Towards an explanatory and computational theory of scientific discovery

By Chaomei Chen, Yue Chen, M. Horowitz, Haiyan Hou, Zeyuan Liu, Donald A. Pellegrino. J. Informetrics, 2009

447 citations recorded by the source corpus.

We propose an explanatory and computational theory of transformative discoveries in science. The theory is derived from a recurring theme found in a diverse range of scientific change, scientific discovery, and knowledge diffusion theories in philosophy of science, sociology of science, social network analysis, and information science. The theory extends the concept of structural holes from social networks to a broader range of associative networks found in science studies, especially including networks that reflect underlying intellectual structures such as co-citation networks and collaboration networks. The central premise is that connecting otherwise disparate patches of knowledge is a valuable mechanism of creative thinking in general and transformative scientific discovery in particular. In addition, the premise consistently explains the value of connecting people from different disciplinary specialties. The theory not only explains the nature of transformative discoveries in terms of the brokerage mechanism but also characterizes the subsequent diffusion process as optimal information foraging in a problem space. Complementary to epidemiological models of diffusion, foraging-based conceptualizations offer a unified framework for arriving at insightful discoveries and optimizing subsequent pathways of search in a problem space. Structural and temporal properties of potentially high-impact scientific discoveries are derived from the theory to characterize the emergence and evolution of intellectual networks of a field. Two Nobel Prize winning discoveries, the discovery of Helicobacter pylori and gene targeting techniques, and a discovery in string theory demonstrated such properties. Connections to and differences from existing approaches are discussed. The primary value of the theory is that it provides not only a computational model of intellectual growth, but also concrete and constructive explanations of where one may find insightful inspirations for transformative scientific discoveries.

Subfields: Molecular Biology, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/170fb17294378916784b1ac93e5d15a33c8d9563

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Citations in the corpus

Science Mapping: A Systematic Review of the Literature (2017, background): “Studies have shown that nodes with high betweenness centrality values tend to identify boundary spanning potentials that may lead to transformative discoveries (Chen et al., 2009).”
The Role of Gender in Scholarly Authorship (2012, background): “Here we show that women historically have been underrepresented in the first author position, though this is changing, and that women are currently underrepresented in the last author position.”
Bias against Novelty in Science: A Cautionary Tale for Users of Bibliometric Indicators (2017, unknown): “or bridging structural holes in the network of science (Chen et al., 2009; Rzhetsky, Foster, Foster, & Evans, 2015; Shi, Foster, & Evans, 2015).”
Identifying emerging topics in science and technology (2014, methodology): “Chen and colleagues used a combination of co-citation analysis and burst detection to characterize emerging trends in the fields of mass extinction and terrorism (Chen, 2006), peptic ulcer, gene targeting and string theory (Chen et al., 2009) and regenerative medicine (Chen et al., 2012).”
Predictive effects of structural variation on citation counts (2012, methodology): “For example, the structural hole theory developed in social network analysis emphasizes the special potential of nodes that are strategically positioned to form brokerage, or boundary-spanning, links and create good ideas (Burt, 2004; Chen et al., 2009).”
Bibliometric visualisation: an application in tourism crisis and disaster management research (2019, background): “Sigma The combined strength of structural and temporal properties of a node, namely, its betweeness centrality and citation burst (Chen et al., 2009).”
Line graphs, link partitions, and overlapping communities. (2009, background): “It can be shown that optimising (5) typically leads to partitions made of larger and larger communities for increasing times and that the optimal partition when n → ∞ is made of two communities [17, 18].”
The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis (2010, unknown): “As demonstrated in case studies (Chen et al., 2009a), Nobel Prize and other award winning research tends to have highest values of this measure.”