Choosing the Next Experiment: Tradition, Innovation, and Efficiency in the Selection of Scientific Ideas (Innovation and Digital Scholarship Lecture Series)
Thursday 21 March 2013, 17:00:00 - 18:30:00
Oxford Internet Institute, 1 St Giles, Oxford OX1 3JS United Kingdom
To attend, please email your name and affiliation to firstname.lastname@example.org
Economic and Social Research Council (ESRC), Oxford e-Research Centre (OeRC), Bodleian Libraries and Digital Social Research
Summary to follow.
What factors affect a scientist’s choice of research problem? Qualitative research in the history, philosophy, and sociology of science suggests that the choice is shaped by an 2essential tension2 between a 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 digital publications. We then build measurements and a model of scientific discovery informed by key properties of this network. Measuring 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 major prizes, we show that the occasional “gamble” for extraordinary impact is the most plausible explanation for observed levels of risk-taking. To examine efficiency in scientific search, we build a model of scientific discovery informed by key properties of this network, namely node degree and inter-node distance. We infer the typical research strategy in biomedical chemistry from 30 years of publications and patents and compare its efficiency with thousands of alternatives. Strategies of chemical discovery are similar in articles and patents, conservative in their neglect of low-degree, distant or disconnected chemicals, and efficient only for initial exploration of the network of chemical relationships. We identify much more efficient strategies for maturing fields.
About the speakers
Professor James EvansAffiliation:
James Evans is Senior Fellow at the Computation Institute, Associate Professor of Sociology and member of the Committee on Conceptual and Historical Studies of Science at the University of Chicago. Evan’s work explores how social and technical institutions shape knowledge – science, scholarship, law, news, religion – and how these understandings reshape the social and technical world. He has studied how the Internet and Open Access shapes knowledge in society. He has also investigated the relation of markets to science by examining how industry collaboration shapes the ethos, secrecy and organization of academic science; the web of individuals and institutions that produce innovations; and markets for ideas and their creators. Finally, Evans is interested in using digital scholarship to identify biases in research and discovery and then using these as statistical instruments to identify promising but under-appreciated hypotheses and unasked questions. He is currently working on related projects in biology, chemistry, and medicine that explore these possibilities. His work uses natural language processing, the analysis of social and semantic networks, statistical modeling, and field-based observation and interviews. Evan’s research is funded by the National Science Foundation, the National Institutes of Health, the Mellon and John F. Templeton Foundations and has been published in Science, American Journal of Sociology, Social Studies of Science, Administrative Science Quarterly and other journals. His work has been featured in the Economist, Atlantic Monthly, Wired, NPR, BBC, El Pais, CNN and many other outlets.