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Simulating Knowledge Dynamics in Innovation Networks 2014 ed. [Kõva köide]

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  • Formaat: Hardback, 248 pages, kõrgus x laius: 235x155 mm, kaal: 5148 g, 37 Illustrations, color; 34 Illustrations, black and white; XII, 248 p. 71 illus., 37 illus. in color., 1 Hardback
  • Sari: Understanding Complex Systems
  • Ilmumisaeg: 05-Aug-2014
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3662435071
  • ISBN-13: 9783662435076
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  • Formaat: Hardback, 248 pages, kõrgus x laius: 235x155 mm, kaal: 5148 g, 37 Illustrations, color; 34 Illustrations, black and white; XII, 248 p. 71 illus., 37 illus. in color., 1 Hardback
  • Sari: Understanding Complex Systems
  • Ilmumisaeg: 05-Aug-2014
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3662435071
  • ISBN-13: 9783662435076

The competitiveness of firms, regions and countries greatly depends on the generation, dissemination and application of new knowledge. Modern innovation research is challenged by the need to incorporate knowledge generation and dissemination processes into the analysis so as to disentangle the complexity of these dynamic processes. With innovation, however, strong uncertainty, nonlinearities and actor heterogeneity become central factors that are at odds with traditional modeling techniques anchored in equilibrium and homogeneity.

This text introduces SKIN (Simulation Knowledge Dynamics in Innovation Networks), an agent-based simulation model that primarily focuses on joint knowledge creation and exchange of knowledge in innovation co-operations and networks. In this context, knowledge is explicitly modeled and not approximated by, for instance, the level of accumulated R&D investment. The SKIN approach supports applications in different domains ranging from sector-based research activities in knowledge-intensive industries to the activities of international research consortia engaged in basic and applied research.

Following a general description of the SKIN model, several applications and modifications are presented. Each chapter introduces in detail the structure of the model, the relevant methodological considerations and the analysis of simulation results, while options for empirically validating the models’ structure and outcomes are also discussed. The book considers the scope of further applications and outlines prospects for the development of joint modeling strategies.

1 Simulating Knowledge Dynamics in Innovation Networks: An Introduction
1(16)
Petra Ahrweiler
Andreas Pyka
Nigel Gilbert
Part I Innovation Strategies
2 Firm-Level Business Strategies and the Evolution of Innovation Networks in the Nordic Internet Service Industry
17(30)
Martin Blom
Jarle Moss Hildrum
3 The Evaluation of Value Chain Marketing Strategies: An Agent-Based Approach
47(26)
Stephanie Hintze
Christian Luthje
4 Micro Strategies and Macro Patterns in the Evolution of Innovation Networks: An Agent-Based Simulation Approach
73(26)
Matthias Muller
Tobias Buchmann
Muhamed Kudic
Part II Testing Policy Options
5 Simulating the Effects of Public Funding on Research in Life Sciences: Direct Research Funds Versus Tax Incentives
99(32)
Manuela Korber
Manfred Paier
6 R&D Policy Support and Industry Concentration: A SKIN Model Analysis of the European Defence Industry
131(24)
Fulvio Castellacci
Arne Fevolden
Martin Blom
7 Testing Policy Options for Horizon 2020 with SKIN
155(30)
Petra Ahrweiler
Michel Schilperoord
Andreas Pyka
Nigel Gilbert
8 Towards a Prototype Policy Laboratory for Simulating Innovation Networks
185(16)
Michel Schilperoord
Petra Ahrweiler
Part III Applying SKIN to Innovation Sectors
9 Modelling the Emergence of a General Purpose Technology from a Knowledge Based Perspective: The Case of Nanotechnology
201(16)
Benjamin Schrempf
Petra Ahrweiler
10 Multilevel Analysis of Industrial Clusters: Actors, Intentions and Randomness Model
217(26)
Ozge Dilaver
Elvira Uyarra
Mercedes Bleda
Index 243