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Schilperoord M.,University College Dublin | Ahrweiler P.,EA European Academy of Technology and Innovation Assessment GmbH
Understanding Complex Systems | Year: 2014

This paper presents an approach for designing and building a computational laboratory for research and innovation policy simulation, centred around the SKIN model. The aim of the paper is to bring together empirical and computational research for policy use. The SKIN model will be embedded in a workflow and an interfacing infrastructure that supports rich user interaction with the lab's simulation database. © 2014 Springer-Verlag Berlin Heidelberg.


Ahrweiler P.,EA European Academy of Technology and Innovation Assessment GmbH | Pyka A.,University of Hohenheim | Gilbert N.,University of Surrey
Understanding Complex Systems | Year: 2014

In this introduction, we outline the theoretical background for the most important concepts of the Simulating Knowledge Dynamics in Innovation Networks (SKIN) model. We describe the basic model, which we understand more as a theoretical framework than as a piece of code and preview the following chapters, which apply the SKIN model to diverse industrial sectors and develop related network models to generate insights about the dynamics of innovation networks. © 2014 Springer-Verlag Berlin Heidelberg.


Ahrweiler P.,EA European Academy of Technology and Innovation Assessment GmbH | Schilperoord M.,University College Dublin | Pyka A.,University of Hohenheim | Gilbert N.,University of Surrey
Understanding Complex Systems | Year: 2014

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]. © 2014 Springer-Verlag Berlin Heidelberg.


Schaffrin A.,University of Cologne | Schaffrin A.,EA European Academy of Technology and Innovation Assessment GmbH | Sewerin S.,University of Cologne | Seubert S.,University of Cologne
Environmental Politics | Year: 2014

To determine whether innovations in policy concerning climate-change mitigation are symbolic or truly radical in the sense of ‘tipping’ existing policy portfolios towards a new instrumental logic, we study policy innovations in the context of pre-existing policy portfolios and analyse the associated dynamics over time. Our analysis is facilitated by a new measurement of policy output, the Index of Climate Policy Activity. This new approach sheds light on the relative importance of policy innovations in complex policy portfolios, and serves as a blueprint for further analyses of the politics of policy innovation. Empirically, we analyse policy innovations within the policy portfolios of energy production in Austria, Germany, and the UK between 1998 and 2010. We find high stability in the instrumental logic but substantial change in policy settings and calibrations. © 2014, © 2014 Taylor & Francis.

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