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.
Bibliographical noteFunding Information:
This work is supported in part by the National Science Foundation (NSF) under grant number IIS-0612129, the US Department of Homeland Security through the Northeast Visualization and Analytics Center (NEVAC), and the Chang Jiang Scholar program of the Chinese Ministry of Education. Thanks to Xianwen Wang and Xiaoyu Zhu of Dalian University of Technology, China, for their assistance in data collection and preliminary analysis of gene targeting and peptic ulcer cases, Juan Maldacena, Institute for Advanced Study, for responding to our inquiries, and anonymous reviewers for their detailed and constructive comments.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Library and Information Sciences