Daniel is Assistant Professor in the Bachelor Program Computational Social Science, a member of the Institutions, Inequalities, and Life courses research group in Sociology, and affiliated with the Data Science Centre.

He uses Agent Based Simulation, among and in combination with other methods, to explain and predict complex socio-technical systems to enhance their governance. He applies Computational Models mainly to questions in Political Epistemology, Collective Behaviour and Economics. Furthermore, he evaluates these models from an analytical and Philosophy of Science perspective. See his two research foci for further details.

Daniel's transdisciplinary background facilitates work on various topics and collaboration in diverse teams.

Across income groups and countries, the public perception of economic inequality and many other macroeconomic variables is spectacularly wrong. These misperceptions have far-reaching consequences, as it is perceived inequality, not actual inequality informing redistributive preferences and hence voting behaviour. The prevalence of this phenomenon independent of social class and welfare regime suggests the existence of a common mechanism behind public perceptions that has not yet been fully accounted for. The same holds for perceptions of gender and racial wage gaps. We identify social comparisons within localised neighbourhoods as a potential mechanism. Moreover, these social comparisons may cause individuals with lower income to consume more than they would otherwise and hence contribute to expenditure cascades.

Our project reconciles these phenomena by offering a mechanism of homophilic linkage based on income. This homophily means that individuals retrieve information from a limited sample which is biased towards less inequality. This allows to explain a variety of phenomena, ranging from perceptions of general inequality (in income and wealth), racial or gender wage gaps and intersectional discrimination to preference formation regarding redistribution and patterns of status consumption.

Several subprojects have already concluded while others are ongoing. See the publication list and the Project Webpage for more details. Feel free to get in touch if you want to collaborate!

FoRPhOS recognizes the transformative global challenges of our times - migration, terrorism, natural disasters, pandemics, and advanced technology, such as AI. These phenomena aren’t isolated to single disciplines; instead, they demand a cross-disciplinary approach to fully comprehend and effectively address them.

Regrettably, existing evaluation metrics are discipline-bound and overlook the valuable contributions from interdisciplinary or phenomenon-oriented research. Consequently, researchers are often compelled to publish within their specific disciplines to gain recognition and enhance their career prospects, consequently stifling the transdisciplinary exploration and understanding needed for these complex phenomena.

Our primary objective at FoRPhOS is to pioneer a new system of metrics capable of evaluating both individual and institutional efforts in phenomenon-oriented research. This implies two specific goals:

1. Develop and refine a methodology and data extraction processes
2. Apply our metrics and tools to various fields, such as terrorism research, the study of wicked problems, or AI.

We have successfully applied the project to understand migration studies and currently work on doing so for terrorism research and the study of WICKED problems. See the Forum Webpage for more details. FoRPhOS welcomes interested researchers as new members.

Daniel does not only build computational models but LEGO models, too. Sometimes, his builds actually illustrate his research or teaching and can be found on his slides.

In his free time, Daniel enjoys extensive political discussions with friends, where he fancies argumentative rigour.