´╗┐´╗┐ GESIS Training Courses

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Sebastian E. Wenz
Tel: +49 221 47694-159

Administrative Coordination

Angelika Ruf
Tel: +49 221 47694-162

Week 2: Policy Modelling

Online via Zoom
General Topics:
Course Level:
Software used:
Students: 500 €
Academics: 750 €
Commercial: 1500 €
Additional links
Lecturer(s): Prof. Dr. Petra Ahrweiler, Dr. Corinna Elsenbroich

About the lecturer - Prof. Dr. Petra Ahrweiler

About the lecturer - Dr. Corinna Elsenbroich

Course description

This course 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.

Target group

Participants will find the course useful if:
  • Master, research-master or PhD-student level in a social science discipline or related disciplines.
  • Affinity with formalizing social-scientific theories

  • Learning objectives

    By the end of the course participants will:
  • Understand the use of modelling and simulation for policy
  • Understand the concept of social complexity and its relation to policy
  • Understand the concept of participation and its relation to policy modelling
  • Know and understand the substantive problems, theories and classical as well as recent related computational models in a number of core areas of policy modelling (e.g., research policy, public policy)
  • Be able to conceptualise an own policy model (a tool based on Python)
  • Be able to apply a set of good practices for developing a policy model
  • Be able to reflect upon the strength and pitfalls of policy modelling
    Organisational Structure of the Course
    Course structure: there will be about three hours of classroom instruction and about three hours of hands-on tutorials and exercises per day. The lectures will introduce and explain the theory and methods in policy modelling, substantive problem areas in the related literature and the specific policy models and their contribution to scientific insight per policy domain. In the tutorials, participants are expected to work individually or in small groups on building concepts and toy models for specific research questions discussed in the lectures. They will work based on template models and instructional material provided by the lecturers. Lecturers will provide hands-on guidance and supervision.


  • Interest in and affinity with formal (computational, mathematical) modelling of social processes
  • Master, research-master, PhD-student or postdoctoral level in a social science discipline or related disciplines
  • No specific prior programming experience required
  • Affinity with formalizing social-scientific theories is desirable