Almost all biologists are lone founders. This is probably suboptimalthere is not enough room for people who aspire to be just researchers but not managersreviewing is a disasterno one will say whether big labs are good or badsenior scientists are bound by the incentives of their studentsuniversities seem to seek to maximize their profits, and positive research is a side effecta large component of our scientific literature is wrongmoney is so hard to come by, even for famous scientists conclusion acknowledgements citations appendix what i say and what i don't say in this essay corrections risk aversion is perhaps exaggerated.Risk aversion possibly exaggerated studying buyers and animals is very difficult more funding ideas large-scale interventions articles in the medicine section letter from lab technician/manager about this experience in response to this essay mutual review administrators push for maximum legibility miscellanea specific examples of important, recent medical advances
See discussions at /r/slatestarcodex (a) (25 comments), hacker news (a) (17 comments), twitter (1 (a), 2 (a)) (>300 retweets), marginal revolution (a) (39 comments)
Summary: academic science has a lot of troubles, and it could do much better. But the problems cited are not as catastrophic as an outsider might imagine. My intuition (contrarian, i guess) is that technology in biology is not slowing down. Specific parts of academia that seem problematic: harsh, punishing deviations, climbing the ladder; peer review; the need for constant fundraising for professors. Parts that seem less of a challenge than i initially thought: short-termism; lack of funding for young scientists.
2021 one year after completing this essay i have become considerably more pessimistic about then academic science and am now working on the new science.
Introduction
In his book asymmetric weapons gone bad, a) scott alexander emphasizes that for some fields of study, their study little by little gets them off the road and only their study in huge batches allows us to understand what is really going on:
Maybe, with a free supply of resources, our research would naturally converge toward the truth. Given infinite intelligence, wisdom, impartiality, education, knowledge of the subject, evidence to review, experiments to conduct, and time to deliberate, we would figure things out.
But if infinite resources would lead to truth, none of this means that truth as a function of resources has to be monotonic. ....
Some difficult questions can be epistemic traps - difficulties in which the steeper you study them, the more wrong you get, all the way to some inflection point that happens to be further away than anyone has studied them before.
I think this observation is a general one - true for almost all fields of study and more, lends itself to a much simpler explanation: almost everyone has an individual agenda. So when you start studying something by reading a book or two or talking to a couple of people, you will inevitably get overwhelmed by their agendas, personal biases, personal ignorance, and all the other nuances that don't lead to a proper model of the subject.
Then, by reading a number of more books or talking to many people, you will see some of the biases that have influenced the construction of your model. However, and all of this will not be enough. When looking for books or interlocutors, the sample will either be very narrow (ie.I.E. The people where you are going to socialize will be extremely similar to each other), or overly broad (i.E. Their experiences will be so contradictory that you will find it so difficult to compare them and place them into a complete picture).
When i talked to the top five people about how life science research works, i got the perfect picture in my head, completely consistent with all the rumors about sclerosis, abandonment, and the short-termism of academic science that i've heard for many a number of years.
After also talking to ten people when necessary, i realized that the area was not very but, was not in a position to put it all together.
I ended up interviewing more than 60 percent % of the people provoked by developments in the life sciences - only graduate students, postdocs and pi (principal investigators, lab managers), and in addition, for example, representatives of philanthropic organizations and venture capital funds investing in the modern sciences.
You can view this essay as the culmination of a year of my endeavor to view applied progress (a). It's shown to me that i've finally been able to come to something for something, although that something for myself seems to be somewhat different from what virtually any citizen where i've communicated imagine: as a model of what's going on in the field.
[Life] science is not slowing
It's tempting to look at the negative side of academic science. Bureaucratization, seeming risk neutralization, loss of freedom... And to conclude that therefore science is not working well. Is a misconception. Yes, funding agencies don't like to take risks; yes, academia now selects those who are likely to be inappropriately selected, such as conformity and high integrity; yes, the average scientist functions in academia not from a passion for science and perhaps the productivity of the average scientist is declining. I would say that comparing the average scientist now - and for 1973 - is identical to comparing the average high school graduate at a given time and during 1973. (A) )
However, none of this is to say that science is stagnating or even slowing down. The pace of discovery in all the areas of biology i've reviewed is astounding. So that it's not necessarily the pace, however, that many researchers are actually working on projects of the highest expected value without significant constraints, bringing in the smartest people they can find and throwing money at them in biology it's so set up that pi raise money and manage/mentor, spin my win casino and grad students and postdocs do the ordering).
If you can at harvard, mit, stanford or so on universities - they are all filled with wonderful researchers working on solid budgets, genuinely passionate about discovery and invention. Hhmi (a) actually gives viewers unrestricted grants; czi (a) funds people specifically to work on po (which is usually hard to choose and for employment purposes on their riskiest, usually unfunded thoughts and often other foundations try to correct inefficiencies they see in resource allocation.
I brain that the view of stagnation in science and for biology in particular is essentially fake news driven by the technological hedonistic treadmill and nostalgia. We are quick to adapt to technological progress - no matter how great it is - and always idealize the past, no matter how awful it was.
That is, we're only willing to go to wikipedia under 2018 in science (and see how far we've come in the past year:
- The first bionic hand with a sense of touch that can be worn outside the lab- the development of a new 3d-bioprinting technology that allows for more accurate printing of organs from soft tissue, such as lungs- a method - by which a citizen's innate immune system happens to be trained to respond more effectively to disease and infection- a new form of biomaterial-based therapeutic drug delivery system that releases its goods only under certain physiological conditions, respectively potentially a new test based on detecting cancer-related dna and proteins in the blood yielded 70% of the coveted results in terms of tumor types tested in 1,005 patients- a method of turning skin cells into stem cells using crispr- two monkey clones created for the first time- a paper presents possible evidence from food that naked moles are not at increased risk of death due to aging
Nothing seems special? But here's the catch: it's not 2018. It's january 2018
If you actually look at the major discoveries made in any year in the first half of the 20th century and check them against 2018, you'll likely see that the pace of scientific progress is only accelerating. The potential problem here is that if the amount of resources spent on science has increased by a factor of 100, and the rate of progress has stayed the same or only increased slightly, then the maximum success from doing science has decreased dramatically. Entirely, after a year of studying how special research actually works and progresses, i'm considerably more optimistic.
What else works because you naively assume (for both better and worse): 3 examples
1. "Nih's risk aversion makes it difficult to fund innovative science."
Observation: nih will not tolerate risk so much that even if it explicitly asks for radical proposals, open philanthropy evaluates nih director's transformative research award proposals (a): "we believe that some of the proposals we reviewed were similar to announcements in more typical rfp s in terms of representations of their freshness and potential impact. Better said, we felt that most of the proposals posted were somewhat ordinary. This surprised us given the "transformational" focus of the tra program. We speculate that this may be due to the constraints that applicants believe they have to work within in order to overcome through the examination." No one believes that the program is really possible to fund something radical, so citizens do not try to apply for research with perfect risk
A implicit conclusion: it is unrealistic to get funding for high-risk projects at nih, so scientists end up working on incremental science
Reality: nih does not force scientists to adhere to the plans presented, so as a result, scientists submit a grant order with a project for which they have supporting preliminary data (so that nih n with the funds available so far, scientists are able to get preliminary information on certain risky ideas, which will make the insects less risky and even more available for nih funding over time. Sometimes this takes a more extreme form, where researchers actually promise the nih good results from experiments they have already done but have not yet published.
So, in practice, the distortions are still present, as one has to come up with rather arcane ways to get f "the nih's bias makes it very difficult to fund methodological research"
Reality: the nih does not worship funding purely methodological research (e.G. Developing a better po)
The implicit conclusion: getting funding for methods design is impossible at the nih
Reality: technology development for the nih can be embellished, e.G. By providing a specific biological for example. E.G. By providing a specific biological target for which insufficient options are a bottleneck, and prove to the nih that you get to make specific progress on something that is important to them using your best methods
But of course this creates obvious friction and inefficiency and skews the work of scientists towards food, which is easy to dress up for the nih, but not what they find most important. But the amount of friction and inefficiency is several times less than we naively expect.
3. "Nih severely underfunds young researchers"
Note: "judging from the curriculum vitae, this r01 application is the first nih grant to dr. Green." (A)
Findings:
The average age of the first recipients of r01 grants, the most frequent and popular form of nih funding, is 42, while the average age of all recipients is 52. More people older than 65 receive research grants than anyone under 35. (Nyt (a))
An implicit conclusion is that young scientists lack funds to prolong research. The nih should allocate more funds to researchers under thirty-five percent and 40 years old.
Reality: many very capable people work in well-funded labs whose managers are quite free and allow their graduate students and postdocs to pursue what they aspire to and not think about cost. Statistically, this looks like "larger sums than previous versions go to old/known pi" and young researchers are not allowed to start independent careers." In spinmywin practice, this means that up to thirty-five percent of the years (i.E. When your creativity is at its highest) you are insulated from management and fundraising (and the pesky administrative duties assigned to every trainee professor) and can always focus on research and articles, while receiving mentoring from senior pi and benefiting from all the infrastructure available in established laboratories (administrative assistants, lab assistants, etc.).
When you encounter a lab x [where x is a famous scientist] discovered y", it was probably z - x's graduate student or postdoc - who came up with the idea, performed the necessary steps (with the help of other young people, and wrote the paper. X's contribution was probably in supplying the research environment in which all of this was possible in obtaining finances and in the following he communicated with z every week about the progress of the idea. I'm not saying that x's contribution isn't important - it usually is - but discussions focused on pi miss the point that the investment is hardly ever spent directly by him, which constantly distorts our view of how funds are allocated.
At the end of the day, yes, distortion does occur. At one time it's really hard to become independent and put together your own personal research program, but the ultimate negative effect on the science done by young researchers is many times lower than we naively expect.
Two points
- When i write "naive conclusion" it tends to suggest that it took me months of research to figure out why because in the meantime it didn't- while nih provides a significant amount of research funding in the current american sciences region, there are a huge number of foundations who are aware of its bias and are actively trying to fund gamers and research that nih misses, support grad students, postdocs and independent researchers taking up careers, etc.Etc.
Look beyond that - how policies meant to improve academic science are actually screwing it up (a)
If you are smart and driven, you will find a job for yourself
Biology has tons of labs with excellent funding and open and risk-loving pi. This suggests that when you are so smart, ambitious, and driven, you can probably see a lab and work on interesting things, even with a questionable education, although you may need a year or so as a research assistant to prove your worth. Pi is usually eager to accept research assistants and very willing to get thoughtful personalized cold letters (this also applies to scientists you think are famous).
I'm pretty sure that if someone capable of doing nobel-level research decides to get a phd in biology or neuroscience, they can fall into the best programs with incredible advisors and funding in less than 2 years.
The problem here is that many for whom understanding what i wrote above would be helpful are misinformed. Contributors unencumbered with biology tend to assume that formalizing a phd degree means spending 6 years behind a bench doing our manager's experiments and that it's only available with a top-notch undergraduate gpa, not realizing that neither of those things are true if you're actually in a position of strength and as a result make the decision to avoid grad school created on misinformation.
Nobody cares if you are a genius
This may seem contradictory to the previous section, but take note that here i only wrote "get in" and not "stay in".
To get in, you only need to convince one pi you are worth the risk. In order to stay, you have to convince many professors, many study sections that will evaluate your grant applications, various charitable foundations, the university hiring committee, the seniority committee, and so on and so forth.
Any of our structures will be less open and minimally condescending than an ip that is adept at contriving to acquire you into business.
I am aware of [many] brilliant researchers.
- Some work in an area that is not in vogue at the moment- some are not very likable or not taught to network- some are in an undesirable demographic- sometimes people can't adumbrate, explain or properly present their research- some are too passionate about their topic (church (a), woodward (a), ramanujan (a), lippmann (a))- many are just not great at anything else (einstein (a)) - "einstein took the entrance exam for the swiss federal polytechnic university, in zurich, switzerland..... He failed to achieve the required level in the general part of the exam, but received exceptional marks in physics and mathematics."
A lot of skill is needed from a prosperous scientist in academia (a). Here's what eric weinstein means about the harms of striving forward (a):
So talent and excellence are both worthy, but they're different forms, and if you refute the fact that it's a huge issue to take a nobody in the idiom of genius and push them into a different idiom, which is to reduce their variance, it's going to be very destructive and it's not going to allow us to create industries that will allow us to change paradigms and travel forward.
Almost all biologists are single founders. This is probably suboptimal
Many of the ailments listed above would meet solved if the researcher had a champion or co-founder to complement him such he is not doing well.
In jessica livingston's (co-founder of y combinator) book "founders at work" (a) max levchin (co-founder of paypal) says:
Try to have a good co-founder. I reflect that every ambush is about people, and if you're doing it on your own, it's pretty hard. Buying is not impossible, especially if you're a loner and introvert, but even now it's too hard.... Before paypal, i ran a company individually and i also thought that this kind of thing was not scary. I could handle it. But to believe in sources of energy and support is only realistic from your own needs. There is no one you can really call and say, "hey, this thing could fall apart any minute. What the hell should we do?"
At first we were supported by the advantages that peter and i always knew that the two of us would not be lost.
Yc itself is known for the fact that it prefers teams over individuals. It must have a ceo (visionary, salesman, manager and cto (builder and designer). In light of this, it is puzzling why universities almost never hire two specialists for the overall management of the lab. There is one pi who simply has to be both the general manager and the technical director, and who, moreover, has to run the lab almost single-handedly - everyone else in the lab will be employees with little responsibility for the long-term future of the lab.
This is very surprising and reminds me of feynman and dyson. Freeman dyson:
[Feynman] worked on all the problems of quantum electrodynamics, and risked doing a lot of things that were always beautiful but that nobody else had dared to do. He loved to talk and i loved to listen. So within six months i had mastered his language quite well. And also after one year i was able to translate his dreams into math, so they became more accessible to different countries. In consequence i became famous, but it all happened in about six months.
How many scientists never reached their potential because they