Month: April 2023

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.

The idiocy of the debt ceiling

PNRJ current events, politics, public policy , , , , , , , , , ,

Apr 23 JDN 2460058

I thought we had put this behind us. I guess I didn’t think the Republicans would stop using the tactic once they saw it worked, but I had hoped that the Democrats would come up with a better permanent solution so that it couldn’t be used again. But they did not, and here we are again: Republicans are refusing to raise the debt ceiling, we have now hit that ceiling, and we are running out of time before we have to start shutting down services or defaulting on debt. There are talks ongoing that may yet get the ceiling raised in time, but we’re now cutting it very close. Already the risk that we might default or do something crazy is causing turmoil in financial markets.

Because US Treasury bonds are widely regarded as one of the world’s most secure assets, and the US dollar is the most important global reserve currency, the entire world’s financial markets get disrupted every time there is an issue with the US national debt, and the debt ceiling creates such disruptions on the regular for no good reason.

I will try to offer some of my own suggestions for what to do here, but first, I want to make something very clear: The debt ceiling should not exist. I don’t think most people understand just how truly idiotic the entire concept of a debt ceiling is. It seems practically designed to make our government dysfunctional.

This is not like a credit card limit, where your bank imposes a limit on how much you can borrow based on how much they think you are likely to be able to repay. A lot of people have been making that analogy, and I can see why it’s tempting; but as usual, it’s important to remember that government debt is not like personal debt.

As I said some years ago, US government debt is about as close as the world is ever likely to come to a perfect credit market: with no effort at all, borrow as much as you want at low, steady interest rates, and everyone will always be sure that you will pay it back on time. The debt ceiling is a limit imposed by the government itself—it is not imposed by our creditors, who would be more than happy to lend us more.

Also, I’d like to remind you that some of the US national debt is owned by the US government itself (is that really even “debt”?) and most of what’s left is owned by US individuals or corporations—only about a third is owed to foreign powers. Here is a detailed breakdown of who owns US national debt.

There is no reason to put an arbitrary cap on the amount the US government can borrow. The only reason anyone is at all worried about a default on the US national debt is because of this stupid arbitrary cap. If it didn’t exist, they would simply roll over more Treasury bonds to make the payments and everything would run smoothly. And this is normally what happens, when the Republicans aren’t playing ridiculous brinkmanship games.

As it is, they could simply print money to pay it—and at this point, maybe that’s what needs to happen. Mint the Coin already: Mint a $1 trillion platinum coin and deposit it in the Federal Reserve, and there you go, you’ve paid off a chunk of the debt. Sometimes stupid problems require stupid solutions.

Aren’t there reasons to be worried about the government borrowing too much? Yes, a little. The amount of concern most people have about this is wildly disproportionate to the actual problem, but yes, there are legitimate concerns about high national debt resulting in high interest rates and eventually forcing us to raise taxes or cut services. This is a slow-burn, long-term problem that by its very nature would never require a sudden, immediate solution; but it is a genuine concern we should be aware of.

But here’s the thing: That’s a conversation we should be having when we vote on the budget. Whenever we pass a government budget, it already includes detailed projections of tax revenue and spending that yield precise, accurate forecasts of the deficit and the debt. If Republicans are genuinely concerned that we are overspending on certain programs, they should propose budget cuts to those programs and get those cuts passed as part of the budget.

Once a budget is already passed, we have committed to spend that money. It has literally been signed into law that $X will be spend on program Y. At that point, you can’t simply cut the spending. If you think we’re spending too much, you needed to say that before we signed it into law. It’s too late now.

I’m always dubious of analogies between household spending and government spending, but if you really want one, think of it this way: Say your credit card company is offering to raise your credit limit, and you just signed a contract for some home improvements that would force you to run up your credit card past your current limit. Do you call the credit card company and accept the higher limit, or not? If you don’t, why don’t you? And what’s your plan for paying those home contractors? Even if you later decide that the home improvements were a bad idea, you already signed the contract! You can’t just back out!

This is why the debt ceiling is so absurd: It is a self-imposed limit on what you’re allowed to spend after you have already committed to spending it. The only sensible thing to do is to raise the debt ceiling high enough to account for the spending you’ve already committed to—or better yet, eliminate the ceiling entirely.

I think that when they last had a majority in both houses, the Democrats should have voted to make the debt ceiling ludicrously high—say $100 trillion. Then, at least for the foreseeable future, we wouldn’t have to worry about raising it, and could just pass budgets normally like a sane government. But they didn’t do that; they only raised it as much as was strictly necessary, thus giving the Republicans an opening now to refuse to raise it again.

And that is what the debt ceiling actually seems to accomplish: It gives whichever political party is least concerned about the public welfare a lever they can pull to disrupt the entire system whenever they don’t get things the way they want. If you absolutely do not care about the public good—and it’s quite clear at this point that most of the Republican leadership does not—then whenever you don’t get your way, you can throw a tantrum that threatens to destabilize the entire global financial system.

We need to stop playing their game. Do what you have to do to keep things running for now—but then get rid of the damn debt ceiling before they can use it to do even more damage.

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.

Will hydrogen make air travel sustainable?

PNRJ current events, futurism, public policy , , , , , , ,

Apr 9 JDN 2460042

Air travel is currently one of the most carbon-intensive activities anyone can engage in. Per passenger kilometer, airplanes emit about 8 times as much carbon as ships, 4 times as much as trains, and 1.5 times as much as cars. Living in a relatively eco-friendly city without a car and eating a vegetarian diet, I produce much less carbon than most First World citizens—except when I fly across the Atlantic a couple of times a year.

Until quite recently, most climate scientists believed that this was basically unavoidable, that simply sustaining the kind of power output required to keep an airliner in the air would always require carbon-intensive jet fuel. But in just the past few years, major breakthroughs have been made in using hydrogen propulsion.

The beautiful thing about hydrogen is that burning it simply produces water—no harmful pollution at all. It’s basically the cleanest possible fuel.


The simplest approach, which is actually quite old, but until recently didn’t seem viable, is the use of liquid hydrogen as airplane fuel.

We’ve been using liquid hydrogen as a rocket fuel for decades; so we knew it had enough energy density. (Actually its energy density is higher than conventional jet fuel.)

The problem with liquid hydrogen is that it must be kept extremely cold—it boils at 20 Kelvin. And once liquid hydrogen boils into gas, it builds up pressure very fast and easily permeates through most materials, so it’s extremely hard to contain. This makes it very difficult and expensive to handle.

But this isn’t the only way to use hydrogen, and may turn out to not be the best one.

There are now prototype aircraft that have flown using hydrogen fuel cells. These fuel cells can be fed with hydrogen gas—so no need to cool below 20 Kelvin. But then they can’t directly run the turbines; instead, these planes use electric turbines which are powered by the fuel cell.

Basically these are really electric aircraft. But whereas a lithium battery would be far too heavy, a hydrogen fuel cell is light enough for aviation use. In fact, hydrogen gas up to a certain pressure is lighter than air (it was often used for zeppelins, though, uh, occasionally catastrophically), so potentially the planes could use their own fuel tanks for buoyancy, landing “heavier” than they took off. (On the other hand it might make more sense to pressurize the hydrogen beyond that point, so that it will still be heavier than air—but perhaps still lighter than jet fuel!)

Of course, the technology is currently too untested and too expensive to be used on a wide scale. But this is how all technologies begin. It’s of course possible that we won’t be able to solve the engineering problems that currently make hydrogen-powered aircraft unaffordable; but several aircraft manufacturers are now investing in hydrogen research—suggesting that they at least believe there is a good chance we will.

There’s also the issue of where we get all the hydrogen. Hydrogen is extremely abundant—literally the most abundant baryonic matter in the universe—but most of what’s on Earth is locked up in water or hydrocarbons. Most of the hydrogen we currently make is produced by processing hydrocarbons (particularly methane), but that produces carbon emissions, so it wouldn’t solve the problem.

A better option is electrolysis: Using electricity to separate water into hydrogen and oxyen. But this requires a lot of energy—and necessarily, more energy than you can get out of burning the hydrogen later, since burning it basically is just putting the hydrogen and oxygen back together to make water.

Yet all is not lost, for while energy density is absolutely vital for an aircraft fuel, it’s not so important for a ground-based power plant. As an ultimate fuel source, hydrogen is a non-starter. But as an energy storage medium, it could be ideal.

The idea is this: We take the excess energy from wind and solar power plants, and use that energy to electrolyze water into hydrogen and oxygen. We then store that hydrogen and use it for fuel cells to run aircraft (and potentially other things as well). This ensures that the extra energy that renewable sources can generate in peak times doesn’t go to waste, and also provides us with what we need to produce clean-burning hydrogen fuel.

The basic technology for doing all this already exists. The current problem is cost. Under current conditions, it’s far more expensive to make hydrogen fuel than to make conventional jet fuel. Since fuel is one of the largest costs for airlines, even small increases in fuel prices matter a lot for the price of air travel; and these are not even small differences. Currently hydrogen costs over 10 times as much per kilogram, and its higher energy density isn’t enough to make up for that. For hydrogen aviation to be viable, that ratio needs to drop to more like 2 or 3—maybe even all the way to 1, since hydrogen is also more expensive to store than jet fuel (the gas needs high-pressure tanks, the liquid needs cryogenic cooling systems).

This means that, for the time being, it’s still environmentally responsible to reduce your air travel. Fly less often, always fly economy (more people on the plane means less carbon per passenger), and buy carbon offsets (they’re cheaper than you may think).

But in the long run, we may be able to have our cake and eat it too: If hydrogen aviation does become viable, we may not need to give up the benefits of routine air travel in order to reduce our carbon emissions.

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.