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R&d networks and regional knowledge production: an agent-based simulation of the Austrian competence centres programme

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  • Korber Manuela
  • Paier Manfred

Abstract

Publicly funded competence centres have gained high recognition for improving science-industry collaboration. With the requirement for long-term and geographically concentrated R&D, competence centres provide an environment for joint learning and transfer of “sticky” knowledge. The objective of this paper is to investigate how a competence centres programme affects knowledge production in 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. Companies, research organisations and universities are heterogeneous agents that create scientific publications, patents, as well as high-tech jobs. Simulation runs refer to long-term scenarios regarding the level and duration of public funding. By addressing the complexities of knowledge interaction in the context of the “local buzz” versus “global pipelines” discussion, the results show the potential of empirically calibrated simulation models for ex-ante impact assessment in R&D policy.

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  • Korber Manuela & Paier Manfred, 2014. "R&d networks and regional knowledge production: an agent-based simulation of the Austrian competence centres programme," Экономика региона, CyberLeninka;Федеральное государственное бюджетное учреждение науки «Институт экономики Уральского отделения Российской академии наук», issue 2, pages 264-275.
    • Handle: RePEc:scn:015306:15659325
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    Cited by:

    1. Petra Ahrweiler, 2017. "Agent-based simulation for science, technology, and innovation policy," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 391-415, January.
    2. Ben Vermeulen & Andreas Pyka, 2018. "The Role of Network Topology and the Spatial Distribution and Structure of Knowledge in Regional Innovation Policy: A Calibrated Agent-Based Model Study," Computational Economics, Springer;Society for Computational Economics, vol. 52(3), pages 773-808, October.

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