Scientist’s Survival Guide

1.1 Post-COVID-19 seminars

Yes, research seminars are now even more global. See researchseminars.org.

1.2 General menus

• Computer vision researcher Jia-Bin Huang maintains jbhuang0604/awesome-tips, a constantly updated collection of practical academic advice covering everything from presentations to time management.

• For early career researchers, the classic Thesis Whisperer blog remains an invaluable resource for navigating the dissertation process.

• The Howard Hughes Medical Institute’s Making the right moves: A Practical Guide to Scientific Management for Postdocs and New Faculty (Burroughs Wellcome Fund and Howard Hughes Medical Institute 2006) offers comprehensive career guidance for scientists transitioning to independence.

• For a refreshing dose of academic permission-giving, check out Things You Are Allowed To Do, Academic Edition which reminds you that many “rules” are actually just suggestions.

• When you’re stuck on a specific academic dilemma, Academia Stack Exchange provides crowdsourced wisdom from researchers worldwide.

• How to teach statistics

• How to do academic online

• publishing is messed up

• Read Academic work-life-balance survey to feel like not bothering with academia.

• Former OpenAI researcher Andrej Karpathy offers brutally honest advice in his survival guide for PhD students, covering everything from choosing advisors to maintaining sanity.

• AI research: the unreasonably narrow path and how not to be miserable

• How To Be an Academic Hyper-Producer

• How to Become the Best in the World at Something

  This is how skill stacking works. It’s easier and more effective to be in the top 10% in several different skills—your “stack” — than it is to be in the top 1% in any one skill.

1.3 Specific morsels

• Go to Yale: An astonishingly small number of elite universities produce an overwhelming number of America’s professors

• Alexandre Afonso advises “How academia resembles a drug gang”.

  If you are mobile, strategic and concerned with employment conditions, you might want to exploit these differences and avoid the outsider boxes at different stages of your career. This would mean avoiding the UK for your PhD and avoiding Germany after your PhD.

• Perfectionism

• PhD workflow

• Maxwell Tabarrok, Overfitting in Academia

  But our selection criteria do not match with what we want academics to do. The out of sample prediction ability of academics is worse than random and laymen. They know lots about research they’ve already been tested on, but are rarely better than random at guessing the results of experiments in their field that they haven’t seen before. Citations and academic rank do not increase the ability of economists to predict the outcome of economic experiments or political events.

• How to Ask for a Letter of Recommendation

• Write an article in 7 days

• Bradshaw on how to write a paper

• Conference dinner schmoozes

• Conference schmoozes

• Publish an R package in the Journal of Statistical Software

• Ten simple rules for good research practice for early-career researchers

1.4 Funding

See research funding.

1.5 Communicate better

See communication and science communication.

1.6 Strategic ignorance

See strategic ignorance.

1.7 Networking hacks

Ah, networking — the part of the job that makes even the most extroverted researcher feel like a rare specimen being traded at a pet show, where for some reason the currency isn’t dollars but awkwardness. For those of us who’d rather be pipetting, the art of the schmooze can feel like a dark art. Here are some thoughts and tools on how to make connections without turning into a snake‑oil merchant, and how to do it efficiently enough that we can get back to the bit that feels more like actual work.

• ACM distinguished speakers
• Mark Suster: “How to write an effective email to people who receive tons of emails (in 12 easy steps)”
• Networking for Nerds
• How to make friends over the internet

2.1 Habits of highly effective scientists

Classic:

• Curiosity

According to SLIME MOLD TIME MOLD:

• Stupidity
• Arrogance
• Laziness
• Carefreeness
• Beauty
• Rebellion
• Humour

Hardtowrite:

In science, the process is just as important as the discoveries. Improving our scientific processes will speed up our rate of discovery. Feyerabend claims contemporary research has over-indexed on processes such as the scientific method and this rigidity has restrained innovation. The crux of his book Against Method (Feyerabend and Hacking 2010) is that paradigm shifts in science stem from epistemological anarchism. Epistemology refers to the formulation of beliefs. This anarchy, to any Thomas Kuhn fans, is what is necessary to achieve Kuhn’s Stage 4 phase of science, the ’evolutionary phase’ in which new paradigms are created. In recent decades we have placed too much importance on science being consistent, while forgetting that paradigm shifts often come from those who refute mainstream assumptions. In other words, the geniuses who generated scientific paradigm shifts were anarchists to their contemporaries.

Jan Hendrik Kirchner, Via productiva

Be wary, be very wary indeed, of engaging with a Hydra problem. You might be able to identify a Hydra problem by keeping track of the branching factor: how many subproblems does each step of your derivation introduce? If this continues to be larger than 1, your approach is probably incorrect or at least infeasible. This might appear obvious, but history is full of famous examples of researchers getting stuck on epicycles. Researchers produced “pseudo-answers” to questions, but they opened more questions than closed. I observe the pattern in my colleagues, students, and myself.

2.2 How precious is my idea?

My understanding of which parts of research are hard and which are valuable has changed.

Let us suppose I was worried about being “scooped” — beaten to publication — which is something people in science tend to worry about a little, but not as much as I imagined before I joined in for myself. How valuable is an insight? It depends. An insight like “this might be worth trying, who knows” isn’t valueless, but the opportunity cost of trying it can be months of specialist time or years of a grad student’s time. Let us call such intuitions. Even if certainty is high, the exercise cost is also high, so it’s not worth keeping this kind of thing quiet; rather, we want to share this kind of thing as widely as possible so the work and risk can be shared among multiple researchers. Having good intuitions is useful but if anything we want to cultivate the [TODO clarify]

If my idea is “I can definitely solve this particular problem in a particular way,” the value of this intelligence is higher. Let us call these solutions. Often, simply knowing a solution exists in a particular domain can lead someone else rapidly to a solution, because it constrains the search enough to let other people find it quickly. This is because most of mathematics, at least for me, involves trying bone-headed things that turn out to be silly; merely narrowing the search space can make things drastically easier. So if we were in a competitive, idea‑stealing environment, knowledge of existing solutions tends to be valuable intelligence. It’s not the whole road to a paper that will get us credit for being clever (and I personally value a paper that has more than one great idea), but a good solution is an important chunk.

On the other hand, academia can often operate like a cooperative endeavour, or even a competitively cooperative potlatch, and giving away valuable things is a way to signal status and attract collaborators and funding.

Overcoming Bias: The InDirect-Check Sweet Spot

Tony Kulesa, in Tyler Cowen is the best curator of talent in the world gives a fannish profile of Tyler Cowen, including ideas about how we would become expert at identifying underexploited talent. Part of that process, interestingly, is not having a committee process, and being shamelessly personal-judgment-driven. Insert speculation about wisdom of crowds versus madness of committees. Also, equity concerns.

• Ideas not mattering is a psyop

2.3 Sidling up to the truth

As a researcher motivated by big-picture ideas (How can we survive on planet earth without being consumed in battles over dwindling resources and environmental crises?) as much as aesthetic ideas, I am sometimes considered damaged goods. Many claim a fine scientist is safely myopic; the job of discovery is detailed piecework.

On one hand, the various research initiatives that pay my way are tied to various real world goals (“Predict financial crises!” “Tell us the future of the climate!”). On the other hand, researchers tell me it’s useless to try to solve these large issues wholesale; instead we should identify small, retail questions where we can hope to make progress. Meanwhile, they’ve just agreed to take a lot of money to solve big problems. In this school, then, the presumed logic is that we take a large research grant to shed light on lots of small problems that lie in the penumbra of the large issue, in the hope that one will flare up to illuminate the shadow. Or burns the lot to the ground. The example given by the Oxonian scholar who most recently expounded this to me was Paul David and the path dependence of the QWERTY keyboard. It’s a deep issue — the contingency of the world — seen through the tiny window opened by substandard keyboard design.

Truth, in these formulations, is a cat: don’t look at it directly or it will perversely slope off to rub against someone else’s leg. Our acceptance is all in the sidling-up, the feigning disinterest, and waiting for truth to come up and show us its belly. I’m not sure I am persuaded by this. It’s the kind of science that would be expounded in an education film directed by Alejandro Jodorowsky.

On the other hand, I’m not sure I buy the grant-makers’ side of this story either, at least the version they seem to tell in Australia, which is that they give out money to go find something out. There are productivity outcomes on the application form where we fill out the goals that our research will fulfil; This rules out much research by restricting us largely to marginally refining a known-good idea rather than trying something new. I romantically imagine that in much research, we would not know what we were discovering in advance.

The compromise is that we meet in the middle and swap platitudes. We will “improve our understanding of X”, we will “find strategies to better manage Y”. We certainly don’t mention that we might spend a while pondering keyboard layouts when the folks ask us to work out how to manage a complex non-linear economy.

2.4 Disruption by field outsiders

Are fields plagued by hyperselection? Can we find fields ripe for disruption by outsiders with radical, out-of-the-box ideas? Do we need left-field eccentrics roaming about, asking if the emperor has clothes?

Is it just stirring the pot? How many physicists, to take one example, can get published by ignoring everyone else’s advances?

How do we know that our left-field idea is a radically simple idea that causes the entire field to advance? And how do we know that it’s not the crazed ramblings of someone who’s missed the advances of the last several decades, an inmate wandering out of the walled disciplinary asylum in a dressing gown, railing against the Vietnam War?

• The Correct Contrarian Cluster

2.5 Optimizing versus satisficing

• EA risks falling into a “meta trap”. But we can avoid it.

2.6 History and philosophy of [TODO clarify]

Check out the amusing curmudgeon DC Stove, Popper and after: Four modern irrationalists.

4.1 Fostering Innovation at Project-level

See science projects