Why do certain political and policy processes stall while others that look similar move ahead without trouble?
Why is it that certain political systems survive but others don’t?
Why is it still so hard to predict how policy measures will pan out in the real world?
The keyphrase is causal complexity. Causal complexity means that chance and randomness plays an important role in what we observe, that similar cases may follow dissimilar routes – and the other way around, of course – that the whole is more than the sum of the parts, and that social processes develop in a non-linear fashion.
Mapping and analysing social complexity is hard. This doesn’t mean that we should give up and go home. On the contrary, there is a whole range of tools and approaches that can be used to unbox this causal complexity.
This course focuses on those tools. We will discuss the nature of causal complexity and will work with various powerful instruments to map, analyse and interpret causal complexity.
The course is open to academic researchers and professionals in the realm of (policy) evaluation and research.
We focus on the nature of complex causality. We discuss where that complexity can be found and how it should be understood. To this end, we will look into issues of epistemology and ontology. We will also discuss what it means to do case-based research. While this is not a theoretical course, it is still important to get on the same page when doing actual research.
We focus on macro structures of social systems. We will learn to trace and map such structures with a technique called systems dynamics modelling. This approach will teach you how to draw causal loop diagrams and, if necessary, to explore and simulate various possible outcomes of the political and policy processes you are studying. System dynamics modelling shows how causes can turn into consequences, or the other way around, and how certain processes can be self-defeating or surprisingly effective.
We change focus from the macro- to the micro-level. We look into human behaviour, and how it can be analysed and understood as a matter of interactions between humans, using a tool called agent-based modeling. This offers a convenient way of exploring interactions and their (emergent) outcomes. It is also helpful in understanding the unpredictability of social complexity.
We focus on social networks. As we are increasingly aware of the many connections between people, we need to get acquainted with the tools of network analysis, in particular dynamic network analysis. We learn how to structure, differentiate and map relationships, and how to interpret the evolution of networks over time.
We connect all the dots from the previous parts, combining the insights gained to build a case-based understanding of social complexity. With the aid of two tools, Complex-it and un-code, we will explore the ways in which we can arrive at research that does justice to social complexity.
As you may gather from the description, the course takes a mixed-methods approach. As such, it we welcome researchers and evaluators from different strands, qualitative and quantitative. The only thing we ask from you is that you think about your research as being contextual, i.e. case-based.
Please note You are kindly requested to bring in examples, data and questions from your own research to make this course work for you. While we will occasionally use pre-prepared datasets for certain exercises, the course and most exercises will focus on your research. If you don’t have any data yet, you can still participate as long as you have a conceptual model and / or theoretical framework to work with. In the end, the course can only be useful for you if you want to get something out of it. The Instructor and TA will be available to answer your questions and to deal with any issue you may have with your own research.