Oil spills are one of the major threats to the marine environment in the German Bight (North Sea). In case of an accident, application of chemical dispersants would be one response option among others. Dispersion breaks oil slicks into small droplets which get then mixed into the water column. Removal of the oil from the water surface may reduce contamination of the coast. However, the window of opportunity for effective dispersant application is short and there are concerns about potential effects to the marine life. We propose a Bayesian network (BN) as an interactive and intuitive tool for responders to justify decisions on using chemical dispersants and possibly the provision of appropriate assets. The BN combines detailed sub-BNs for different criteria that govern the decision process. Expected drift trajectories are estimated based on comprehensive numerical ensemble simulations of hypothetical oil spills. Ecological impacts are represented prototypically, focusing on vulnerability of seabird concentrations to pollution in coastal areas. Dispersant effectiveness is estimated considering oil properties and weather conditions. Decision making is supposed to be based on expected satisfaction. The definition of what is considered satisfactory is of central importance for the whole analysis.