Data Science is about enhancing the understanding of social and economic processes through data. The results from data should start discussions and further considerations - they are not the end. In my research I use data in simulation research. Connecting data with simulation provides the opportunity to analyse scenarios beyond observations, identify interdependencies in processes, and derive theory.
Agents for Theory
Although machine learning can come to great results for big data sets, there are some methodical pitfalls from which we potentially may draw wrong conclusions. To understand these results on a deeper level, models are needed that can include theories of individual behaviour in dynamic contexts.
Worldwide we collect far more data than we can process. Large amounts of so-called "dark data" therefore slumber in our servers. New perspectives and the continuous development of new analysis methods direct our attention to dark data, which can also reach far into the past. This leads to many fears, but also holds great potential - not only from a business perspective...
Decisions in the Digital World
Most decisions are nowadays based on data or happen in a digital space. This requires the consideration of individual distortions of perceptions and team decision processes. Applying a mixed-method approach, I use laboratory experiments and simulation to model decision-making processes...
Languages: Python, R, Java
Statistics and Stochastics
Math in digital Technologies
Understanding Complexity through Data and Simulation
Algorithms & Data Structure