This online course, taught (via Zoom) by Prof. Dr. Petra Ahrweiler (Johannes Gutenberg University Mainz; president of the European Social Simulation Association (ESSA)) and Dr. Corinna Elsenbroich (Glasgow University, MRC/CSO Social & Public Health Sciences Unit), is about policy modelling with a focus on complexity issues. Policy modelling means to identify areas that need intervention, to specify the desired state of the target system, to find the regulating mechanisms, to design policy and its implementation, and to control and evaluate the robustness of interventions. The methodological difficulty hereby is to bridge the gap between policy practice, often expressed in qualitative and narrative terms, and the scientific realm of formal models. Furthermore, policymaking in complex social systems is not a clear cut cause-effect process but characterised by contingency and uncertainty. To take into account technological, social, economic, political, cultural, ecological and other relevant parameters, policy modelling can be enhanced and supported by new ICT-oriented research initiatives.

Reviewing the current state-of-the-art of policy context analysis such as forecasting, foresight, backcasting, impact assessment, scenarios, early warning systems, and technology roadmapping, the need for policy intelligence dealing with complexity becomes more and more obvious. This course will introduce the participants to complexity sensitive computational methods for policy modelling, with a particular focus on agent-based modelling (ABM).

Modelling of policy initiatives can take into account more parameters than previously possible and perform social simulations to forecast potential impacts of proposed policy measures. Changing parameters within ABMs is analogous to applying different policy options in the real world. These models could therefore be used to examine the likely real-world effects of different policy options before they are implemented. Thus, altering elements of the models that equate with policy interventions makes it possible to use ABM as a tool for evaluating the results of the policy interactions that typically occur between policy interventions, policy contexts and agents. The objective of this course is to explore these issues. The course will promote exchange of experiences and ideas with respect to policy modelling.