Forecasting science:
Models of science and technology dynamics for innovation policy


June 29, 2015

Meeting Place:

15th International Conference on Scientometrics & Infometrics
Bogazici University
Istanbul, Turkey


Katy Börner (Indiana University, USA)
Andrea Scharnhorst (KNAW, The Netherlands)
Stasa Milojevic (Indiana University, USA)
Petra Ahrweiler (Director and CEO, EA European Academy of Technology and Innovation Assessment GmbH, Bad Neuenahr-Ahrweiler, Germany)
David Chavalarias (Centre d'Analyses de Mathématiques Sociales (CAMS), Ecole des Hautes Etudes en Sciences Sociales (EHESS), Director of the Complex Systems Institute of Paris Ile-de-France, Paris, France)
Santo Fortunato, Professor of Complex Systems of the Department of Biomedical Engineering and Computational Science (BECS) of the School of Science of Aalto University in Espoo, Finland
Advisor: Nicolay Vitanov (Professor, Institute of Mechanics, Bulgarian Academy of Sciences, Alexander von Humboldt Fellow)

organizer photo

Summary: 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 labor) in an effective way to foster innovation. In the last decades, science policy has embraced scientometrics to gain insights into the structure and evolution of science and devised diverse metrics and indicators. However, it has not invested significant efforts into modelling the dynamics of science, technology, and/or innovation (STI) (mathematically, statistically, and computationally). While it may not be possible to predict the nature and essence of the next scientific or technological innovation, it is often possible to predict the circumstances leading to it, i.e., where it is most likely to happen and under which conditions. Some examples are: Which career paths are more likely to lead to high impact works? Which funding system has the highest return on investments? Which institutions will be most productive over the next years? This workshop invites the community of researchers working on models of STI to both share their latest research and collectively create a roadmap to foster future modeling efforts. Extended abstracts are solicited for presentation and will be reviewed by the workshop organizing committee. We specifically seek models which predict/forecast the structure and/or dynamics of STI. The focus of the workshop is on mathematical, statistical, and computational models, but we do not exclude qualitative models as long as they can be used to develop scenarios of future STI dynamics.


16:15 Welcome by Organizers
16:20 Overview of existing model types by Nikolay Vitanov & Andrea Scharnhorst
16:30 Presentation of key models:

Governance of research and innovation networks by Petra Ahrweiler
Models of citation dynamics by Santo Fortunato
Modeling the Social Game of Science when time matters by David Chavalarias
From funding agencies to scientific agency by Katy Börner

17:00 Brainstorm on

–Taxonomy of Existing Model Types: Structure and Content
–User Needs: Who needs what model when? What obstacles exist for using models effectively?
–Modelling community: How can we best share expertise, data, code, publications?

17:40 Brainstorming teams report back

18:00 Next Steps


• Ahrweiler, Petra, Nigel Gilbert and Andreas Pyka, eds. 2015. Joining Complexity Science and Social Simulation for Innovation Policy. Cambridge Publishers

• Scharnhorst, Andrea, Katy Börner, and Peter van den Besselaar, eds. 2012. Models of Science Dynamics: Encounters Between Complexity Theory and Information Science. Springer Verlag

• Watts, Christopher and Nigel Gilbert. 2014. Simulating Innovation. Computer-based Tools for Re-Thinking Innovation. London: Edward Elgar

Thank you to our generous sponsors: