Decision makers are often faced with insufficient and incomplete information, yet are forced to make decisions on this basis. The result may often be unintended consequences or situations where too few or too many resources have been allocated to solve the problem. Practicing decision making is often realised through live-exercises, which tend to be extremely expensive, or by using table-top games, providing a much lesser amount of realism to the game. MindLab allows for more sophisticated training arenas to a relatively low cost. The idea is to create a simulation model general enough to accommodate different decision making scenarios, accompanied by relatively rich user interfaces and an experiment setting that gives the game a high level of realism. This paper looks into how the MindLab architecture functions, as well as presenting two different simulation models with accompanied user interfaces that are currently being used with MindLab.