Simulation Techniques in the Social Sciences
March 07 - March 25, 2022, Online Seminar
The GESIS Spring Seminar (formerly ZA Spring Seminar) offers three consecutive one-week courses in advanced methods of quantitative data analysis for Social Scientists. It has been taking place in Cologne annually every spring since 1972. The Spring Seminar 2022 provided in depth knowledge of simulation techniques in the social sciences. It enabled participants to apply state of the art techniques to their own research projects and provided participants with the opportunity to discuss their own research with interested colleagues and foremost experts.
07 - 11 March, 2022
Prof. Dr. Andreas Flache
Carlos de Matos Fernandes
Short Course Description:
Agent-based computational modeling (ABCM, or often just ABM) increasingly attracts social scientists as a tool for unravelling the complex dynamics which often underlie puzzling social phenomena such as segregation, cultural diversity, opinion polarization, or collective action. ABCM is an approach for theory elaboration that combines analytic precision, ability to capture complex micro-macro interactions in a computational model, and flexibility to accommodate empirically realistic assumptions. This course gives an introduction to ABCM for social scientists, focusing on its use for theory building and on best practices for systematic experimentation and analysis of models.
In tutorials accompanying the lectures, participants get a “hands-on” introduction to software tools specifically designed for ABCM of complex (social) dynamics. Participants learn to build ABCM from scratch for a range of core domains in the social sciences, including segregation, cooperation, cultural diversity, and opinion polarization and how to systematically experiment with these models for gaining a deep understanding of them. Throughout the lectures, “classical” and more recent ABCM will be introduced and explained, while participants learn to work, experiment, and extend these models in tutorial sessions. Participants will specifically work with two software tools: NetLogo, a widely used software and defSim, a new Python-based tool for modelling social influence dynamics.
14 - 18 March, 2022
Prof. Dr. Petra Ahrweiler
Dr. Corinna Elsenbroich
Short 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 practise, 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 an exchange of experiences and ideas with respect to policy modelling.
21 - 25 March, 2022
Dr. Tim Morris
Dr. Matteo Quartagno
Short Course Description:
Simulation studies are computer experiments that generate data by pseudo-random sampling. The aim of a simulation study is to understand the statistical properties of methods and to compare different methods. They are particularly useful when analytic results are not available. As such, simulation studies are a key tool for both methodological and applied quantitative researchers. To produce a meaningful simulation study, careful thought is required. This five-day course outlines the rationale for using simulation studies and offers guidance for design, execution, analysis, reporting, and presentation. The course will be practically focused: so concepts will be described in terms of examples, tested out, and there will be opportunities for critical discussion of published simulation studies or those being planned by participants. The course is ‘bilingual’, with computer code to support R and Stata users, with one lecturer expert in each.