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New Article by EA Director Prof. Dr. Petra Ahrweiler: Agent-Based Simulation for Science, Technology and Innovation Policy

Monday, 30 January 2017

EA Director, Prof. Dr. Petra Ahrweiler, has published a new article in the journal Scientometrics about “Agent-Based Simulation for Science, Technology and Innovation Policy”. The article is now available in the paginated issue of the journal.



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”.


You can download the article here.