Exploring Patterns of Knowledge Production

I’m posting up some work-in-progress entitled Exploring Patterns of Knowledge Production (link to full pdf) that follows up to my earlier post of a year and a bit ago. Below I’ve excerpted the introduction plus list of motivational questions. Comments (and critique) very welcome!

Exploring Patterns of Knowledge Production Paper ‘Alpha’ (pdf)


In what follows 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.

Today, thanks to rapid advances in IT, we have available substantial datasets pertaining both to the extent and the structure of knowledge production across disciplines, space and time.

Especially recent is the availability of good ‘structural’ data — that is data on the linkages and relationships of different pieces of knowledge, for example as provided by citation information. This new material allows us to explore the “patterns of knowledge production” in deeper and richer ways than ever previously possible and often using entirely new methods.

For example, it has long been accepted that innovation and creativity are cumulative processes, in which new ideas build upon old. However, other than anecdotal and case-study material provided by historians of ideas and sociologists of science there has been little data with which to study this issue — and almost none of a comprehensive kind that would make possible a systematic examination.

However, the recent availability of comprehensive databases containing ‘citation’ information have allowed us to begin really examining the extent to which new work builds upon old — be it a new technology as represented by a patent or a new idea in academia as represented by a paper, builds upon old.

Similar opportunities present themselves in relation to identifying the creation of new fields of research or technology, and tracing their evolution over time. Here the existence of extensive “structural information” as presented, for example, by citation databases, enables new systematic approaches — for example, can new fields be identified (or perhaps defined) as points in ‘knowledge space’ far away from the existing loci of effort? or, alternatively, by the nature of its connections to the existing body of work?

Structural information of this kind can also be used in charting other changes in the life-cycle of knowledge creation. For example, to offer a specific conjecture, a field entering decline, though still exhibiting a similar level of output (papers etc) and even citations to a field in rude health, may display a citation structure which is markedly different — for example, more clustered within the field itself. Thus, by using this additional structural information we may be able to gain insights not available with simpler approaches.

At the same time, structure must also play a central role in any attempt to estimate knowledge related ‘output’ measures. This is of course not true for other forms of ‘output’, for example that of corn of steel, where we have relatively well-defined objective measures available: tonnes of such-and-such a quality.

But knowledge is different: the most obvious metrics, such as number of patents or papers produced, seem entirely inadequate: one particular innovation or paper may be ‘worth’ as much as a hundred or a thousand others.

The issue here is that, compared to corn or steel, knowledge is extremely inhomogeneous, or put slightly differently, quality (or significance) differs very substantially across the individual pieces of knowledge (papers, patents etc).

Thus, any serious attempt to measure the progress of knowledge must must find some way to do this quality-adjustment and structural information seems essential to this.

What specific questions might we explore with such datasets?

The following is a (non-exhaustive) list of the kinds of questions one might explore using these new datasets:

  • 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 estimated based 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?
  • Can we use structural information to investigate the life-cycle of fields? For example, can we see fields ‘dying out’ or the onset of diminishing returns? Can we see new fields coming into existence and their initial growth patterns?
  • 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 or do small shocks have large long-term effects?


Update: 2011-01-31: have now broken out data worked into dedicated repos on bitbucket.

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