it’s “unimportant” or “uninteresting”. They say it’s “not a good fit” or “not what we’re looking for right now”. (Journal editors sometimes make noises like that too, but there’s always a subtext of “If this were better science, we’d have taken it.”) Unlike peer reviewers, they don’t come back with suggestions for “improvements” that are often pointless or utterly infeasible.

And unlike peer reviewers, fiction publishers acknowledge their own subjectivity and that of the market they serve. Nobody really thinks that Fifty Shades of Grey was good in any deep sense; but it was popular and successful, and that’s all the publisher really cares about. As a result, failing to be the next Fifty Shades of Grey ends up stinging a lot less than failing to be the next article in American Economic Review. Indeed, I’ve never had any illusions that my work would be popular among mainstream economists. But I once labored under the belief that it would be more important that it is true; and I guess I now consider that an illusion.

Moreover, fiction writers understand that rejection hurts; I’ve been shocked how few academics actually seem to. Nearly every writing conference I’ve ever been to has at least one seminar on dealing with rejection, often several; at academic conferences, I’ve literally never seen one. There seems to be a completely different mindset among academics—at least, the successful, tenured ones—about the process of peer review, what it means, even how it feels. When I try to talk with my mentors about the pain of getting rejected, they just… don’t get it. They offer me guidance on how to deal with anger at rejection, when that is not at all what I feel—what I feel is utter, hopeless, crushing despair.

There is a type of person who reacts to rejection with anger: Narcissists. (Look no further than the textbook example, Donald Trump.) I am coming to fear that I’m just not narcissistic enough to be a successful academic. I’m not even utterly lacking in narcissism: I am almost exactly average for a Millennial on the Narcissistic Personality Inventory. I score fairly high on Authority and Superiority (I consider myself a good leader and a highly competent individual) but very low on Exploitativeness and Self-Sufficiency (I don’t like hurting people and I know no man is an island). Then again, maybe I’m just narcissistic in the wrong way: I score quite low on “grandiose narcissism”, but relatively high on “vulnerable narcissism”. I hate to promote myself, but I find rejection devastating. This combination seems to be exactly what doesn’t work in academia. But it seems to be par for the course among writers and poets. Perhaps I have the mind of a scientist, but I have the soul of a poet. (Send me through the wormhole! Please? Please!?)

Will we ever have the space opera future?

pnrj ethics, futurism, personal, science and academia, tribal paradigm , , , , , , , , , , , , ,

May 22 JDN 2459722

Space opera has long been a staple of science fiction. Like many natural categories, it’s not that easy to define; it has something to do with interstellar travel, a variety of alien species, grand events, and a big, complicated world that stretches far beyond any particular story we might tell about it.

Star Trek is the paradigmatic example, and Star Wars also largely fits, but there are numerous of other examples, including most of my favorite science fiction worlds: Dune, the Culture, Mass Effect, Revelation Space, the Liaden, Farscape, Babylon 5, the Zones of Thought.

I think space opera is really the sort of science fiction I most enjoy. Even when it is dark, there is still something aspirational about it. Even a corrupt feudal transplanetary empire or a terrible interstellar war still means a universe where people get to travel the stars.

How likely is it that we—and I mean ‘we’ in the broad sense, humanity and its descendants—will actually get the chance to live in such a universe?

First, let’s consider the most traditional kind of space opera, the Star Trek world, where FTL is commonplace and humans interact as equals with a wide variety of alien species that are different enough to be interesting, but similar enough to be relatable.

This, sad to say, is extremely unlikely. FTL is probably impossible, or if not literally impossible then utterly infeasible by any foreseeable technology. Yes, the Alcubierre drive works in theory… all you need is tons of something that has negative mass.

And while, by sheer probability, there almost have to be other sapient lifeforms somewhere out there in this vast universe, our failure to contact or even find clear evidence of any of them for such a long period suggests that they are either short-lived or few and far between. Moreover, any who do exist are likely to be radically different from us and difficult to interact with at all, much less relate to on a personal level. Maybe they don’t have eyes or ears; maybe they live only in liquid hydrogen or molten lead; maybe they communicate entirely by pheromones that are toxic to us.

Does this mean that the aspirations of space opera are ultimately illusory? Is it just a pure fantasy that will forever be beyond us? Not necessarily.

I can see two other ways to create a very space-opera-like world, one of which is definitely feasible, and the other is very likely to be. Let’s start with the one that’s definitely feasible—indeed so feasible we will very likely get to experience it in our own lifetimes.

That is to make it a simulation. An MMO video game, in a way, but something much grander than any MMO that has yet been made. Not just EVE and No Man’s Sky, not just World of Warcraft and Minecraft and Second Life, but also Facebook and Instagram and Zoom and so much more. Oz from Summer Wars; OASIS from Ready Player One. A complete, multifaceted virtual reality in which we can spend most if not all of our lives. One complete with not just sight and sound, but also touch, smell, even taste.

Since it’s a simulation, we can make our own rules. If we want FTL and teleportation, we can have them. (And I would like to note that in fact teleportation is available in EVE, No Man’s Sky, World of Warcraft, Minecraft, and even Second Life. It’s easy to implement in a simulation, and it really seems to be something people want to have.) If we want to meet—or even be—people from a hundred different sapient species, some more humanoid than others, we can. Each of us could rule entire planets, command entire starfleets.

And we could do this, if not right now, today, then very, very soon—the VR hardware is finally maturing and the software capability already exists if there is a development team with the will and the skills (and the budget) to do it. We almost certainly will do this—in fact, we’ll do it hundreds or thousands of different ways. You need not be content with any particular space opera world, when you can choose from a cornucopia of them; and fantasy worlds too, and plenty of other kinds of worlds besides.

Yet, I admit, there is something missing from that future. While such a virtual-reality simulation might reach the point where it would be fair to say it’s no longer simply a “video game”, it still won’t be real. We won’t actually be Vulcans or Delvians or Gek or Asari. We will merely pretend to be. When we take off the VR suit at the end of the day, we will still be humans, and still be stuck here on Earth. And even if most of the toil of maintaining this society and economy can be automated, there will still be some time we have to spend living ordinary lives in ordinary human bodies.

So, is there some chance that we might really live in a space-opera future? Where we will meet actual, flesh-and-blood people who have blue skin, antennae, or six limbs? Where we will actually, physically move between planets, feeling the different gravity beneath our feet and looking up at the alien sky?

Yes. There is a way this could happen. Not now, not for awhile yet. We ourselves probably won’t live to see it. But if humanity manages to continue thriving for a few more centuries, and technology continues to improve at anything like its current pace, then that day may come.

We won’t have FTL, so we’ll be bounded by the speed of light. But the speed of light is still quite fast. It can get you to Mars in minutes, to Jupiter in hours, and even to Alpha Centauri in a voyage that wouldn’t shock Magellan or Zheng He. Leaving this arm of the Milky Way, let alone traveling to another galaxy, is out of the question (at least if you ever want to come back while anyone you know is still alive—actually as a one-way trip it’s surprisingly feasible thanks to time dilation).

This means that if we manage to invent a truly superior kind of spacecraft engine, one which combines the high thrust of a hydrolox rocket with the high specific impulse of an ion thruster—and that is physically possible, because it’s well within what nuclear rockets ought to be capable of—then we could travel between planets in our solar system, and maybe even to nearby solar systems, in reasonable amounts of time. The world of The Expanse could therefore be in reach (well, the early seasons anyway), where human colonies have settled on Mars and Ceres and Ganymede and formed their own new societies with their own unique cultures.

We may yet run into some kind of extraterrestrial life—bacteria probably, insects maybe, jellyfish if we’re lucky—but we probably ever won’t actually encounter any alien sapients. If there are any, they are probably too primitive to interact with us, or they died out millennia ago, or they’re simply too far away to reach.

But if we cannot find Vulcans and Delvians and Asari, then we can become them. We can modify ourselves with cybernetics, biotechnology, or even nanotechnology, until we remake ourselves into whatever sort of beings we want to be. We may never find a whole interplanetary empire ruled by a race of sapient felinoids, but if furry conventions are any indication, there are plenty of people who would make themselves into sapient felinoids if given the opportunity.

Such a universe would actually be more diverse than a typical space opera. There would be no “planets of hats“, no entire societies of people acting—or perhaps even looking—the same. The hybridization of different species is almost by definition impossible, but when the ‘species’ are cosmetic body mods, we can combine them however we like. A Klingon and a human could have a child—and for that matter the child could grow up and decide to be a Turian.

Honestly there are only two reasons I’m not certain we’ll go this route:

One, we’re still far too able and willing to kill each other, so who knows if we’ll even make it that long. There’s also still plenty of room for some sort of ecological catastrophe to wipe us out.

And two, most people are remarkably boring. We already live in a world where one could go to work every day wearing a cape, a fursuit, a pirate outfit, or a Starfleet uniform, and yet people don’t let you. There’s nothing infeasible about me delivering a lecture dressed as a Kzin Starfleet science officer, and nor would it even particularly impair my ability to deliver the lecture well; and yet I’m quite certain it would be greatly frowned upon if I were to do so, and could even jeopardize my career (especially since I don’t have tenure).

Would it be distracting to the students if I were to do something like that? Probably, at least at first. But once they got used to it, it might actually make them feel at ease. If it were a social norm that lecturers—and students—can dress however they like (perhaps limited by local decency regulations, though those, too, often seem overly strict), students might show up to class in bunny pajamas or pirate outfits or full-body fursuits, but would that really be a bad thing? It could in fact be a good thing, if it helps them express their own identity and makes them more comfortable in their own skin.

But no, we live in a world where the mainstream view is that every man should wear exactly the same thing at every formal occasion. I felt awkward at the AEA conference because my shirt had color.

This means that there is really one major obstacle to building the space opera future: Social norms. If we don’t get to live in this world one day, it will be because the world is ruled by the sort of person who thinks that everyone should be the same.