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Analyzing Complex Problems

Approaching societal, scientific, or policy problems from a complexity perspective requires joining forces from different fields, in terms of methods, concepts, theoretical frameworks, and data. How to practice the interdisciplinarity of complexity approaches is a core topic explored in a series of seminars at IAS.

Edition 3 

Human Health Complexity

Health is notoriously a complex phenomenon. Questions about what health and disease are, how they spread across individuals and populations, and how we can intervene to reduce the burden of disease have been approached from very different disciplinary, conceptual, and methodological perspective. In this seminar series, we continue the conversation on human health complexity that was initiated at IAS during Spring 2022. We focus on questions about problems, concepts & methods, and data. Our goal is to contribute to exchanging ideas and approaches, and gradually building what may be called distinctively a ‘complexity approach’ in the health sciences.

The series is composed of 1 on-site event and of 3 online events with the following dates and topics.


Karien Stronks, Federica Russo

Edition 2

The Practice of Mixed Methods and Mixed Data Research

As a ‘spin off’ from the Causality series, this series explored a number of issues related to mixed methods and mixed data research. From the very meaning of data to the role of causality, from the way to use theory to how to teach mixed methods. This series gathered scholars from around Europe and beyond to exchange experiences – and the challenges – to cross disciplinary borders to tinker with different types of method and of data.


Lasse Gerrits, Sofia PagliarinFederica Russo

Edition 1

The Causality Think Tank

Causality is a central notion in the science and in philosophy. And yet, it is well-known that no consensus exist on its very meaning, or on the ‘right’ method to do causal inference. In this series of seminars, scholars from the sciences, the humanities, and from the policy world discussed how they understand causality and how they practice causal inference. The group built gradually a common vocabulary to understand similarities and differences across fields and approaches.


Federica Russo