A definition: the term ‘knowledge’ is here used broadly to signify all forms of information production including those involved in technological innovation, cultural creativity and academic advance.
Largely as a result of better ICT we now have available some very substantial datasets regarding both the extent and structure of knowledge production across different jurisdictions and different disciplines.
Of particular interest here is this is second aspect: the structure of knowledge production; as it has long been accepted that innovation and creativity are cumulative processes, in which new ideas build upon old.
However, other than the anecdotal and case-study material provided by historians of ideas and sociologists of science there has been little evidence on this issue – and almost none of a comprehensive kind that would make a systematic examination possible.
In particular, the existence of databases containing ‘citation’ information allows us to, at least partially, determine the extent to which new work, be it a new technology as represented by a patent or a new idea in academia as represented by a paper, builds upon old.
What specific issues might we explore with such datasets?
Given the availability of these new datasets and the basic cumulative nature of most knowledge production what specific issues and question might we explore? The following provides a basic, but non-exhaustive, list:
- Can we use structure to infer information about quality of individual items? Clearly the answer is yes, for example by using a citation-based metric where a work’s value is computed on its citation by others.
- Can we then use this information together with more global structure of the production network to gain a better idea of total (quality-adjusted) output. This would allow one to chart progress, or the lack of it, over time?
- What about productivity per capita and its variation across the population? It is likely that one would need to focus here within a discipline as it would be difficult to directly compare across disciplines, at least when using quality adjusted productivity.
- Do the structures of knowledge production vary over time and across disciplines and does this have implications for their productivity? Can we compare the structure of evolution in technology or economics with that in ‘natural’ evolution and, if not, what are the primary differences?
- How do other (observable) attributes related to the producers of knowledge (their collaboration with others, their geographical location) affect the structures we observe and the associated outcomes (output, productivity) already discussed above?
- Do different policies (for example openness vs. closedness – weak vs. strong IP) have implications for the structure of production and hence for output and productivity?
- Is knowledge production (in a particular area) ergodic or path-dependent? Crudely: do we always end up in the same place and do small shocks have small or large effects in the long term?