C.Chen，Y. Chen，M. Horowitz，H. Hou, Z. Liu, D. Pellegrinoa. Towards an explanatory and computational theoryof scientific discovery. Journal of Informetrics，2009（3）：191-209. （SSCI收录）影响因子2.531（该刊在国际图书馆与信息科学领域排名第3位），在web of science中被引10次，在google学术搜索被引94次。

Towards an explanatory and computational theory of scientific discovery.pdf

We propose an explanatory and computational theory of transformative discoveries in sci-

ence. 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, soci-

ology 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 cre-

ative 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 opti-

mal 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 net-

works 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.