About this section

As Knowledge Lab and the Metaknowledge Research Network crank out results, those results will populate this space. Our goal is to present citations (and links) to published research, as well as preprints, articles and prospectives, responses, as well as presentations of our work and results formatted for a general audience.


New Findings

Tradition and Innovation in Scientists’ Research Strategies

What factors affect a scientist’s choice of research problem? Qualitative research in the history, philosophy, and sociology of science suggests that this choice is shaped by an “essential tension” between the professional demand for productivity and a conflicting drive toward risky innovation. We examine this tension empirically in the context of biomedical chemistry. We use complex networks to represent the evolving state of  scientific knowledge, as expressed in publications. We then define research strategies relative to these networks. Scientists can introduce novel chemicals or chemical relationships—or delve deeper into known ones. They can consolidate existing knowledge clusters, or bridge distant ones. Analyzing such choices in aggregate, we find that the distribution of strategies remains remarkably stable, even as chemical knowledge grows dramatically. High-risk strategies, which explore new chemical relationships, are less prevalent in the literature, reflecting a growing focus on established knowledge at the expense of new opportunities. Research following a risky strategy is more likely to be ignored but also more likely to achieve high impact and recognition. While the outcome of a risky strategy has a higher expected reward than the outcome of a conservative strategy, the additional reward is insufficient to compensate for the additional risk. By studying the winners of 137 different prizes in biomedicine and chemistry, we show that the occasional “gamble” for extraordinary impact is the most plausible explanation for observed levels of risk-taking. Our empirical demonstration and unpacking of the “essential tension” suggests policy interventions that may foster more innovative research.

For More: Foster, Jacob G., Andrey Rzhetsky, and James A. Evans. “Tradition and Innovation in Scientists’ Research Strategies.”  American Sociological Review 0003122415601618 (2015). doi:10.1177/0003122415601618.

Weaving the fabric of science: Dynamic network models of science’s unfolding structure

Science is a complex system. Building on Latour’s actor network theory, we model published science as a dynamic hypergraph and explore how this fabric provides a substrate for future scientific discovery. Using millions of abstracts from MEDLINE, we show that the network distance between biomedical things (i.e., people, methods, diseases, chemicals) is surprisingly small. We then show how science moves from questions answered in one year to problems investigated in the next through a weighted random walk model. Our analysis reveals intriguing modal dispositions in the way biomedical science evolves: methods play a bridging role and things of one type connect through things of another. This has the methodological implication that adding more node types to network models of science and other creative domains will likely lead to a superlinear increase in prediction and understanding.
 For More: Shi, F., Foster, J.G., Evans, J.A., 2015. Weaving the fabric of science: Dynamic network models of science’s unfolding structure. Social Networks 43, 73–85. doi:10.1016/j.socnet.2015.02.006