Decision-making in the context of the mobility transition requires considering complexity, many actors, and uncertainty about the future. So, choosing effective policies to achieve a more sustainable system is challenging. We build on participatory modeling and decision-making under deep uncertainty to create a novel approach to investigate the capabilities of decision-makers to interact with an agent-based model to explore various transport policies. This paper reports the results of two workshops with students exploring the mobility transition for a fictional version of a city in the Netherlands The participants made decisions in the role of either government or transport provider and evaluated the systemic impact of those decisions. We found that the participants were well-equipped to deliberate policy options under deep uncertainty using model simulations depicting a range of possible outcomes under different scenarios, embracing uncertainty in some respects and ignoring it in others. This study demonstrates the potential of participatory model-based exploration for mobility transitions to deliberate policy options under uncertainty using an agent-based model.

Many European cities are investigating how to transition to climate-neutral transport systems. Due to the transport system's complexity and uncertainty about the future, identifying drivers and choosing effective policies to make the city more sustainable is challenging. Additionally, the chosen policies need to be supported by relevant actors. This study aims to support the municipality of The Hague in generating robust policies supported by and within the municipality. We build on participatory modeling and decision-making under deep uncertainty to create a novel approach to address this goal. In two workshops, the participants formulated goals and objectives, created Causal Loop diagrams, and identified potential interventions. Using a set of possible futures, the interventions were then stress-tested to evaluate their robustness. By explicitly linking, for the first time, participatory modeling and decision-making under deep uncertainty approaches, the participants could understand the system better and deal with uncertainty. Participants gained insight into systemic complexity and methods to deal with it, the inter-relatedness of interventions and their effects, and a shared understanding of the problem and its scope. This study demonstrates the potential of a novel approach to generate supported robust interventions to achieve the goal of a climate-neutral transport system.