We present a multiscale method for a class of problems that are locally self-similar
in scales and hence do not have scale separation. Our method is based on the framework of the
heterogeneous multiscale method (HMM). At each point where macroscale data is needed, we perform
several small scale simulations using the microscale model, then using the results and local selfsimilarity
to predict the needed data at the scale of interest. We illustrate this idea by computing
the effective macroscale transport of a percolation network at the percolation threshold.