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Ahrweiler P.,EA European Academy of Technology and Innovation Assessment | Schilperoord M.,EA European Academy of Technology and Innovation Assessment | Pyka A.,University of Hohenheim | Gilbert N.,University of Surrey
JASSS | Year: 2015

This paper presents the agent-based model INFSO-SKIN, which provides ex-ante evaluation of possible funding policies in Horizon 2020 for the European Commission’s DG Information Society and Media (DG INFSO). Informed by a large dataset recording the details of funded projects, the simulation model is set up to reproduce and assess the funding strategies, the funded organisations and projects, and the resulting network structures of the Commission’s Framework 7 (FP7) programme. To address the evaluative questions of DG INFSO, this model, extrapolated into the future without any policy changes, is taken as an evidence-based benchmark for further experiments. Against this baseline scenario the following example policy changes are tested: (i) What if there were changes to the thematic scope of the programme? (ii) What if there were changes to the instruments of funding? (iii) What if there were changes to the overall amount of programme funding? (iv) What if there were changes to increase Small and Medium Enterprise (SME) participation? The results of these simulation experiments reveal some likely scenarios as policy options for Horizon 2020. The paper thus demonstrates that realistic modelling with a close data-to-model link can directly provide policy advice. © JASSS.

Schrempf B.,EA European Academy of Technology and Innovation Assessment | Ahrweiler P.,EA European Academy of Technology and Innovation Assessment
Understanding Complex Systems | Year: 2014

Nanotechnology, the manipulation and control of matter at the scale 1-100 nm, proves to have an increasing socio-economic impact on its way to become the key-technology of the twenty-first century. It has already found applications in various industrial sectors such as information and communication technology, pharmaceuticals, materials and manufacturing, or biotechnology. Nanotech is a so-called "General Purpose Technology (GPT)": with a broad range of applicability and spread in many industries, its innovation networks considerably differ from those of other emerging technologies. Having a strong semi-conductor, materials, and biotechnology industry, the highest "Revealed Technological Advantage" in nanotechnology of Western Europe, and a high share of nanotechnology patents, Ireland seems to be a promising case study for investigating the dynamics of nanotechnology knowledge, its role in the economy, and the effect of policies on both. In this paper, we address the specific characteristics of GPT innovation networks and suggest ways to model them using Ireland as an empirical case study. We discuss literature providing important stylised facts about nanotechnology/GPT and suggest how they can be implemented into a SKIN application simulating Irish nanotech innovation networks. © 2014 Springer-Verlag Berlin Heidelberg.

Ahrweiler P.,EA European Academy of Technology and Innovation Assessment
Scientometrics | Year: 2016

Policymaking implies planning, and planning requires prediction—or at least some knowledge about the future. This contribution starts from the challenges of complexity, uncertainty, and agency, which refute the prediction of social systems, especially where new knowledge (scientific discoveries, emergent technologies, and disruptive innovations) is involved as a radical game-changer. It is important to be aware of the fundamental critiques, approaches, and fields such as Technology Assessment, the Forrester World Models, Economic Growth Theory, or the Linear Model of Innovation have received in the past decades. It is likewise important to appreciate the limitations and consequences these diagnoses pose on science, technology and innovation policy (STI policy). However, agent-based modeling and simulation now provide new options to address the challenges of planning and prediction in social systems. This paper will discuss these options for STI policy with a particular emphasis on the contribution of the social sciences both in offering theoretical grounding and in providing empirical data. Fields such as Science and Technology Studies, Innovation Economics, Sociology of Knowledge/Science/Technology etc. inform agent-based simulation models in a way that realistic representations of STI policy worlds can be brought to the computer. These computational STI worlds allow scenario analysis, experimentation, policy modeling and testing prior to any policy implementations in the real world. This contribution will illustrate this for the area of STI policy using examples from the SKIN model. Agent-based simulation can help us to shed light into the darkness of the future—not in predicting it, but in coping with the challenges of complexity, in understanding the dynamics of the system under investigation, and in finding potential access points for planning of its future offering “weak prediction”. © 2016 Akadémiai Kiadó, Budapest, Hungary

Schaffrin A.,EA European Academy of Technology and Innovation Assessment | Reibling N.,Harvard University
Energy Policy | Year: 2015

One central aim of climate change mitigation in the European Union is to reduce energy consumption in the housing sector. In order to ensure effectiveness of policies targeting household energy conservation, it is important to investigate existing energy practices of different social groups. This article describes and explains energy practices in three leading states in environmental politics, technological innovation, and support for renewable energy production: Denmark, Austria, and the United Kingdom. Based on a longitudinal analysis of housing utility costs from the European Community Statistics on Income and Living Conditions we show that income plays a central role in households' energy practices. While high-income households have higher overall energy consumption, low-income groups spend a larger share of their income on utility costs. The variation of energy consumption across income groups is related to household characteristics, characteristics of the dwellings, and cross-national differences in the housing sector. © 2014 Elsevier Ltd.

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