Abstract
This chapter is about a SKIN application to the world of EU-funded research networks in the area of information and communication technologies (ICT). The application was commissioned by the DG Information Society and Media (DG INFSO) as an impact assessment of the funding strategies in the 7th Framework Programme (FP7) and ex-ante evaluation of the upcoming funding cycle called Horizon 2020. The focus of this chapter is on the changes of the SKIN model to become SKIN-INFSO, the strategy to calibrate the adapted SKIN model with empirical data from the European Commission to achieve realistic simulation results, and the ways we analysed and validated our results using network analysis. Details of the policy experiments using the SKIN-INFSO application for the study and their results are reported elsewhere [Ahrweiler, Gilbert, Pyka, Innovation policy modelling with SKIN. In: Johnston E et al (eds) Policy informatics. MIT Press, Cambridge, 2014, forthcoming].
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References
Ahrweiler P, Pyka A, Gilbert N (2011) A new model for university-industry links in knowledge-based economies. J Prod Innovat Manag 28(2):218–235
Ahrweiler P, Gilbert N, Pyka A (2014a) Innovation policy modeling with SKIN. In: Johnston E et al (eds) Handbook of policy informatics. Routledge, London
European Commission (2009) Working document consultation on the future “EU 2020” strategy
European Commission/DG INFSO (2008) Panel report “Information Society Research and Innovation: delivering results with sustained impact – evaluation of the effectiveness of Information Society Research in the 6th framework programme 2003-2006”, May 2008
Scholz R, Nokkala T, Ahrweiler P, Pyka A, Gilbert N (2010) The agent-based Nemo Model (SKEIN) – simulating European Framework Programmes. In: Ahrweiler P (ed) Innovation in complex social Systems. Routledge, London
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Ahrweiler, P., Schilperoord, M., Pyka, A., Gilbert, N. (2014). Testing Policy Options for Horizon 2020 with SKIN. In: Gilbert, N., Ahrweiler, P., Pyka, A. (eds) Simulating Knowledge Dynamics in Innovation Networks. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43508-3_7
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DOI: https://doi.org/10.1007/978-3-662-43508-3_7
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