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Assume we have a network of discrete-time Markov decision processes (MDPs) which synchronize via common actions. We investigate how to compute probability measures in case the structure of some of the component MDPs (so-called blackbox MDPs) is not known. We then extend this computation to work on networks of MDPs that share integer data variables of finite domain. We use a protocol which spreads information within a network as a case study to show the feasibility and effectiveness of our approach.