Rick Shang (Washington University, St. Louis)
I first argue that philosophers' interest in unique and distinctive forms of knowledge in engineering cannot explain the epistemic collaboration between science and engineering. I then argue that, using the early history of neuroimaging as my case study, fitting knowledge both captures the distinctive nature of engineering and enables fruitful collaboration between science and engineering.
On the one hand, philosophers of science are increasingly interested in cross-discipline, cross-industry collaboration. The general philosophical interest reflects the reality that contemporary research is often interdisciplinary and interfield. For example, the development of the Large Hadron Collider is critical in basic physics research.
On the other hand, philosophers of engineering are interested in unique, distinctive forms of engineering knowledge that are separate from scientific knowledge. For example, Bunge, a pioneer in philosophy of engineering, talks about operative knowledge in engineering. Operative knowledge is a kind of “superficial” knowledge that is rough but sufficient for action. For example, knowledge sufficient for driving a car involves minimal knowledge of the mechanism of the car.
The challenge to philosophers of engineering, then, is how distinctive forms of engineering knowledge can learn from and inform scientific knowledge to enable science-engineering collaboration.
I suggest that philosophers should look at the early history of neuroimaging. The earliest instrument to measure positron emission came out of nuclear physics research into the nature of positron emission and annihilation in the 50s. Medical researchers quickly adopted the instrument to study the anatomy and physiology by introducing positron emitting isotopes into animal and human bodies. The adoption initially received lukewarm reception because existing technologies were already able to produce similar data at one tenth the cost. After years of adjusting and trying, medical researchers in the 70s decided to focus on the real time, in vivo measurement of cerebral physiological changes, because the positron emission detection instrument could perform scans faster than all existing technologies.
The history demonstrates the development of fitting knowledge in engineering. The fitting knowledge involves knowledge of what the engineered mechanism was best for. It involves mutual adjustments of the mechanism and potential uses to find a socially and scientifically viable fit between the mechanism and its use(s).
This form of knowledge is uniquely engineering because it is primarily about the adjustment of engineered mechanism and its uses. It does not involve extended research into natural phenomena. For example, both the rapid nature of cerebral physiological changes and the scientific importance of capturing the changes in real time were well known at the time.
Fitting knowledge, at the same time, bridges across science and engineering. First, the creation of the original mechanism often involves the input of scientific knowledge. In my case, the indispensable input was the nature of positron emission. Second, finding the best fit often involves scientific considerations and goals. In my case, the new use turned out to be measuring cerebral processes in real time. Locating the fit quickly enabled the scientific study of the physiological basis of cognition.
