As an ever-increasing amount of innovation takes place within networks, companies are collaborating in developing and marketing new products, services and practices. This in turn requires knowledge to flow across company boundaries. This book demonstrates how companies encourage this knowledge to fl
Simulating Knowledge Dynamics in Innovation Networks
β Scribed by Nigel Gilbert, Petra Ahrweiler, Andreas Pyka (eds.)
- Publisher
- Springer-Verlag Berlin Heidelberg
- Year
- 2014
- Tongue
- English
- Leaves
- 253
- Series
- Understanding Complex Systems
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
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 coβoperations 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 modelsβ structure and outcomes are also discussed. The book considers the scope of further applications and outlines prospects for the development of joint modeling strategies.
β¦ Table of Contents
Front Matter....Pages i-xii
Simulating Knowledge Dynamics in Innovation Networks: An Introduction....Pages 1-13
Front Matter....Pages 15-15
Firm-Level Business Strategies and the Evolution of Innovation Networks in the Nordic Internet Service Industry....Pages 17-45
The Evaluation of Value Chain Marketing Strategies: An Agent-Based Approach....Pages 47-72
Micro Strategies and Macro Patterns in the Evolution of Innovation Networks: An Agent-Based Simulation Approach....Pages 73-95
Front Matter....Pages 97-97
Simulating the Effects of Public Funding on Research in Life Sciences: Direct Research Funds Versus Tax Incentives....Pages 99-130
R&D Policy Support and Industry Concentration: A SKIN Model Analysis of the European Defence Industry....Pages 131-154
Testing Policy Options for Horizon 2020 with SKIN....Pages 155-183
Towards a Prototype Policy Laboratory for Simulating Innovation Networks....Pages 185-198
Front Matter....Pages 199-199
Modelling the Emergence of a General Purpose Technology from a Knowledge Based Perspective: The Case of Nanotechnology....Pages 201-216
Multilevel Analysis of Industrial Clusters: Actors, Intentions and Randomness Model....Pages 217-241
Back Matter....Pages 243-248
β¦ Subjects
Innovation/Technology Management; Socio- and Econophysics, Population and Evolutionary Models; Simulation and Modeling; Complexity; Operation Research/Decision Theory
π SIMILAR VOLUMES
As an ever-increasing amount of innovation takes place within networks, companies are collaborating in developing and marketing new products, services and practices. This in turn requires knowledge to flow across company boundaries. This book demonstrates how companies encourage this knowledge to fl
The network is the pervasive organizational image of the new millennium. This book examines one particular kind of network - the 'knowledge network' - whose primary mandate is to create and disseminate knowledge based on multidisciplinary research that is informed by problem-solving as well as theor
<p>Examines the 'knowledge network' whose primary mandate is to create and disseminate knowledge based on multidisciplinary research that is informed by problem-solving as well as theoretical agendas.</p>