Complex social processes are at the heart of many areas in which decisions are highly necessary, as e.g. climate protection, urban development, or social inequality, and thus decision support using mathematical models and simulations for such complex systems is often asked for. An example for a complex socio-technical system is the mobility sector. It is key for achieving the CO2 emission goals but constantly fails at delivering substantial reductions.
The Mobility Transition Model (MoTMo) was developed to simulate private mobility demand in Germany and project it to the future. It is an agent-based model with a synthetic German population that has to take their mobility decisions. Different policy options are provided in order to explore their effects on the system, especially in terms of resulting CO2 emissions.
In this talk, I will give an overview of the model features and their implementation. Additionally, I will also present how we used the model to discuss its outcomes (but also its assumptions) with all kind of stakeholders, decision makers as well as "normal" citizens. With the latter I also hope to stimulate a discussion about in how far mathematical models can provide an additional value to the political discourse.