Descripción del título
Simulating Knowledge Dynami...
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
Monografía
monografia Rebiun36401791 https://catalogo.rebiun.org/rebiun/record/Rebiun36401791 cr nn 008mamaa 140722s2014 gw s 00 0 eng d 9783662435083 9783662435090 9783662435076 9783662511480 10.1007/978-3-662-43508-3 doi UMA.RE eng KJMV6 bicssc PDG bicssc BUS087000 bisacsh 658.514 23 Simulating Knowledge Dynamics in Innovation Networks Recurso electrónico] edited by Nigel Gilbert, Petra Ahrweiler, Andreas Pyka Berlin, Heidelberg Springer Berlin Heidelberg 2014 Berlin, Heidelberg Berlin, Heidelberg Springer Berlin Heidelberg Berlin, Heidelberg Springer Berlin Heidelberg Imprint: Springer 2014 Berlin, Heidelberg Berlin, Heidelberg Springer Berlin Heidelberg Imprint: Springer XII, 248 p. 71 il., 37 il. col XII, 248 p. 71 il., 37 il. col Text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Understanding Complex Systems Bibliographic Level Mode of Issuance: Monograph Introduction: SKIN modeling -- Part 1 Innovation Strategies.-Part 2 Testing Policy Options -- Part 3 Applying SKIN to innovation sectors -- Summary and Outlook 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 English Management Computer simulation Engineering Operations research Innovation/Technology Management. Data-driven Science, Modeling and Theory Building. Simulation and Modeling. Complexity. Operations Research/Decision Theory. Management Computer simulation Engineering Operations research Innovation/Technology Management Data-driven Science, Modeling and Theory Building Simulation and Modeling Complexity Operations Research/Decision Theory Gilbert, Nigel ed. lit Ahrweiler, Petra ed. lit Pyka, Andreas ed. lit 3-662-43507-1 Understanding Complex Systems