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Science Forecasts: Modeling and Communicating Developments in Science, Technology, and Innovation

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Springer Handbook of Science and Technology Indicators

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Abstract

In a knowledge-based economy, science and technology are omnipresent, and their importance is undisputed. Equally evident is the need to allocate resources, both monetary and human, in an effective way to foster innovation [6.1, 6.2]. In the preceding decades, science policy has embraced data mining and metrics to gain insights into the structure and evolution of science and to devise metrics and indicators [6.3], but it has not invested significant efforts into mathematical, statistical, and computational models that can predict future developments in science, technology, and innovation ( ) in support of data-driven decision making.

Recent advances in computational power combined with the unprecedented volume and variety of data concerning science and technology developments (e. g., publications, patents, funding, clinical trials, and stock market and social media data) yielded ideal conditions for the advancement of computational modeling approaches that can be not only empirically validated, but used to simulate and understand the structure and dynamics of STI in support of improved human decision making.

In this chapter, we review and demonstrate the power of computational models for simulating and predicting possible STI developments and futures. In addition, we discuss novel means to visualize and broadcast STI forecasts to make them more accessible to general audiences.

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Acknowledgements

The work was supported in part by the National Institutes of Health under awards U01CA198934, P01AG0393, and OT2OD026671 and National Science Foundation awards 1546824, 1713567, 1735095, and 1839167, NETE Federal IT, Thomson Reuters, Indiana University Network Science Institute, and the Cyberinfrastructure for Network Science Center at Indiana University. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Authors and Affiliations

  1. School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Katy Börner & Staša Milojević

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  1. Katy Börner
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  2. Staša Milojević
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Correspondence to Katy Börner .

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Editors and Affiliations

  1. ECOOM and Faculty of Economics and Business, KU Leuven, Leuven, Belgium

    Wolfgang Glänzel

  2. Amsterdam, The Netherlands

    Henk F. Moed

  3. Competence Center Policy – Industry – Innovation, Fraunhofer Institute for Systems and Innovation Research ISI, Karlsruhe, Germany

    Ulrich Schmoch

  4. Faculty of Science and Engineering, University of Wolverhampton, Wolverhampton, UK

    Mike Thelwall

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Börner, K., Milojević, S. (2019). Science Forecasts: Modeling and Communicating Developments in Science, Technology, and Innovation. In: Glänzel, W., Moed, H.F., Schmoch, U., Thelwall, M. (eds) Springer Handbook of Science and Technology Indicators. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-030-02511-3_6

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