Structural Diversity in Science

October 26, 2022

Michael Nielsen and Kanjun Qiu recently published a massive essay entitled “A Vision of Metascience: An Engine of Improvement for the Social Processes of Science.” Metascience, the central topic of the essay, is science about science. As the authors put it, metascience “overlaps and draws upon many well-established fields, including the philosophy, history, and sociology of science, as well as newer fields such as the economics of science funding, the science of science, science policy, and others.”

The essay emphasizes how there are likely massive potential improvements to what they call the “social processes” of science, or the ways in which science is practiced by scientists. There are a lot of important ideas and conclusions in the essay, but here I want to focus on one specific theme that caught my attention: the importance of structural diversity in driving scientific progress. In this context, structural diversity means “the presence of many differently structured groups in science.” High structural diversity means many different types of scientific groups, while low structural diversity means only a few different types of scientific groups. To quote Nielsen and Qiu directly:

…structural diversity is a core, precious resource for science, a resource enlarging the range of problems humanity can successfully attack. The reason is that different ambient environments enable different kinds of work. Problems easily soluble in one environment may be near insoluble in another; and vice versa. Indeed, often we don't a priori know what environment would best enable an attack on an important problem. Thus it's important to ensure many very different environments are available, and to enable scientists to liquidly move between them. In this view, structural diversity is a resource to be fostered and protected, not homogenized away for bureaucratic convenience, or the false god of efficiency. What we need is a diverse range of very different environments, expressing a wide range of powerful ideas about how to support discovery. In some sense, the range of available environments is a reflection of our collective metascientific intelligence. And monoculture is the enemy of creative work.

Today, structural diversity is low: scientific research is overwhelmingly performed in universities by professor-led research groups. These groups typically contain from 5 to 30 people, have at most two additional administrators beyond the principal investigator (i.e. the professor), and are composed of undergrads, graduate students, and postdocs. (There are, of course, many exceptions to the template I’ve outlined above.)

This structure influences the sort of scientific work that gets done. To graduate, PhD students need to have papers, which means that they need to work on projects that have sufficiently short time horizons to conclude before they graduate. Additionally, they need to have first-author papers, which means that they can’t work in large teams; if three students work together, they can’t all be first authors. Taken together, these considerations imply that most projects should take 10 person-years or less to accomplish.1

This is a long time, but not that long for science: unfortunately, most truly groundbreaking projects are “too big” for a single academic lab. Conversely, students are incentivized to publish in high-impact journals, and so projects that are “too small” are penalized for not being ambitious enough. Skilled academics are able to thread the needle between projects that are “too big” and projects that are “too small” and provide exactly the right amount of effort to generate high-impact publications within a reasonable time horizon.

These same challenges are echoed (on grand scale) for assistant professors. New faculty typically have 5 to 7 years before they must submit their tenure package, which means they’re forced to choose projects likely to work in that time frame (with inexperienced students and relatively few resources, no less). This disincentives tool-building, which generally chews up too much time to be an efficient use of resources for young labs, and puts a ceiling on their ambition.

These aren’t the only consequences of academia’s structure. “Deep” skills requiring more than a year or two of training are tricky, because even PhD students are only there for 4–6 years, so the time it takes to acquire skills comes directly out of the time they can be doing productive research. Additionally, certain skill sets (e.g. software engineering) command such a premium that it’s difficult to attract such people to academia. Specialized instrumentation is another challenge: a given lab might only have the budget for a few high-end instruments, implying that its projects must be chosen accordingly.

A defender of the status quo might reasonably respond that smaller labs do lead to smaller projects, but in a greater number of areas: “what is any ocean, but a multitude of drops?” The academic structure, with its incentives for demonstrable progress, certainly cuts back on the number of costly, high-profile failures: most failed research groups never get tenure, limiting the damage.

Nevertheless, it seems that at this point many fields have been picked clean of projects with low enough barriers to entry to make them accessible to academics. Many remaining insights, including those needed to spawn new fields of science, may be simply out of reach of a decentralized array of small, independent academic groups. As Nielsen and Qiu put it, “you can stack up as many canonical researchers as you like and they still won't do the non-canonical work; it's a bottleneck on our capacity for discovery.” To support this point, they cite examples where large organizations were able to produce big advances inaccessible to their smaller counterparts: LIGO, the Large Hadron Collider, Brian Nosek’s Center for Open Science, and the Human Genome Project.

If we accept the claim that structural diversity is important, we ought to look for opportunities to expand structural diversity wherever possible. At the margin, this might look like supporting non-traditional hires in academic groups, including people who don’t fit into the established undergrad–graduate student–postdoc pipeline, and allowing for greater flexibility in the structure of labs (i.e. multiple professors within the same lab). More radical solutions might look like scientific start-ups where profitability can realistically be achieved, or “focused research organizations” where it cannot.2 What could a well-managed team of professional scientists, focused solely on work “not easily possible in existing environments,” accomplish when pitted against some of the toughest unsolved problems in the world? We won’t know until we try.

Thanks to Ari Wagen for reading a draft of this post.


  1. It’s true that there are a lot of efforts to create larger supra-lab organizations to tackle big questions: in chemistry, the NSF has funded “centers for chemical innovation” to unite like-minded researchers. But trying to forge a functional organization from a myriad of independent sovereign teams seems much harder than simply starting a new organization de novo.
  2. What if the discoveries needed to advance science are too big for these proposed solutions? For instance, it’s tough to imagine funding a Manhattan Project-style endeavor this way. I don’t think that it’s the case that science requires government-scale resources to push past stagnation, but if that were really true, it might be an argument for using the full might of state capacity to drive research, like something out of Liu Cixin’s novels. Note, however, that this would make the task of “picking the right problems” that much more important.