by Stuart Buck
For many decades, science policy and funding have made a distinction between “basic” and “applied” research. But the book Cycles of Invention and Discovery by Venkatesh Narayanamurti and Toluwalogo Odumosu argues that basic vs. applied is the wrong distinction. Instead, we need to think of science as occurring in a “discovery-innovation cycle” in which discovery and innovation work hand-in-hand, in an ongoing cycle.
This model seems extraordinarily useful for new initiatives (such as ARPA-H or the new NSF Directorate for Technology, Innovation, and Partnerships), as well as for existing agencies to consider. Metaphors and models matter, after all. They can open our minds and policies to better ways of doing science–or they can be an obstacle to good science.
Let’s take a step back, though. The now-traditional view can be seen in this typical chart from the NIH:
As you can see, the percentages sum to 100 in each year: 100% of research is either basic or applied, with no overlap or ambiguity. Never the twain shall meet.
But what exactly is the difference between basic and applied research? Can they be so easily pieced apart?
One distinction comes from Vannevar Bush, the eminent Roosevelt advisor whose monograph The Endless Frontier has been an inspiration to generations of science policy advisors. For Bush, the difference between basic and applied science is in how practical and predictable it is. In his view, basic science “is performed without thought of practical ends,” but is aiming at “general knowledge and understanding of nature and its laws.” He added that it is hard to predict the results of basic science—some of the results may, someday, lead to practical applications that would never have been envisioned before, and that may be far removed from the original finding.
Another distinction is in how well-defined the problem is, versus how much freedom the scientist has to explore outside of a well-defined problem. That distinction can be found in a National Science Foundation report from 1953: “The essential difference between basic and applied research lies in the freedom permitted the scientist. In applied work, his problem is defined and he looks for the best possible solution meeting these conditions. In basic research he is released of such restrictions; he is confined only by his own imagination and creative ability.”
A related distinction is whether the scientist is motivated by curiosity or by a particular use. The great Abraham Flexner – whose 1910 report on medical schools revolutionized how doctors were trained, and who founded the Institute for Advanced Study in Princeton – wrote a classic essay called “The Usefulness of Useless Knowledge.” Drawing on many examples, ranging from the invention of the radio to the development of mathematical theories, he made the case that great insights often arise when a researcher is just exploring nature out of sheer curiosity.
A typical passage from his essay: “No educational administrator can possibly direct the channels in which these or other men shall work. The waste, I admit again, looks prodigious. It is not really so. All the waste that could be summed up in developing the science of bacteriology is as nothing compared to the advantages which have accrued from the discoveries of Pasteur, Koch, Ehrlich, Theobald Smith, and scores of others—advantages that could never have accrued if the idea of possible use had permeated their minds.”
Hmmm. Taking all of this into account, we are left with vague and ambiguous criteria like:
- Was the scientist motivated by curiosity, or by usefulness?
- Was the scientist free to explore, or was the scientist working on a defined problem?
- Was the scientist working on a practical problem with predictable consequences, or on a more fundamental understanding of the universe?
You might notice that these criteria aren’t mutually exclusive at all!
It is quite possible to work on practical and well-defined problems or on an actual use case, and yet to do so out of curiosity, or with the freedom to explore various aspects of the problem, or for such research to have downstream impacts that are hard to predict.
Alternatives to Basic vs. Applied
As a result, some have argued that science isn’t as binary between basic and applied as the NIH’s chart would suggest. Indeed, Donald Stokes’ book Pasteur’s Quadrant famously points out that we should think of research along two dimensions rather than one: Whether it is undertaken with considerations of use, and whether it is undertaken out of a quest for fundamental understanding. The resulting quadrant:
In box 1 is “pure basic research,” done solely for fundamental understanding with no usefulness in sight. In box 4 is “pure applied research” like Edison—just inventing and developing new technologies without necessarily caring about more general scientific principles. Box 2 is where we find Pasteur’s “use-inspired basic research,” e.g., research into vaccines both to understand the scientific principles and to come up with a working vaccine.
[Let’s leave aside the possibility that some scientific research (perhaps a lot!) might actually lie in the supposedly empty box 3 – i.e., research that doesn’t improve our fundamental understanding of anything, but neither does it come up with any useful products, treatments, inventions, etc.]
Cycles of Invention and Discovery
Thus we get to Narayanamurti and Odumosu (which I’ll abbreviate “N/O”). They argue that the classification of basic vs. applied is not only wrong, but “has significant negative effects on the practice of research” by creating “significant dissonance between what researchers actually have to do to get their jobs done, and the categories and classification systems that fund and govern their work.”
One example of “negative effects”: A 2015 controversy in Congress over whether the Department of Energy was “putting too much emphasis on applied energy research” and of “valu[ing] applied R&D over basic scientific research.”
In N/O’s view, there was no need for such a controversy in the first place, because there is not really any such thing as a sharp distinction between basic and applied.
Instead, to use their words:
“Research, by its very nature, is integral. It constantly shifts between discovery and invention. Research leads to new ideas, new tools, and new devices. Classifying certain research activity as ‘basic’ and other activity as ‘applied,’ and adopting funding models that reinforce this division, is highly problematic.
For one, it creates conflict by pitting ‘basic’ research against ‘applied’ research. This false dichotomy is taken up by the program managers who make funding decisions. These program managers require researchers to write grant applications that conform to the rational of ‘basic’ vs. ‘applied’ research.
Researchers, in turn, organize their activities and their laboratories in such a way as to optimize their productive efforts to fulfill the stated objectives of their funded grants.”
Another example, also from energy research:
The Energy Frontier Research Centers (of which there are 41) have been doing “excellent work in materials science and engineering,” including “great strides in understanding the physics and the chemistry of their various projects.”
According to the federal webpage, these Centers “represent a unique approach, bringing together creative, multi-disciplinary scientific teams to tackle the toughest scientific challenges preventing advances in energy technologies.”
Yet N/O found that these Centers refused “to actually build devices to prototype their ideas.”
The Center directors “argued that they could not build prototypes because to do so would mean that they were no longer doing ‘science,’” at least not the “basic” science that they were supposed to do.
To N/O, that state of affairs is “shocking” – such an artificial refusal to do good scientific work is contrary to some of the best examples of high-impact team science in history (e.g., Bell Labs).
Perhaps even worse, they interviewed some energy researchers who confidentially “admitted to using obfuscatory language in their reports and proposals in a bid to placate their program managers—for example, avoiding the term ‘device.’ In essence, they were hiding their efforts to go where their research led them.”
As N/O observe, “If Bell Labs had been organized in such a way, it would never have invented the transistor, and it would not have accomplished any of the other breakthroughs associated with it.”
* * *
So how should we characterize science if we abandon “basic vs. applied”? Or to be more specific, what if we abandon the idea that R&D proceeds along a completely linear timeline:
Basic Research–>Applied Research–>Product Development–>Market Diffusion and Deployment
N/O say that Stokes didn’t go far enough with his Pasteur’s Quadrant. Indeed, that classification is still too obsessed (in their view) with knowing the researcher’s motivation in any particular case—is the researcher thinking about knowledge, or about use, or about both? Indeed, Pasteur’s Quadrant could be seen as merely expanding the linear model a bit by inserting “use-inspired basic research” in between true “basic research” and true “applied research.”
Instead, N/O say that we need a more “integrated” and “holistic” model of science that they call “the discovery-invention cycle.” They define discovery as the “creation of new knowledge and facts about the world,” while “invention” is the “accumulation and creation of knowledge that results in a new tool, device or process that accomplishes a particular, specific purpose” (pp. 31-32).
You might think that these definitions end up replicating “basic vs. applied.” And that would be a fair point: “Discovery” sounds a lot like basic research, and “invention” sounds a lot like applied research.
But I think N/O’s real point in changing terminology here is to break away from both the linear approach, and from the focus on a scientist’s initial motivations (which hardly matter in the end).
Instead, we need to think about discovery and invention working both:
- hand-in-hand in the same organization or even in the same research project, as well as
- cyclically and interdependently, in which new discoveries can lead to new tools and products, but new tools and products can equally lead to further insights about fundamental principles.
To me, the above is the crux of N/O’s insight into how science works. It’s how high-performing institutions tend to function. And we need the funding and organizational commitments to match.
* The book also has an extensive analysis of the intellectual culture at Bell Labs and Janelia, with chapter titles like “Designing Radically Innovative Research Institutions.” But that’s for another day—for now, reconsidering “basic vs. applied” is enough to chew on.