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We explore several computational issues involving the coarsegraining of agent based models and illustrate them through an example involving belief propagation initially studied by Omurtag and Sirovich. Depending on the parameter regime chosen, we find that the agentbased model can effectively close as an nonlinear, nonlocal evolution equation (reminiscent of FokkerPlanck equations), or as an effective stochastic differential equation in terms of a single coarse variable (observable). We use datamining techniques (and, in particular, diffusion maps) to detect good coarse variables for this latter closure: this is the “variablefree” component of the work. We estimate escape times through direct simulations as well as through Kramers’ approximations based on the effective equation. We also demonstrate the performance of alternative coarsegrained computational tasks (such as coarsecontrol of unstable, macroscopically stationary agent distributions). We conclude by exploring the modifications arising when the interaction between the agents does not occur in an “alltoall” communication manner, but on a communication network with prescribed characteristics (e.g. with prescribed degree distribution Host: Turab Lookman 