A bird’s eye view of the evolution of disciplines begs the question of what produces changes of direction in a field. Most fields have periods of stability during which the set of motivating questions are well accepted and pursued. During these times, deeper and deeper exploration of key questions, of theory and application, persist in relative equilibrium.
However, throughout history there are moments of rapid change in fields and disciplines. For example, the invention of the microscope in the 16th-17th century, coming out of the thinking of astronomers’ telescopes, prompted a transformation in theories of life. Indeed, it could be argued that the tool was key to the explosion of insights within biology, cell theory, and ubiquity of bacteria throughout the natural world. Long-term effects of this new tool include the evolution of medicine as a science-based profession.
Much more recently, and of personal interest to me, is the importation of statistical sampling theory to human measurement. In the depths of the US depression in the 1930’s, there were no useful estimates of what portion of the working age public was unemployed and seeking work. Government policy had blinders that limited its effectiveness. Only in 1940 was the first proper statistical sample survey of the US labor force conducted, which offered estimates of unemployment with theoretically sound margins of sampling error. This importation from statistics to the social sciences radically altered the course of knowledge-building in the social sciences in the 20th century. A tool from one field was used to expand knowledge in another field.
There are many other examples of enhanced tools of measurement spurring innovation. Many of these are tools developed in other than the one in which the innovation is spurred.
This is a post observing a related phenomenon – sometimes the use of a tool creates a new interdisciplinary field.
There are centuries of study of the human nervous system and the human brain. In parallel, for many decades psychology as a field sought insights into cognition and emotion, the mental functions underlying individual and group behavior. Psychological insights into neural functioning were plagued by inability to make direct observations, except in extreme cases of brain trauma. But psychology had developed a research culture of rigorous experimental design to draw causal inferences. As more advanced neuroimaging devices (e.g., fMRI, PET) emerged in biomedical practice, psychology discover the value of those tools for their own inquiries. Eventually, the tools of experimental design and neuroimaging came together, along with cognitive psychology and neuroscience to produce a new subfield of cognitive neuroscience, to study how psychological functions are produced by neural circuitry.
Another example is the development of machine learning within computer science, algorithms that improve their prediction of outcomes by ingestion of new data. The large family of machine learning approaches informs artificial intelligence, as a field of application. Statistics, on the other hand, developed as a discipline through formal theoretical proofs of properties of statistical information, as a branch of mathematics. The confrontation of machine learning with statistics (and a set of other subfields) is producing the new field of data science, which blends together tools from the two fields.
Many fields separate themselves into theory-building and applications/experimentation. These subfields can proceed in happy equilibrium for many decades. However, the importation or invention of new tools of measurement or analysis often acts as a disruptor to this equilibrium. It is exciting from the outside to watch a field undergoing these changes, but it can even be more deeply energizing to be inside a field going through such changes.
Some of our best scholars are doing their reading in other fields, looking for opportunities to find new tools and new ways of thinking to apply to their own field. They often become leaders of the next evolutionary step.