General Purpose Technologies from a Knowledge Perspective – A Computational Social Science Approach to Innovation Networks in Nanotechnology
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|Title:||General Purpose Technologies from a Knowledge Perspective – A Computational Social Science Approach to Innovation Networks in Nanotechnology||Authors:||Schrempf, Benjamin Karl||metadata.dc.contributor.advisor:||Ahrweiler, Petra||Permanent link:||http://hdl.handle.net/10197/8664||Date:||2016||Abstract:||Nanotechnology is expected to have major economic impact over the next decades. Due to its importance, nanotechnology has drawn the attention of policy makers. The huge impact mainly relies on its properties as a general purpose technology (GPT). GPTs can be combined with other technologies, thereby bringing about new innovations and thus feedback effects. If nanotechnology in particular and GPTs in general are of such great importance, effective and efficient policy designs aiming at the fostering of nanotechnology research, development (R&D) and innovation are of vital importance. To design these policies, it is important to understand how GPTs are affecting different technological areas. An empirical study of the knowledge structure of Ireland using social network analysis shows how nanotechnology is connected to the overall knowledge available. We find that nanotechnology is still a rather weakly connected multidisciplinary field however, showing signs of increasingly connecting technology areas in which it is applied.Policy does, however, not exclusively focus on innovations and growth. A recent example is the normative concept of responsible research and innovation, aiming at designing policies, which solve a broader range of societal problems. It is demonstrated that the framework developed can also be used to design research and innovation policies not purely aimed at economic problems. In policy implications it is outlined that due to the great impacts of a GPT on innovation and thus on the economy, normative questions gain even more significance.If policy aims at fostering research and innovation (R&I), it needs to consider the complex nature of R&I, the collaborations in which R&I happens and the relations within such an innovation system. To design efficient policies, these policies can be evaluated before they are introduced by applying computer simulations. With agent-based modelling (ABM) a methodology is available for developing such simulations. At the same time, these computer simulations must can reproduce complex behaviour. An ABM is developed and it is shown that policy questions, here about the importance of heterogeneity can be tackled applying agent-based simulation.With the SKIN (Simulation Knowledge Dynamics in Innovation Networks) a promising tool for R&I policy modelling is identified. Applying a set of indicators, it is shown that the SKIN model in its current form does not capture the emergence and diffusion of GPTs. Subsequently, pathways for adaptations of the model are developed.The work concludes by outlining policy implications and the applicability of the Systems of Innovation approach in connection to the concept of GPTs.||Type of material:||Doctoral Thesis||Publisher:||University College Dublin. School of Sociology||Advisor:||Ph.D.||Copyright (published version):||2016 the author||Keywords:||Agent-Based Modeling;Complexity;Computational Social Sciences;Evolutionary Economics;General Purpose Technologies;Innovation Networks||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Sociology Theses|
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