Category Archives: Life as a Physicist

How Much Academic Attrition Is Too Much?

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Have you seen “population pyramids“? They’re diagrams that show snapshots of a population, how many people there are of each age. They can give you an intuition for how a population is changing, and where the biggest hurdles are to survival.

I wonder what population pyramids would look like for academia. In each field and subfield, how many people are PhD students, postdocs, and faculty?

If every PhD student was guaranteed to become faculty, and the number of faculty stayed fixed, you could roughly estimate what this pyramid would have to look like. An estimate for the US might take an average 7-year PhD, two postdoc positions at 3 years each, followed by a 30-year career as faculty, and estimate the proportions of each stage based on proportions of each scholar’s life. So you’d have roughly one PhD student per four faculty, and one postdoc per five. In Europe, with three-year PhDs, the proportion of PhD students decreases further, and in a world where people are still doing at least two postdocs you expect significantly more postdocs than PhDs.

Of course, the world doesn’t look like that at all, because the assumptions are wrong.

The number of faculty doesn’t stay fixed, for one. When population is growing in the wider world, new universities open in new population centers, and existing universities find ways to expand. When population falls, enrollments shrink, and universities cut back.

But this is a minor perturbation compared to the much more obvious difference: most PhD students do not stay in academia. A single professor may mentor many PhDs at the same time, and potentially several postdocs. Most of those people aren’t staying.

You can imagine someone trying to fix this by fiat, setting down a fixed ratio between PhD students, postdocs, and faculty. I’ve seen partial attempts at this. When I applied for grants at the University of Copenhagen, I was told I had to budget at least half of my hires as PhD students, not postdocs, which makes me wonder if they were trying to force careers to default to one postdoc position, rather than two. More likely, they hadn’t thought about it.

Zero attrition doesn’t really make sense, anyway. Some people are genuinely better off leaving: they made a mistake when they started, or they changed over time. Sometimes new professions arise, and the best way in is from an unexpected direction. I’ve talked to people who started data science work in the early days, before there really were degrees in it, who felt a physics PhD had been the best route possible to that world. Similarly, some move into policy, or academic administration, or found a startup. And if we think there are actually criteria to choose better or worse academics (which I’m a bit skeptical of), then presumably some people are simply not good enough, and trying to filter them out earlier is irresponsible when they still don’t have enough of a track record to really judge.

How much attrition should be there is the big question, and one I don’t have an answer for. In academia, when so much of these decisions are made by just a few organizations, it seems like a question that someone should have a well-considered answer to. But so far, it’s unclear to me that anyone does.

It also makes me think, a bit, about how these population pyramids work in industry. There there is no overall control. Instead, there’s a web of incentives, many of them decades-delayed from the behavior they’re meant to influence, leaving each individual to try to predict as well as they can. If companies only hire senior engineers, no-one gets a chance to start a career, and the population of senior engineers dries up. Eventually, those companies have to settle for junior engineers. (Or, I guess, ex-academics.) It sounds like it should lead to the kind of behavior biologists model in predators and prey, wild swings in population modeled by a differential equation. But maybe there’s something that tamps down those wild swings.

A Paper With a Bluesky Account

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People make social media accounts for their pets. Why not a scientific paper?

Anthropologist Ed Hagen made a Bluesky account for his recent preprint, “Menopause averted a midlife energetic crisis with help from older children and parents: A simulation study.” The paper’s topic itself is interesting (menopause is surprisingly rare among mammals, he has a plausible account as to why), but not really the kind of thing I cover here.

Rather, it’s his motivation that’s interesting. Hagen didn’t make the account out of pure self-promotion or vanity. Instead, he’s promoting it as a novel approach to scientific publishing. Unlike Twitter, Bluesky is based on an open, decentralized protocol. Anyone can host an account compatible with Bluesky on their own computer, and anyone with the programming know-how can build a computer program that reads Bluesky posts. That means that nothing actually depends on Bluesky, in principle: the users have ultimate control.

Hagen’s idea, then, is that this could be a way to fulfill the role of scientific journals without channeling money and power to for-profit publishers. If each paper is hosted on a scientist’s own site, the papers can link to each other via following each other. Scientists on Bluesky can follow or like the paper, or comment on and discuss it, creating a way to measure interest from the scientific community and aggregate reviews, two things journals are supposed to cover.

I must admit, I’m skeptical. The interface really seems poorly-suited for this. Hagen’s paper’s account is called @menopause-preprint.edhagen.net. What happens when he publishes another paper on menopause, what will he call it? How is he planning to keep track of interactions from other scientists with an account for every single paper, won’t swapping between fifteen Bluesky accounts every morning get tedious? Or will he just do this with papers he wants to promote?

I applaud the general idea. Decentralized hosting seems like a great way to get around some of the problems of academic publishing. But this will definitely take a lot more work, if it’s ever going to be viable on a useful scale.

Still, I’ll keep an eye on it, and see if others give it a try. Stranger things have happened.

Academia Tracks Priority, Not Provenance

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A recent Correspondence piece in Nature Machine Intelligence points at an issue with using LLMs to write journal articles. LLMs are trained on enormous amounts of scholarly output, but the result is quite opaque: it is usually impossible to tell which sources influence a specific LLM-written text. That means that when a scholar uses an LLM, they may get a result that depends on another scholar’s work, without realizing it or documenting it. The ideas’ provenance gets lost, and the piece argues this is damaging, depriving scholars of credit and setting back progress.

It’s a good point. Provenance matters. If we want to prioritize funding for scholars whose ideas have the most impact, we need a way to track where ideas arise.

However, current publishing norms make essentially no effort to do this. Academic citations are not used to track provenance, and they are not typically thought of as tracking provenance. Academic citations track priority.

Priority is a central value in scholarship, with a long history. We give special respect to the first person to come up with an idea, make an observation, or do a calculation, and more specifically, the first person to formally publish it. We do this even if the person’s influence was limited, and even if the idea was rediscovered independently later on. In an academic context, being first matters.

In a paper, one is thus expected to cite the sources that have priority, that came up with an idea first. Someone who fails to do so will get citation request emails, and reviewers may request revisions to the paper to add in those missing citations.

One may also cite papers that were helpful, even if they didn’t come first. Tracking provenance in this way can be nice, a way to give direct credit to those who helped and point people to useful resources. But it isn’t mandatory in the same way. If you leave out a secondary source and your paper doesn’t use anything original to that source (like new notation), you’re much less likely to get citation request emails, or revision requests from reviewers. Provenance is just much lower priority.

In practice, academics track provenance in much less formal ways. Before citations, a paper will typically have an Acknowledgements section, where the authors thank those who made the paper possible. This includes formal thanks to funding agencies, but also informal thanks for “helpful discussions” that don’t meet the threshold of authorship.

If we cared about tracking provenance, those acknowledgements would be crucial information, an account of whose ideas directly influenced the ideas in the paper. But they’re not treated that way. No-one lists the number of times they’ve been thanked for helpful discussions on their CV, or in a grant application, no-one considers these discussions for hiring or promotion. You can’t look them up on an academic profile or easily graph them in a metascience paper. Unlike citations, unlike priority, there is essentially no attempt to measure these tracks of provenance in any organized way.

Instead, provenance is often the realm of historians or history-minded scholars, writing long after the fact. For academics, the fact that Yang and Mills published their theory first is enough, we call it Yang-Mills theory. For those studying the history, the story is murkier: it looks like Pauli came up with the idea first, and did most of the key calculations, but didn’t publish when it looked to him like the theory couldn’t describe the real world. What’s more, there is evidence suggesting that Yang knew about Pauli’s result, that he had read a letter from him on the topic, that the idea’s provenance goes back to Pauli. But Yang published, Pauli didn’t. And in the way academia has worked over the last 75 years, that claim of priority is what actually mattered.

Should we try to track provenance? Maybe. Maybe the emerging ubiquitousness of LLMs should be a wakeup call, a demand to improve our tracking of ideas, both in artificial and human neural networks. Maybe we need to demand interpretability from our research tools, to insist that we can track every conclusion back to its evidence for every method we employ, to set a civilizational technological priority on the accurate valuation of information.

What we shouldn’t do, though, is pretend that we just need to go back to what we were doing before.

Energy Is That Which Is Conserved

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In school, kids learn about different types of energy. They learn about solar energy and wind energy, nuclear energy and chemical energy, electrical energy and mechanical energy, and potential energy and kinetic energy. They learn that energy is conserved, that it can never be created or destroyed, but only change form. They learn that energy makes things happen, that you can use energy to do work, that energy is different from matter.

Some, between good teaching and good students, manage to impose order on the jumble of concepts and terms. Others end up envisioning the whole story a bit like Pokemon, with different types of some shared “stuff”.

Energy isn’t “stuff”, though. So what is it? What relates all these different types of things?

Energy is something which is conserved.

The mathematician Emmy Noether showed that, when the laws of physics are symmetrical, they come with a conserved quantity. For example, because the laws of the physics are the same from place to place, momentum is conserved. Similarly, because the laws of physics are the same from one time to another, Noether’s theorem states that there must be some quantity related to time, some number we can calculate, that is conserved, even as other things change. We call that number energy.

If energy is that simple, why are there all those types?

Energy is a number we can calculate. It’s a number we can calculate for different things. If you have a detailed description of how something in physics works, you can use that description to calculate that thing’s energy. In school, you memorize formulas like \frac{1}{2}m v^2 and m g h. These are all formulas that, with a bit more knowledge, you could calculate. They are the things that, for a something that meets the conditions, are conserved. They are things that, according to Noether’s theorem, stay the same.

Because of this, you shouldn’t think of energy as a substance, or a fuel. Energy is something we can do: we physicists, and we students of physics. We can take a physical system, and see what about it ought to be conserved. Energy is an action, a calculation, a conceptual tool that can be used to make predictions.

Most things are, in the end.

Mandatory Dumb Acronyms

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Sometimes, the world is silly for honest, happy reasons. And sometimes, it’s silly for reasons you never even considered.

Scientific projects often have acronyms, some of which are…clever, let’s say. Astronomers are famous for acronyms. Read this list, and you can find examples from 2D-FRUTTI and ABRACADABRA to WOMBAT and YORIC. Some of these aren’t even “really” acronyms, using letters other than the beginning of each word, multiple letters from a word, or both. (An egregious example from that list: VESTALE from “unVEil the darknesS of The gAlactic buLgE”.)

But here’s a pattern you’ve probably not noticed. I suggest that you should see more of these…clever…acronyms in projects in Europe, and they should show up in a wider range of fields, not just astronomy. And the reason why, is the European Research Council.

In the US, scientific grants are spread out among different government agencies. Typical grants are small, the kind of thing that lets a group share a postdoc every few years, with different types of grants covering projects of different scales.

The EU, instead, has the European Research Council, or ERC, with a flagship series of grants covering different career stages: Starting, Consolidator, and Advanced. Unlike most US grants, these are large (supporting multiple employees over several years), individual (awarded to a single principal investigator, not a collaboration) and general (the ERC uses the same framework across multiple fields, from physics to medicine to history).

That means there are a lot of medium-sized research projects in Europe that are funded by an ERC grant. And each of them are required to have an acronym.

Why? Who knows? “Acronym” is simply one of the un-skippable entries in the application forms, with a pre-set place of honor in their required grant proposal format. Nobody checks whether it’s a “real acronym”, so in practice it often isn’t, turning into some sort of catchy short name with “acronym vibes”. It, like everything else on these forms, is optimized to catch the attention of a committee of scientists who really would rather be doing something else, often discussed and refined by applicants’ mentors and sometimes even dedicated university staff.

So if you run into a scientist in Europe who proudly leads a group with a cutesy, vaguely acronym-adjacent name? And you keep running into these people?

It’s not a coincidence, and it’s not just scientists’ sense of humor. It’s the ERC.

What You’re Actually Scared of in Impostor Syndrome

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Academics tend to face a lot of impostor syndrome. Something about a job with no clear criteria for success, where you could always in principle do better and you mostly only see the cleaned-up, idealized version of others’ work, is a recipe for driving people utterly insane with fear.

The way most of us talk about that fear, it can seem like a cognitive bias, like a failure of epistemology. “Competent people think they’re less competent than they are,” the less-discussed half of the Dunning-Kruger effect.

(I’ve talked about it that way before. And, in an impostor-syndrome-inducing turn of events, I got quoted in a news piece in Nature about it.)

There’s something missing in that perspective, though. It doesn’t really get across how impostor syndrome feels. There’s something very raw about it, something that feels much more personal and urgent than an ordinary biased self-assessment.

To get at the core of it, let me ask a question: what happens to impostors?

The simple answer, the part everyone will admit to, is to say they stop getting grants, or stop getting jobs. Someone figures out they can’t do what they claim, and stops choosing them to receive limited resources. Pretty much anyone with impostor syndrome will say that they fear this: the moment that they reach too far, and the world decides they aren’t worth the money after all.

In practice, it’s not even clear that that happens. You might have people in your field who are actually thought of as impostors, on some level. People who get snarked about behind their back, people where everyone rolls their eyes when they ask a question at a conference and the question just never ends. People who are thought of as shiny storytellers without substance, who spin a tale for journalists but aren’t accomplishing anything of note. Those people…aren’t facing consequences at all, really! They keep getting the grants, they keep finding the jobs, and the ranks of people leaving for industry are instead mostly filled with those you respect.

Instead, I think what we fear when we feel impostor syndrome isn’t the obvious consequence, or even the real consequence, but something more primal. Primatologists and psychologists talk about our social brain, and the role of ostracism. They talk about baboons who piss off the alpha and get beat up and cast out of the group, how a social animal on their own risks starvation and becomes easy prey for bigger predators.

I think when we wake up in a cold sweat remembering how we had no idea what that talk was about, and were too afraid to ask, it’s a fear on that level that’s echoing around in our heads. That the grinding jags of adrenaline, the run-away-and-hide feeling of never being good enough, the desperate unsteadiness of trying to sound competent when you’re sure that you’re not and will get discovered at any moment…that’s not based on any realistic fears about what would happen if you got caught. That’s your monkey-brain, telling you a story drilled down deep by evolution.

Does that help? I’m not sure. If you manage to tell your inner monkey that it won’t get eaten by a lion if its friends stop liking it, let me know!

Publishing Isn’t Free, but SciPost Makes It Cheaper

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I’ve mentioned SciPost a few times on this blog. They’re an open journal in every sense you could think of: diamond open-access scientific publishing on an open-source platform, run with open finances. They even publish their referee reports. They’re aiming to cover not just a few subjects, but a broad swath of academia, publishing scientists’ work in the most inexpensive and principled way possible and challenging the dominance of for-profit journals.

And they’re struggling.

SciPost doesn’t charge university libraries for access, they let anyone read their articles for free. And they don’t charge authors Article Processing Charges (or APCs), they let anyone publish for free. All they do is keep track of which institutions those authors are affiliated with, calculate what fraction of their total costs comes from them, and post it in a nice searchable list on their website.

And amazingly, for the last nine years, they’ve been making that work.

SciPost encourages institutions to pay their share, mostly by encouraging authors to bug their bosses until they do. SciPost will also quite happily accept more than an institution’s share, and a few generous institutions do just that, which is what has kept them afloat so far. But since nothing compels anyone to pay, most organizations simply don’t.

From an economist’s perspective, this is that most basic of problems, the free-rider problem. People want scientific publication to be free, but it isn’t. Someone has to pay, and if you don’t force someone to do it, then the few who pay will be exploited by the many who don’t.

There’s more worth saying, though.

First, it’s worth pointing out that SciPost isn’t paying the same cost everyone else pays to publish. SciPost has a stripped-down system, without any physical journals or much in-house copyediting, based entirely on their own open-source software. As a result, they pay about 500 euros per article. Compare this to the fees negotiated by particle physics’ SCOAP3 agreement, which average to closer to 1000 euros, and realize that those fees are on the low end: for-profit journals tend to make their APCs higher in order to, well, make a profit.

(By the way, while it’s tempting to think of for-profit journals as greedy, I think it’s better to think of them as not cost-effective. Profit is an expense, like the interest on a loan: a payment to investors in exchange for capital used to set up the business. The thing is, online journals don’t seem to need that kind of capital, especially when they’re based on code written by academics in their spare time. So they can operate more cheaply as nonprofits.)

So when an author publishes in SciPost instead of a journal with APCs, they’re saving someone money, typically their institution or their grant. This would happen even if their institution paid their share of SciPost’s costs. (But then they would pay something rather than nothing, hence free-rider problem.)

If an author instead would have published in a closed-access journal, the kind where you have to pay to read the articles and university libraries pay through the nose to get access? Then you don’t save any money at all, your library still has to pay for the journal. You only save money if everybody at the institution stops using the journal. This one is instead a collective action problem.

Collective action problems are hard, and don’t often have obvious solutions. Free-rider problems do suggest an obvious solution: why not just charge?

In SciPost’s case, there are philosophical commitments involved. Their desire to attribute costs transparently and equally means dividing a journal’s cost among all its authors’ institutions, a cost only fully determined at the end of the year, which doesn’t make for an easy invoice.

More to the point, though, charging to publish is directly against what the Open Access movement is about.

That takes some unpacking, because of course, someone does have to pay. It probably seems weird to argue that institutions shouldn’t have to pay charges to publish papers…instead, they should pay to publish papers.

SciPost itself doesn’t go into detail about this, but despite how weird it sounds when put like I just did, there is a difference. Charging a fee to publish means that anyone who publishes needs to pay a fee. If you’re working in a developing country on a shoestring budget, too bad, you have to pay the fee. If you’re an amateur mathematician who works in a truck stop and just puzzled through something amazing, too bad, you have to pay the fee.

Instead of charging a fee, SciPost asks for support. I have to think that part of the reason is that they want some free riders. There are some people who would absolutely not be able to participate in science without free riding, and we want their input nonetheless. That means to support them, others need to give more. It means organizations need to think about SciPost not as just another fee, but as a way they can support the scientific process as a whole.

That’s how other things work, like the arXiv. They get support from big universities and organizations and philanthropists, not from literally everyone. It seems a bit weird to do that for a single scientific journal among many, though, which I suspect is part of why institutions are reluctant to do it. But for a journal that can save money like SciPost, maybe it’s worth it.

I Have a Theory

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“I have a theory,” says the scientist in the book. But what does that mean? What does it mean to “have” a theory?

First, there’s the everyday sense. When you say “I have a theory”, you’re talking about an educated guess. You think you know why something happened, and you want to check your idea and get feedback. A pedant would tell you you don’t really have a theory, you have a hypothesis. It’s “your” hypothesis, “your theory”, because it’s what you think happened.

The pedant would insist that “theory” means something else. A theory isn’t a guess, even an educated guess. It’s an explanation with evidence, tested and refined in many different contexts in many different ways, a whole framework for understanding the world, the most solid knowledge science can provide. Despite the pedant’s insistence, that isn’t the only way scientists use the word “theory”. But it is a common one, and a central one. You don’t really “have” a theory like this, though, except in the sense that we all do. These are explanations with broad consensus, things you either know of or don’t, they don’t belong to one person or another.

Except, that is, if one person takes credit for them. We sometimes say “Darwin’s theory of evolution”, or “Einstein’s theory of relativity”. In that sense, we could say that Einstein had a theory, or that Darwin had a theory.

Sometimes, though, “theory” doesn’t mean this standard official definition, even when scientists say it. And that changes what it means to “have” a theory.

For some researchers, a theory is a lens with which to view the world. This happens sometimes in physics, where you’ll find experts who want to think about a situation in terms of thermodynamics, or in terms of a technique called Effective Field Theory. It happens in mathematics, where some choose to analyze an idea with category theory not to prove new things about it, but just to translate it into category theory lingo. It’s most common, though, in the humanities, where researchers often specialize in a particular “interpretive framework”.

For some, a theory is a hypothesis, but also a pet project. There are physicists who come up with an idea (maybe there’s a variant of gravity with mass! maybe dark energy is changing!) and then focus their work around that idea. That includes coming up with ways to test whether the idea is true, showing the idea is consistent, and understanding what variants of the idea could be proposed. These ideas are hypotheses, in that they’re something the scientist thinks could be true. But they’re also ideas with many moving parts that motivate work by themselves.

Taken to the extreme, this kind of “having” a theory can go from healthy science to political bickering. Instead of viewing an idea as a hypothesis you might or might not confirm, it can become a platform to fight for. Instead of investigating consistency and proposing tests, you focus on arguing against objections and disproving your rivals. This sometimes happens in science, especially in more embattled areas, but it happens much more often with crackpots, where people who have never really seen science done can decide it’s time for their idea, right or wrong.

Finally, sometimes someone “has” a theory that isn’t a hypothesis at all. In theoretical physics, a “theory” can refer to a complete framework, even if that framework isn’t actually supposed to describe the real world. Some people spend time focusing on a particular framework of this kind, understanding its properties in the hope of getting broader insights. By becoming an expert on one particular theory, they can be said to “have” that theory.

Bonus question: in what sense do string theorists “have” string theory?

You might imagine that string theory is an interpretive framework, like category theory, with string theorists coming up with the “string version” of things others understand in other ways. This, for the most part, doesn’t happen. Without knowing whether string theory is true, there isn’t much benefit in just translating other things to string theory terms, and people for the most part know this.

For some, string theory is a pet project hypothesis. There is a community of people who try to get predictions out of string theory, or who investigate whether string theory is consistent. It’s not a huge number of people, but it exists. A few of these people can get more combative, or make unwarranted assumptions based on dedication to string theory in particular: for example, you’ll see the occasional argument that because something is difficult in string theory it must be impossible in any theory of quantum gravity. You see a spectrum in the community, from people for whom string theory is a promising project to people for whom it is a position that needs to be defended and argued for.

For the rest, the question of whether string theory describes the real world takes a back seat. They’re people who “have” string theory in the sense that they’re experts, and they use the theory primarily as a mathematical laboratory to learn broader things about how physics works. If you ask them, they might still say that they hypothesize string theory is true. But for most of these people, that question isn’t central to their work.

Physics Gets Easier, Then Harder

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Some people have stories about an inspiring teacher who introduced them to their life’s passion. My story is different: I became a physicist due to a famously bad teacher.

My high school was, in general, a good place to learn science, but physics was the exception. The teacher at the time had a bad reputation, and while I don’t remember exactly why I do remember his students didn’t end up learning much physics. My parents were aware of the problem, and aware that physics was something I might have a real talent for. I was already going to take math at the university, having passed calculus at the high school the year before, taking advantage of a program that let advanced high school students take free university classes. Why not take physics at the university too?

This ended up giving me a huge head-start, letting me skip ahead to the fun stuff when I started my Bachelor’s degree two years later. But in retrospect, I’m realizing it helped me even more. Skipping high-school physics didn’t just let me move ahead: it also let me avoid a class that is in many ways more difficult than university physics.

High school physics is a mess of mind-numbing formulas. How is velocity related to time, or acceleration to displacement? What’s the current generated by a changing magnetic field, or the magnetic field generated by a current? Students learn a pile of apparently different procedures to calculate things that they usually don’t particularly care about.

Once you know some math, though, you learn that most of these formulas are related. Integration and differentiation turn the mess of formulas about acceleration and velocity into a few simple definitions. Understand vectors, and instead of a stack of different rules about magnets and circuits you can learn Maxwell’s equations, which show how all of those seemingly arbitrary rules fit together in one reasonable package.

This doesn’t just happen when you go from high school physics to first-year university physics. The pattern keeps going.

In a textbook, you might see four equations to represent what Maxwell found. But once you’ve learned special relativity and some special notation, they combine into something much simpler. Instead of having to keep track of forces in diagrams, you can write down a Lagrangian and get the laws of motion with a reliable procedure. Instead of a mess of creation and annihilation operators, you can use a path integral. The more physics you learn, the more seemingly different ideas get unified, the less you have to memorize and the more just makes sense. The more physics you study, the easier it gets.

Until, that is, it doesn’t anymore. A physics education is meant to catch you up to the state of the art, and it does. But while the physics along the way has been cleaned up, the state of the art has not. We don’t yet have a unified set of physical laws, or even a unified way to do physics. Doing real research means once again learning the details: quantum computing algorithms or Monte Carlo simulation strategies, statistical tools or integrable models, atomic lattices or topological field theories.

Most of the confusions along the way were research problems in their own day. Electricity and magnetism were understood and unified piece by piece, one phenomenon after another before Maxwell linked them all together, before Lorentz and Poincaré and Einstein linked them further still. Once a student might have had to learn a mess of particles with names like J/Psi, now they need just six types of quarks.

So if you’re a student now, don’t despair. Physics will get easier, things will make more sense. And if you keep pursuing it, eventually, it will stop making sense once again.

Government Science Funding Isn’t a Precision Tool

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People sometimes say there is a crisis of trust in science. In controversial subjects, from ecology to health, increasingly many people are rejecting not only mainstream ideas, but the scientists behind them.

I think part of the problem is media literacy, but not in the way you’d think. When we teach media literacy, we talk about biased sources. If a study on cigarettes is funded by the tobacco industry or a study on climate change is funded by an oil company, we tell students to take a step back and consider that the scientists might be biased.

That’s a worthwhile lesson, as far as it goes. But it naturally leads to another idea. Most scientific studies aren’t funded by companies, most studies are funded by the government. If you think the government is biased, does that mean the studies are too?

I’m going to argue here that government science funding is a very different thing than corporations funding individual studies. Governments do have an influence on scientists, and a powerful one, but that influence is diffuse and long-term. They don’t have control over the specific conclusions scientists reach.

If you picture a stereotypical corrupt scientist, you might imagine all sorts of perks. They might get extra pay from corporate consulting fees. Maybe they get invited to fancy dinners, go to corporate-sponsored conferences in exotic locations, and get gifts from the company.

Grants can’t offer any of that, because grants are filtered through a university. When a grant pays a scientist’s salary, the university pays less to compensate, instead reducing their teaching responsibilities or giving them a slightly better chance at future raises. Any dinners or conferences have to obey not only rules from the grant agency (a surprising number of grants these days can’t pay for alcohol) but from the university as well, which can set a maximum on the price of a dinner or require people to travel economy using a specific travel agency. They also have to be applied for: scientists have to write their planned travel and conference budget, and the committee evaluating grants will often ask if that budget is really necessary.

Actual corruption isn’t the only thing we teach news readers to watch out for. By funding research, companies can choose to support people who tend to reach conclusions they agree with, keep in contact through the project, then publicize the result with a team of dedicated communications staff.

Governments can’t follow up on that level of detail. Scientific work is unpredictable, and governments try to fund a wide breadth of scientific work, so they have to accept that studies will not usually go as advertised. Scientists pivot, finding new directions and reaching new opinions, and government grant agencies don’t have the interest or the staff to police them for it. They also can’t select very precisely, with committees that often only know bits and pieces about the work they’re evaluating because they have to cover so many different lines of research. And with the huge number of studies funded, the number that can be meaningfully promoted by their comparatively small communications staff is only a tiny fraction.

In practice, then, governments can’t choose what conclusions scientists come to. If a government grant agency funds a study, that doesn’t tell you very much about whether the conclusion of the study is biased.

Instead, governments have an enormous influence on the general type of research that gets done. This doesn’t work on the level of conclusions, but on the level of topics, as that’s about the most granular that grant committees can get. Grants work in a direct way, giving scientists more equipment and time to do work of a general type that the grant committees are interested in. It works in terms of incentives, not because researchers get paid more but because they get to do more, hiring more students and temporary researchers if they can brand their work in terms of the more favored type of research. And it works by influencing the future: by creating students and sustaining young researchers who don’t yet have temporary positions, and by encouraging universities to hire people more likely to get grants for their few permanent positions.

So if you’re suspicious the government is biasing science, try to zoom out a bit. Think about the tools they have at their disposal, about how they distribute funding and check up on how it’s used. The way things are set up currently, most governments don’t have detailed control over what gets done. They have to filter that control through grant committees of opinionated scientists, who have to evaluate proposals well outside of their expertise. Any control you suspect they’re using has to survive that.