data science and the university
This is by now a familiar line of thought but it has just now struck me with clarity I wanted to jot down.
- Code is law, so the full weight of human inquiry should be brought to bear on software system design.
- (1) has been understood by “hackers” for years but has only recently been accepted by academics.
- (2) is due to disciplinary restrictions within the academy.
- (3) is due to the incentive structure of the academy.
- Since there are incentive structures for software development that are not available for subjects whose primary research project is writing, the institutional conditions that are best able to support software work and academic writing work are different.
- Software is a more precise and efficious way of communicating ideas than writing because its interpretation is guaranteed by programming language semantics.
- Because of (6), there is selective pressure to making software the lingua franca of scholarly work.
- (7) is inducing a cross-disciplinary paradigm shift in methods.
- (9) may induce a paradigm shift in theoretical content, or it may result in science whose contents are tailored to the efficient execution of adaptive systems. (This is not to say that such systems are necessarily atheoretic, just that they are subject to different epistemic considerations).
- Institutions are slow to change. That’s what makes them institutions.
- By (5), (7), and (9), the role of universities as the center of research is being threatened existentially.
- But by (1), the myriad intellectual threads currently housed in universities are necessary for software system design, or are at least potentially important.
- With (11) and (12), a priority is figuring out how to manage a transition to software-based scholarship without information loss.