If necessity is the mother of invention, could invention also be the mother of discovery? Historically, engineering inventions such as James Watt’s steam engine have triggered scientific discoveries, such as the laws of thermodynamics. More recently, Sir Tim Berners-Lee’s invention of the World Wide Web opened the way for fundamental advances in network and social systems theory.
These examples, among many others, suggest that young researchers would do well to set their minds to solving real-world problems while, at the same time, generalising their insights to make foundational scientific discoveries.
This formula is, of course, a departure from the 70-year-old linear model of research, in which basic research comes first, followed by applied research and then product development. Hypnotised by this model, many basic researchers work happily on esoteric problems of their own creation, hoping that some useful applications will eventually be found. They enjoy freedom to explore, but sometimes the constraints of working with domain experts on applied research are stronger catalysts for invention and discovery. At Google, practical problems drive its research projects, but these lead to foundational insights (and published papers), as well as widely used improvements in software.
This model – which I call Applied and Basic Combined, or the ABC principle – means that practitioners and theoreticians have to learn each other’s language, as well as their working methods and metrics for success. The intense conversations that clarify terminology, research practices and project goals may be the secret of success, allowing the progressive refinement of theories by way of real-world interventions.
Relatedly, there is also a momentum towards blending the methodologies of science, engineering and design (what I call the SED principle). In the past, the scientific method was the expected way to do research, but the prototyping by which engineering progresses has been widely adopted in many major research projects, such as the pursuit of subatomic particles or the exploration of huge social network databases. Scaling up from small prototypes and scaling out to accommodate diverse contexts helps to refine both practice and theory.
Design thinking is a newer research method that is increasingly popular. This advocates a stronger emphasis on reformulating project goals, questioning requirements, talking with users and inspiring creative solutions. Its rise is often tied to Apple’s success, but many businesses and universities are racing to train employees and students to – as Apple’s ads put it – “think different”. Design thinking benefits more than commercial product development: it is also a way of addressing the immense problems of healthcare delivery, community safety and energy conservation.
Replacing the linear model isn’t easy. It requires academic leaders, business managers and policymakers to reshape the way research is funded, students are trained, faculty are rewarded and universities are run. But progress is being made. Some funding schemes already encourage basic researchers and domain experts to team up. Partnerships between universities and businesses are on the rise, causing much discussion about the right way to share intellectual property while ensuring open publication of results. And teamwork, including across disciplines, is gaining attention as the strategies for enhancing its effectiveness become more widely known – especially given the evidence that teams are more likely to produce highly cited papers.
As universities shift to new models of research, traditional impact indicators, such as citation counts, are being supplemented by metrics such as web download counts, social media mentions and viral video views. Some institutions are doing more to recognise teamwork and reward novel contributions such as patents, business adoption, research dissemination and influence on public policy decisions.
The further good news is that research on research is also improving. This will lead to an even better understanding of what produces greater successes with higher impacts. We are all set for an era in which the processes of invention and discovery are significantly accelerated.
is professor of computer science at the University of Maryland. His book, , was published this month by Oxford University Press.
