Tag Archives: academia

When and How Scientists Reach Out

You’ve probably heard of the myth of the solitary scientist. While Newton might have figured out calculus isolated on his farm, most scientists work better when they communicate. If we reach out to other scientists, we can make progress a lot faster.

Even if you understand that, you might not know what that reaching out actually looks like. I’ve seen far too many crackpots who approach scientific communication like a spammer: sending out emails to everyone in a department, commenting in every vaguely related comment section they can find. While commercial spammers hope for a few gullible people among the thousands they contact, that kind of thing doesn’t benefit crackpots. As far as I can tell, they communicate that way because they genuinely don’t know any better.

So in this post, I want to give a road map for how we scientists reach out to other scientists. Keep these steps in mind, and if you ever need to reach out to a scientist you’ll know what to do.

First, decide what you want to know. This may sound obvious, but sometimes people skip this step. We aren’t communicating just to communicate, but because we want to learn something from the other person. Maybe it’s a new method or idea, maybe we just want confirmation we’re on the right track. We don’t reach out just to “show our theory”, but because we hope to learn something from the response.

Then, figure out who might know it. To do this, we first need to decide how specialized our question is. We often have questions about specific papers: a statement we don’t understand, a formula that seems wrong, or a method that isn’t working. For those, we contact an author from that paper. Other times, the question hasn’t been addressed in a paper, but does fall under a specific well-defined topic: a particular type of calculation, for example. For those we seek out a specialist on that specific topic. Finally, sometimes the question is more general, something anyone in our field might in principle know but we happen not to. For that kind of question, we look for someone we trust, someone we have a prior friendship with and feel comfortable asking “dumb questions”. These days, we can supplement that with platforms like PhysicsOverflow that let us post technical questions and invite anyone to respond.

Note that, for all of these, there’s some work to do first. We need to read the relevant papers, bone up on a topic, even check Wikipedia sometimes. We need to put in enough work to at least try to answer our question, so that we know exactly what we need the other person for.

Finally, contact them appropriately. Papers will usually give contact information for one, or all, of the authors. University websites will give university emails. We’d reach out with something like that first, and switch to personal email (or something even more casual, like Skype or social media) only for people we already have a track record of communicating with in that way.

By posing and directing our questions well, scientists can reach out and get help when we struggle. Science is a team effort, we’re stronger when we work together.

To Elliptics and Beyond!

I’ve been busy running a conference this week, Elliptics and Beyond.

After Amplitudes was held online this year, a few of us at the Niels Bohr Institute were inspired. We thought this would be the perfect time to hold a small online conference, focused on the Calabi-Yaus that have been popping up lately in Feynman diagrams. Then we heard from the organizers of Elliptics 2020. They had been planning to hold a conference in Mainz about elliptic integrals in Feynman diagrams, but had to postpone it due to the pandemic. We decided to team up and hold a joint conference on both topics: the elliptic integrals that are just starting to be understood, and the mysterious integrals that lie beyond. Hence, Elliptics and Beyond.

I almost suggested Buzz Lightyear for the logo but I chickened out

The conference has been fun thus far. There’s been a mix of review material bringing people up to speed on elliptic integrals and exciting new developments. Some are taking methods that have been successful in other areas and generalizing them to elliptic integrals, others have been honing techniques for elliptics to make them “production-ready”. A few are looking ahead even further, to higher-genus amplitudes in string theory and Calabi-Yaus in Feynman diagrams.

We organized the conference along similar lines to Zoomplitudes, but with a few experiments of our own. Like Zoomplitudes, we made a Slack space for the conference, so people could chat physics outside the talks. Ours was less active, though. I suspect that kind of space needs a critical mass of people, and with a smaller conference we may just not have gotten there. Having fewer people did allow us a more relaxed schedule, which in turn meant we could mostly keep things on-time. We had discussion sessions in the morning (European time), with talks in the afternoon, so almost everyone could make the talks at least. We also had a “conference dinner”, which went much better than I would have expected. We put people randomly into Zoom Breakout Rooms of five or six, to emulate the tables of an in-person conference, and folks chatted while eating their (self-brought of course) dinner. People seemed to really enjoy the chance to just chat casually with the other folks at the conference. If you’re organizing an online conference soon, I’d recommend trying it!

Holding a conference online means that a lot of people can attend who otherwise couldn’t. We had over a hundred people register, and while not all of them showed up there were typically fifty or sixty people on the Zoom session. Some of these were specialists in elliptics or Calabi-Yaus who wouldn’t ordinarily make it to a conference like this. Others were people from the rest of the amplitudes field who joined for parts of the conference that caught their eye. But surprisingly many weren’t even amplitudeologists, but students and young researchers in a variety of topics from all over the world. Some seemed curious and eager to learn, others I suspect just needed to say they had been to a conference. Both are responding to a situation where suddenly conference after conference is available online, free to join. It will be interesting to see if, and how, the world adapts.

Grants at the Other End

I’m a baby academic. Two years ago I got my first real grant, a Marie Curie Individual Fellowship from the European Union. Applying for it was a complicated process, full of Word templates and mismatched expectations. Two years later the grant is over, and I get another new experience: grant reporting.

Writing a report after a grant is sort of like applying for a grant. Instead of summarizing and justifying what you intend to do, you summarize and justify what you actually did. There are also Word templates. Grant reports are probably easier than grant applications: you don’t have to “hook” your audience or show off. But they are harder in one aspect: they highlight the different ways different fields handle uncertainty.

If you do experiments, having a clear plan makes sense. You buy special equipment and hire postdocs and even technicians to do specific jobs. Your experiments may or may not find what you hope for, but at least you can try to do them on schedule, and describe the setbacks when you can’t.

As a theorist, you’re more nimble. Your equipment are computers, your postdocs have their own research. Overall, it’s easy to pick up new projects as new ideas come in. As a result, your plans change more. New papers might inspire you to try new things. They might also discourage you, if you learn the idea you had won’t actually work. The field can move fast, and you want to keep up with it.

Writing my first grant report will be interesting. I’ll need to thread the gap between expectations and reality, to look back on my progress and talk about why. And of course, I have to do it in Microsoft Word.

The Pointy-Haired University

We all know what it looks like when office work sucks. Maybe you think of Dilbert, or The Office, or the dozens of other comics and shows with the same theme. You picture characters like Dilbert’s Pointy-Haired Boss, stupid and controlling, terrible people with far too much power.

Pictured: what you picture

What does it look like when grad school sucks?

There aren’t a lot of comics, or shows, about grad school. The main one I can think of is PHD Comics.

There are a few characters like the Pointy-Haired Boss in PHD Comics, who are just genuinely bad people, in particular the main character’s advisor Professor Smith. But for the most part, the dysfunction the comic depicts is subtler. Characters aren’t selfish so much as oblivious, they aren’t demanding out of malice but out of misplaced expectations, they’re ineffective not due to incompetence but to understandable human weaknesses.

The comic gets this mostly right. If you’re struggling in grad school, you might have a Pointy-Haired Advisor. But more likely, you’re surrounded by well-meaning, reasonable, intelligent people, who nevertheless are somehow making your life a living hell.

In that situation, it can be tempting to blame yourself. You instinctively look for someone at fault, some terrible person who’s causing the problem, and nobody knows your own faults better than you do.

But before you blame yourself, consider another possibility. Consider that there aren’t just Pointy-Haired Bosses, but Pointy-Haired Institutions. Start with the wrong rules, the wrong incentives, the wrong access to information and accountability, and those well-meaning, intelligent people will end up doing some pretty stupid things. Before deciding you aren’t good enough, ask yourself: is this the only way things could have gone? Instead of a Pointy-Haired Advisor, or a Pointy-Haired Self, maybe you’re just attending a Pointy-Haired University.

Kicking Students Out of Their Homes During a Pandemic: A Bad Idea

I avoid talking politics on this blog. There are a few issues, though, where I feel not just able, but duty-bound, to speak out. Those are issues affecting graduate students.

This week, US Immigration and Customs Enforcement (ICE) announced that, if a university switched to online courses as a response to COVID-19, international students would have to return to their home countries or transfer to a school that still teaches in-person.

This is already pretty unreasonable for many undergrads. But think about PhD students.

Suppose you’re a foreign PhD student at a US university. Maybe your school is already planning to have classes online this fall, like Harvard is. Maybe your school is planning to have classes in person, but will change its mind a few weeks in, when so many students and professors are infected that it’s clearly unreasonable to continue. Maybe your school never changes its mind, but your state does, and the school has to lock down anyway.

As a PhD student, you likely don’t live in the dorms. More likely you live in a shared house, or an apartment. You’re an independent adult. Your parents aren’t paying for you to go to school. Your school is itself a full-time job, one that pays (as little as the university thinks it can get away with).

What happens when your school goes online? If you need to leave the country?

You’d have to find some way out of your lease, or keep paying for it. You’d have to find a flight on short notice. You’d have to pack up all your belongings, ship or sell anything you can’t store, or find friends to hold on to it.

You’d have to find somewhere to stay in your “home country”. Some could move in with their parents temporarily, many can’t. Some of those who could in other circumstances, shouldn’t if they’re fleeing from an outbreak: their parents are likely older, and vulnerable to the virus. So you have to find a hotel, eventually perhaps a new apartment, far from what was until recently your home.

Reminder: you’re doing all of this on a shoestring budget, because the university pays you peanuts.

Can you transfer instead? In a word, no.

PhD students are specialists. They’re learning very specific things from very specific people. Academics aren’t the sort of omnidisciplinary scientists you see in movies. Bruce Banner or Tony Stark could pick up a new line of research on a whim, real people can’t. This is why, while international students may be good at the undergraduate level, they’re absolutely necessary for PhDs. When only three people in the world study the thing you want to study, you don’t have the luxury of staying in your birth country. And you can’t just transfer schools when yours goes online.

It feels like the people who made this decision didn’t think about any of this. That they don’t think grad students matter, or forgot they exist altogether. It seems frustratingly common for policy that affects grad students to be made by people who know nothing about grad students, and that baffles me. PhDs are a vital part of the academic career, without them universities in their current form wouldn’t even exist. Ignoring them is like if hospital policy ignored residencies.

I hope that this policy gets reversed, or halted, or schools find some way around it. At the moment, anyone starting school in the US this fall is in a very tricky position. And anyone already there is in a worse one.

As usual, I’m going to ask that the comments don’t get too directly political. As a partial measure to tone things down, I’d like to ask you to please avoid mentioning any specific politicians, political parties, or political ideologies. Feel free to talk instead about your own experiences: how this policy is likely to affect you, or your loved ones. Please also feel free to talk more technically on the policy/legal side. I’d like to know what universities can do to work around this, and whether there are plausible paths to change or halt the policy. Please be civil, and be kind to your fellow commenters.

The Citation Motivation Situation

Citations are the bread and butter of academia, or maybe its prison cigarettes. They link us together, somewhere between a map to show us the way and an informal currency. They’re part of how the world grades us, a measure more objective than letters from our peers but that’s not saying much. It’s clear why we we want to be cited, but why do we cite others?

For more reasons than you’d expect.

First, we cite to respect priority. Since the dawn of science, we’ve kept track not only of what we know, but of who figured it out first. If we use an idea in our paper, we cite its origin: the paper that discovered or invented it. We don’t do this for the oldest and most foundational ideas: nobody cites Einstein for relativity. But if the idea is at all unusual, we make sure to give credit where credit is due.

Second, we cite to substantiate our claims. Academic papers don’t stand on their own: they depend on older proofs and prior discoveries. If we make a claim that was demonstrated in older work, we don’t need to prove it again. By citing the older work, we let the reader know where to look. If they doubt our claim, they can look at the older paper and see what went wrong.

Those two are the most obvious uses of citations, but there are more. Another important use is to provide context. Academic work doesn’t stand alone: we choose what we work on in part based on how it relates to other work. As such, it’s important to cite that other work, to help readers understand our motivation. When we’re advancing the state of the art, we need to tell the reader what that state of the art is. When we’re answering a question or solving a problem, we can cite the paper that asked the question or posed the problem. When we’re introducing a new method or idea, we need to clearly say what’s new about it: how it improves on older, similar ideas.

Scientists are social creatures. While we often have a scientific purpose in mind, citations also follow social conventions. These vary from place to place, field to field, and sub-field to sub-field. Mention someone’s research program, and you might be expected to cite every paper in that program. Cite one of a pair of rivals, and you should probably cite the other one too. Some of these conventions are formalized in the form of “citeware“, software licenses that require citations, rather than payments, to use. Others come from unspoken cultural rules. Citations are a way to support each other, something that can slightly improve another’s job prospects at no real cost to your own. It’s not surprising that they ended up part of our culture, well beyond their pure academic use.

In Defense of Shitty Code

Scientific programming was in the news lately, when doubts were raised about a coronavirus simulation by researchers at Imperial College London. While the doubts appear to have been put to rest, doing so involved digging through some seriously messy code. The whole situation seems to have gotten a lot of people worried. If these people are that bad at coding, why should we trust their science?

I don’t know much about coronavirus simulations, my knowledge there begins and ends with a talk I saw last month. But I know a thing or two about bad scientific code, because I write it. My code is atrocious. And I’ve seen published code that’s worse.

Why do scientists write bad code?

In part, it’s a matter of training. Some scientists have formal coding training, but most don’t. I took two CS courses in college and that was it. Despite that lack of training, we’re expected and encouraged to code. Before I took those courses, I spent a summer working in a particle physics lab, where I was expected to pick up the C++-based interface pretty much on the fly. I don’t think there’s another community out there that has as much reason to code as scientists do, and as little training for it.

Would it be useful for scientists to have more of the tools of a trained coder? Sometimes, yeah. Version control is a big one, I’ve collaborated on papers that used Git and papers that didn’t, and there’s a big difference. There are coding habits that would speed up our work and lead to fewer dead ends, and they’re worth picking up when we have the time.

But there’s a reason we don’t prioritize “proper coding”. It’s because the things we’re trying to do, from a coding perspective, are really easy.

What, code-wise, is a coronavirus simulation? A vector of “people”, really just simple labels, all randomly infecting each other and recovering, with a few parameters describing how likely they are to do so and how long it takes. What do I do, code-wise? Mostly, giant piles of linear algebra.

These are not some sort of cutting-edge programming tasks. These are things people have been able to do since the dawn of computers. These are things that, when you screw them up, become quite obvious quite quickly.

Compared to that, the everyday tasks of software developers, like making a reliable interface for users, or efficient graphics, are much more difficult. They’re tasks that really require good coding practices, that just can’t function without them.

For us, the important part is not the coding itself, but what we’re doing with it. Whatever bugs are in a coronavirus simulation, they will have much less impact than, for example, the way in which the simulation includes superspreaders. Bugs in my code give me obviously wrong answers, bad scientific assumptions are much harder for me to root out.

There’s an exception that proves the rule here, and it’s that, when the coding task is actually difficult, scientists step up and write better code. Scientists who want to run efficiently on supercomputers, who are afraid of numerical error or need to simulate on many scales at once, these people learn how to code properly. The code behind the LHC still might be jury-rigged by industry standards, but it’s light-years better than typical scientific code.

I get the furor around the Imperial group’s code. I get that, when a government makes a critical decision, you hope that their every input is as professional as possible. But without getting too political for this blog, let me just say that whatever your politics are, if any of it is based on science, it comes from code like this. Psychology studies, economic modeling, polling…they’re using code, and it’s jury-rigged to hell. Scientists just have more important things to worry about.

Zoomplitudes 2020

This week, I’m at Zoomplitudes!

My field’s big yearly conference, Amplitudes, was supposed to happen in Michigan this year, but with the coronavirus pandemic it was quickly clear that would be impossible. Luckily, Anastasia Volovich stepped in to Zoomganize the conference from Brown.

Obligatory photo of the conference venue

The conference is still going, so I’ll say more about the scientific content later. (Except to say there have been a lot of interesting talks!) Here, I’ll just write a bit about the novel experience of going to a conference on Zoom.

Time zones are always tricky in an online conference like this. Our field is spread widely around the world, but not evenly: there are a few areas with quite a lot of amplitudes research. As a result, Zoomganizing from the US east coast seems like it was genuinely the best compromise. It means the talks start a bit early for the west coast US (6am their time), but still end not too late for the Europeans (10:30pm CET). The timing is awkward for our colleagues in China and Taiwan, but they can still join in the morning session (their evening). Overall, I don’t think it was possible to do better there.

Usually, Amplitudes is accompanied by a one-week school for Master’s and PhD students. That wasn’t feasible this year, but to fill the gap Nima Arkani-Hamed gave a livestreamed lecture the Friday before, which apparently clocked in at thirteen hours!

One aspect of the conference that really impressed me was the Slack space. The organizers wanted to replicate the “halls” part of the conference, with small groups chatting around blackboards between the talks. They set up a space on the platform Slack, and let attendees send private messages and make their own channels for specific topics. Soon the space was filled with lively discussion, including a #coffeebreak channel with pictures of everyone’s morning coffee. I think the organizers did a really good job of achieving the kind of “serendipity” I talked about in this post, where accidental meetings spark new ideas. More than that, this is the kind of thing I’d appreciate even in face-to-face conferences. The ability to message anyone at the conference from a shared platform, to have discussions that anyone can stumble on and read later, to post papers and links, all of this seems genuinely quite useful. As one of the organizers for Amplitudes 2021, I may soon get a chance to try this out.

Zoom itself worked reasonably well. A few people had trouble connecting or sharing screens, but overall things worked reliably, and the Zoom chat window is arguably better than people whispering to each other in the back of an in-person conference. One feature of the platform that confused people a bit is that co-hosts can’t raise their hands to ask questions: since speakers had to be made co-hosts to share their screens they had a harder time asking questions during other speakers’ talks.

A part I was more frustrated by was the scheduling. Fitting everyone who wanted to speak between 6am west coast and 10:30pm Europe must have been challenging, and the result was a tightly plotted conference, with three breaks each no more than 45 minutes. That’s already a bit tight, but it ended up much tighter because most talks went long. The conference’s 30 minute slots regularly took 40 minutes, between speakers running over and questions going late. As a result, the conference’s “lunch break” (roughly dinner break for the Europeans) was often only 15 minutes. I appreciate the desire for lively discussion, especially since the conference is recorded and the question sessions can be a resource for others. But I worry that, as a pitfall of remote conferences, the inconveniences people suffer to attend can become largely invisible. Yes, we can always skip a talk, and watch the recording later. Yes, we can prepare food beforehand. Still, I don’t think a 15 minute lunch break was what the organizers had in mind, and if our community does more remote conferences we should brainstorm ways to avoid this problem next time.

I’m curious how other fields are doing remote conferences right now. Even after the pandemic, I suspect some fields will experiment with this kind of thing. It’s worth sharing and paying attention to what works and what doesn’t.

The Point of a Model

I’ve been reading more lately, partially for the obvious reasons. Mostly, I’ve been catching up on books everyone else already read.

One such book is Daniel Kahneman’s “Thinking, Fast and Slow”. With all the talk lately about cognitive biases, Kahneman’s account of his research on decision-making was quite familiar ground. The book turned out to more interesting as window into the culture of psychology research. While I had a working picture from psychologist friends in grad school, “Thinking, Fast and Slow” covered the other side, the perspective of a successful professor promoting his field.

Most of this wasn’t too surprising, but one passage struck me:

Several economists and psychologists have proposed models of decision making that are based on the emotions of regret and disappointment. It is fair to say that these models have had less influence than prospect theory, and the reason is instructive. The emotions of regret and disappointment are real, and decision makers surely anticipate these emotions when making their choices. The problem is that regret theories make few striking predictions that would distinguish them from prospect theory, which has the advantage of being simpler. The complexity of prospect theory was more acceptable in the competition with expected utility theory because it did predict observations that expected utility theory could not explain.

Richer and more realistic assumptions do not suffice to make a theory successful. Scientists use theories as a bag of working tools, and they will not take on the burden of a heavier bag unless the new tools are very useful. Prospect theory was accepted by many scholars not because it is “true” but because the concepts that it added to utility theory, notably the reference point and loss aversion, were worth the trouble; they yielded new predictions that turned out to be true. We were lucky.

Thinking Fast and Slow, page 288

Kahneman is contrasting three theories of decision making here: the old proposal that people try to maximize their expected utility (roughly, the benefit they get in future), his more complicated “prospect theory” that takes into account not only what benefits people get but their attachment to what they already have, and other more complicated models based on regret. His theory ended up more popular, both than the older theory and than the newer regret-based models.

Why did his theory win out? Apparently, not because it was the true one: as he says, people almost certainly do feel regret, and make decisions based on it. No, his theory won because it was more useful. It made new, surprising predictions, while being simpler and easier to use than the regret-based models.

This, a theory defeating another without being “more true”, might bug you. By itself, it doesn’t bug me. That’s because, as a physicist, I’m used to the idea that models should not just be true, but useful. If we want to test our theories against reality, we have a large number of “levels” of description to choose from. We can “zoom in” to quarks and gluons, or “zoom out” to look at atoms, or molecules, or polymers. We have to decide how much detail to include, and we have real pragmatic reasons for doing so: some details are just too small to measure!

It’s not clear Kahneman’s community was doing this, though. That is, it doesn’t seem like he’s saying that regret and disappointment are just “too small to be measured”. Instead, he’s saying that they don’t seem to predict much differently from prospect theory, and prospect theory is simpler to use.

Ok, we do that in physics too. We like working with simpler theories, when we have a good excuse. We’re just careful about it. When we can, we derive our simpler theories from more complicated ones, carving out complexity and estimating how much of a difference it would have made. Do this carefully, and we can treat black holes as if they were subatomic particles. When we can’t, we have what we call “phenomenological” models, models built up from observation and not from an underlying theory. We never take such models as the last word, though: a phenomenological model is always viewed as temporary, something to bridge a gap while we try to derive it from more basic physics.

Kahneman doesn’t seem to view prospect theory as temporary. It doesn’t sound like anyone is trying to derive it from regret theory, or to make regret theory easier to use, or to prove it always agrees with regret theory. Maybe they are, and Kahneman simply doesn’t think much of their efforts. Either way, it doesn’t sound like a major goal of the field.

That’s the part that bothered me. In physics, we can’t always hope to derive things from a more fundamental theory, some theories are as fundamental as we know. Psychology isn’t like that: any behavior people display has to be caused by what’s going on in their heads. What Kahneman seems to be saying here is that regret theory may well be closer to what’s going on in people’s heads, but he doesn’t care: it isn’t as useful.

And at that point, I have to ask: useful for what?

As a psychologist, isn’t your goal ultimately to answer that question? To find out “what’s going on in people’s heads”? Isn’t every model you build, every theory you propose, dedicated to that question?

And if not, what exactly is it “useful” for?

For technology? It’s true, “Thinking Fast and Slow” describes several groups Kahneman advised, most memorably the IDF. Is the advantage of prospect theory, then, its “usefulness”, that it leads to better advice for the IDF?

I don’t think that’s what Kahneman means, though. When he says “useful”, he doesn’t mean “useful for advice”. He means it’s good for giving researchers ideas, good for getting people talking. He means “useful for designing experiments”. He means “useful for writing papers”.

And this is when things start to sound worryingly familiar. Because if I’m accusing Kahneman’s community of giving up on finding the fundamental truth, just doing whatever they can to write more papers…well, that’s not an uncommon accusation in physics as well. If the people who spend their lives describing cognitive biases are really getting distracted like that, what chance does, say, string theory have?

I don’t know how seriously to take any of this. But it’s lurking there, in the back of my mind, that nasty, vicious, essential question: what are all of our models for?

Bonus quote, for the commenters to have fun with:

I have yet to meet a successful scientist who lacks the ability to exaggerate the importance of what he or she is doing, and I believe that someone who lacks a delusional sense of significance will wilt in the face of repeated experiences of multiple small failures and rare successes, the fate of most researchers.

Thinking Fast and Slow, page 264

The Academic Workflow (Or Lack Thereof)

I was chatting with someone in biotech recently, who was frustrated with the current state of coronavirus research. The problem, in her view, was that researchers were approaching the problem in too “academic” a way. Instead of coordinating, trying to narrow down to a few approaches and make sure they get the testing they need, researchers were each focusing on their own approach, answering the questions they thought were interesting or important without fitting their work into a broader plan. She thought that a more top-down, corporate approach would do much better.

I don’t know anything about the current state of coronavirus research, what works and what doesn’t. But the conversation got me thinking about my own field.

Theoretical physics is about as far from “top-down” as you can get. As a graduate student, your “boss” is your advisor, but that “bossiness” can vary from telling you to do specific calculations to just meeting you every so often to discuss ideas. As a postdoc, even that structure evaporates: while you usually have an official “supervisor”, they won’t tell you what to do outside of the most regimented projects. Instead, they suggest, proposing ideas they’d like to collaborate on. As a professor, you don’t have this kind of “supervisor”: while there are people in charge of the department, they won’t tell you what to research. At most, you have informal hierarchies: senior professors influencing junior professors, or the hot-shots influencing the rest.

Even when we get a collaboration going, we don’t tend to have assigned roles. People do what they can, when they can, and if you’re an expert on one part of the work you’ll probably end up doing that part, but that won’t be “the plan” because there almost never is a plan. There’s very rarely a “person in charge”: if there’s a disagreement it gets solved by one person convincing another that they’re right.

This kind of loose structure is freeing, but it can also be frustrating. Even the question of who is on a collaboration can be up in the air, with a sometimes tacit assumption that if you were there for certain conversations you’re there for the paper. It’s possible to push for more structure, but push too hard and people will start ignoring you anyway.

Would we benefit from more structure? That depends on the project. Sometimes, when we have clear goals, a more “corporate” approach can work. Other times, when we’re exploring something genuinely new, any plan is going to fail: we simply don’t know what we’re going to run into, what will matter and what won’t. Maybe there are corporate strategies for that kind of research, ways to manage that workflow. I don’t know them.