I am working as a postdoc at Maastricht University with the New Science of Mental Disorders (NSMD) consortium, where I am part of the Network and Mind-Body Interface teams, but also support all other teams. I am also a visiting researcher at the Psychological Methods Group at the University of Amsterdam, where I am part of the Bayesian Graphical Modeling Lab, the Theory Methods Lab, and the Psychosystems group.
In my research, I develop methods for the social and behavioral sciences. The motivation behind my methodological research is to support empirical research projects that have a positive impact on society and its necessary conditions such as a livable planet. Therefore, I also collaborate with and provide methodological support to many applied researchers. My methods research spans three areas:
Statistical Modeling. In collaboration with many colleagues, I have worked on network models for cross-sectional data, including the development of the popular R package mgm, which allows one to estimate networks with different types of variables, moderated (higher order) network models and the estimation of time-varying MGMs and mixed variable VAR models. I am currently collaborating with Maarten Marsman’s lab on the development of a whole suite around analyzing network models for ordinal data using a Bayesian approach. Recently, I have focused more on the issue of analyzing the surge of time series data from EMA studies, collaborating with colleagues on the issue of non-stationarity, alternative ways to model heterogeneity in VAR models such as Latent Class VAR models, multilevel Hidden Markov Models, and many other ongoing projects in this area.
Computational Modeling. If our goal is to understand and effectively intervene on systems, I find it hard to imagine how we do not end up building computational models. I think that this approach can be very useful even in the early stages of research programs, because it can help clarify theoretical ideas and can improve research designs. My collaborators and I have argued for computational models in psychology and in clinical psychology in particular; we have built the first believable computational model of panic disorder and the first computational model of a CBT treatment for panic disorder. We have also recently proposed a computational model for emotion dynamics, with several ongoing computational modeling projects.
Natural Language Processing. Since the NLP revolution in 2023, we are able to analyze text data at a quality and scale that was unthinkable just a few years ago. I am exploring these new possibilities in several areas. I collaborate on projects in communication science, analyzing media coverage of various topics such as activist groups and climate change at scale. I am also involved in projects investigating how LLMs and embedding models can be used for psychometric scale development. Finally, I am working within the MITNB consortium on the analysis open-text responses from EMA studies using NLP.
I have found that I can learn a lot from researchers in other disciplines, so I am always involved in a number of interdisciplinary collaborations. For example, we have recently studied how scientists around the world think about climate change. I also organize events that bring researchers from different disciplines together. For example, I was part of the organizing team of the Winter Workshop on Complex Systems (WWCS) in Amsterdam in 2023 and have been part of the WWCS steering committee since then. I am also a founding member and president of the foundation behind the yearly Amsterdam Complexity School on Climate Change (ACSCC) hosted by the Institute for Advanced Study, which brings together (early career) researchers and professionals from government, NGOs, and industry to work on projects related to climate change.
Contact
jonashaslbeck@protonmail.com
Links
Amsterdam Complexity School on Climate Change
New Science of Mental Disorders (NSMD)