Abstract
Competence centres have gained high recognition as a policy instrument for improving science-industry collaboration. With the requirement for longer-term, institutionalized and geographically concentrated R&D, competence centres provide an environment for joint learning processes and transfer of “sticky” knowledge. They can thus be interpreted as spatially focused R&D networks linking academia and industry. The objective of this chapter is to investigate in a dynamic perspective how a public competence centres programme affects knowledge production in its environment – the regional innovation system. In order to address this issue, we draw on a simulation approach and develop an agent-based model of the Vienna Life Sciences innovation system. Heterogeneous agents representing companies, research organisations and universities are endowed with knowledge and create output, thus generating system performance in terms of scientific publications, patents as well as high-tech jobs. Simulations refer to different long-term scenarios regarding public funds for competence centres. Thus, we explore agent-based simulation as a potential way to address the complexities of knowledge interaction in the context of the “local buzz” versus “global pipelines” discussion in the geography of innovation literature. First results with the empirically calibrated model, e.g. on long-term effects, indicate the potential of the approach for ex-ante impact assessment of network-related measures in R&D policy.
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- 1.
COMET (Competence Centers for Excellent Technologies) programme: funding period from 2008 to 2019.
- 2.
Tacit knowledge is uncodified, maybe even uncodifiable and varies individually (Fischer 2003, p. 345).
- 3.
These companies were Boehringer Ingelheim and Genentech.
- 4.
Reiss et al. (2005, pp. 74–75) used historical data (1994–2002) on policy activities and national performance in biotechnology and benchmarked data regarding biotech policies in the year 2004.
- 5.
Agent population at the setup comprises 75 organizations as given by the database on life sciences in the Vienna region. Detailed parameter settings including reference to the data sources are provided in Tables 19.2, 19.3, 19.4 and 19.5 in the Appendix. Simulations were run for 120 time steps (four time steps per year), representing an observation period of 30 years. All diagrams show mean values over ten simulation runs.
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Acknowledgements
This chapter reports results of research carried out in the framework of the Innovation Economics Vienna – Knowledge and Talent Development Program. The first author gratefully acknowledges the scholarship provided by this program. Both authors gratefully acknowledge support from the Austrian Science Fund (FWF): [I 886-G11] within the project “Innovation networks for regional development – An agent-based simulation approach”. Furthermore, the authors would like to thank Manfred M. Fischer, Andreas Pyka and Michael Barber for valuable comments and discussions.
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Appendix
Appendix
The presented model is programmed with NetLogo, version 5.0.3 (Wilensky 1999). The program code for the NetLogo model on which this chapter is based is available from the authors on request. The simulation runs described in Sect. 19.5 are based on the parameter settings given in Tables 19.2, 19.3, 19.4 and 19.5.
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Korber, M., Paier, M. (2013). Effects of Competence Centres on Regional Knowledge Production: An Agent-Based Simulation of the Vienna Life Sciences Innovation System. In: Scherngell, T. (eds) The Geography of Networks and R&D Collaborations. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-02699-2_19
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