Agent-based modeling (ABM) as a methodology can easily integrate information on causality from various sources of knowledge, but building causal mechanisms into representing complex adaptive systems requires a heavy load of theoretical and empirical information.
Multiple case studies, especially when designed and analysed with the use of qualitative comparative analysis (QCA), are one of the important sources of information about causality in real-life complex systems. However, as a standalone methodology, QCA cannot answer research questions regarding the emergence of phenomena in complex systems. Until now, no good practices of combining QCA and ABM have been documented.
The workshop aims to contribute to the development of innovative mixed-methods designs combining these two methodologies (i.e. QCA and ABM), as the combination is fruitful to address scientific & real-life research questions about causal effects. Networking scholars and practitioners specialising in these two methodologies to explore the possibilities of integration of the two methodological approaches offers (a) novel research designs exploring causality and (b) development of practical guidelines needed in responding to ambitious research challenges outlined by granting institutions.
To achieve that, invited participants will be working in small groups on real life examples of studies combining the two methods.