By Keith McDowell
It used to be so easy. The “lone wolf” researcher observed natural phenomena and collected data using homemade equipment. Or perhaps those with a theoretical bent puzzled over data and speculated on a new theory using only pencil, paper and their native intellect. Einstein became the poster child for the iconoclastic scientist with his unkempt appearance and penetrating, but friendly, eyes. Rarely did a polymath appear able to leap over discipline boundaries ala Superman. Such nimble gymnastics mostly weren’t needed.
But then the Twentieth Century arrived with an exponential explosion of science, engineering, and technology. Disciplinary research boundaries collapsed as interdisciplinary became the buzzword of the middle part of the century followed by multidisciplinary and now transdisciplinary in the first decade of the Twenty-First Century. The “lone wolf” or individual researcher was overrun by teams, research centers and institutes, national laboratories, industrial R&D laboratories, and now “lablets,” innovation hubs, and innovation centers. The century of the physical sciences was replaced by a spurt in the life sciences.
The structure of funding for research moved from potentates and personal donors to industry and government while the nature of the funding shifted from pure basic research with scientific significance as the principal measure for funding to use-directed research with “broader impact” – often under the umbrella of grand challenges – as a significant metric. Of course, what constitutes “broad impact” or “relevance” as it is sometimes known is mostly in the eye of the beholder. Some would even argue that the skill of grantsmanship supercedes the natural research ability of researchers when it comes to promotion or tenure. Certainly one’s record of grantsmanship is equally as important as one’s publication record, almost independent of the quality of the research.
And then we have the phenomenon of “relevance” and “broad impact” being overtaken and encompassed by the newest trend: the commercialization of university research and the desire to include innovation, commercialization, and entrepreneurial metrics as measures of faculty productivity. Even teaching with the advent of a multiplicity of “learning styles” and the concomitant introduction of many new advanced technologies for delivering content has not been immune to transformational change – not to mention the rapid expansion of the knowledge frontier and the race to keep up in lecture content and textbooks.
From another perspective, the collapse at the end of the Twentieth Century of the meso-scale or nano-scale barrier that bridges atoms and molecules to the micro-scale along with the parallel growth and ability to attack biological systems brought about a new research concept or paradigm: convergence. Convergence was celebrated by a new acronym “nbic” which stands for nano-bio-info-cogno. Later, the letter “e” was added at the end to include “eco.” Personally, I prefer to rearrange the letters of the acronym to “bnice.” Somehow, the phrase “be nice” sounds better than the geek speak “nbice.”
Coupled to the concept of convergence was the equally important, if not more important, concept of “complexity.” Complexity is in some sense a measure of the connectivity of knowledge or networks. Together, convergence and complexity along with other related events led to the creation of network science, an approach to parsing phenomena into three categories: physical, biological, and social. Network science entails a systems view of the world with layered architectures as the dominant structure and “emergent phenomena” occurring in the higher-tiered layers. Life itself is considered an emergent process in the macroworld which itself is built upon the micro-, nano-, and atomic and molecular layers. Ray Kurzweil, the futurist, postulated in his book, The Singularity is Near, that the complexity of computers is poised to surpass that of the human brain and that computers will soon become “self-aware” as an emergent phenomenon. What will happen to humankind as such self-aware computers become exponentially brilliant and able to assess all known knowledge at nearly the speed of light?
Will scientific research ever get a pink slip? John Horgan in his book The End of Science would make us believe so. Convergence, network science, and complexity theory might lead us to think so as we pull together all branches of science into the final grand frontier. It is an interesting debate, best left for now to the coffee klatch and student debate. It certainly is the case that full access to the nanoscale and BNICE convergence have brought about the social phenomena of self-assembly of STEM and health personnel into teams taking on societal problems. Furthermore, global competition and the resulting explosion in the commercialization of university research have taken us to the transdisciplinary age with STEM and health teams joining forces with business and legal teams as well as those who understand the social dimensions to ensure prosperity for Americans. As I like to tell my colleagues in all fields of endeavor, what is easy has been done! Get used to it!
What are the implications for this massive paradigm shift in how we do research, keeping in mind that the scientific method per se has not changed? Indeed, ARE there any implications, especially for the innovation ecosystem? Certainly, we must at a minimum be aware that a transformation has occurred however one chooses to characterize it. In my experience, far too many people simply don’t get it and merely view what is happening as a treadmill dialed to a faster speed. While that is true, it is only a fraction of the real story.
In other articles and in Go Forth and Innovate, I’ve addressed in my opinion some of the changes that need to occur in grantsmanship, publication of research, peer review, research compliance, and a retuning of academe to replace the service function with a community engagement function that includes all aspects of innovation and entrepreneurship. Each of these areas and many more deserve a detailed review. But it is important that such reviews and the subsequent changes that are made be understood within the broader context of how the scientific endeavor has changed. It is important for all to understand that what is easy has been done.