  Simulation with agent-based models is increasingly used in the study of
complex socio-technical systems and in social simulation in general. This
paradigm offers a number of attractive features, namely the possibility of
modeling emergent phenomena within large populations. As a consequence, often
the quantity in need of calibration may be a distribution over the population
whose relation with the parameters of the model is analytically intractable.
Nevertheless, we can simulate. In this paper we present a simulation-based
framework for the calibration of agent-based models with distributional output
based on indirect inference. We illustrate our method step by step on a model
of norm emergence in an online community of peer production, using data from
three large Wikipedia communities. Model fit and diagnostics are discussed.
