It’s what it takes to calibrate the SimpopLocal model, that simulates the dynamical hierarchical and spatial organization of settlements at the time when cities emerged, a few thousand years after the emergence of agriculture. Even if this model has been built using a few simple mechanisms, 7 parameters have no known empirical value. To find suitable values for those parameters, an automated calibration algorithm has been designed. In doing so, three quantitative goals have been defined in order to measure the quality of the output of a simulation, hence the quality of a set of parameters. One goal targets the shape of the distribution of the size of settlements, another one the size of the biggest settlement of the distribution and a last one the length of the simulation, or number of iterations, required to achieve those goals. These criteria are evaluated by computing 30 replications (independent execution) of the model (due to its stochasticity). Using the OpenMOLE framework (www.openmole.org) a genetic algorithm (a global optimization algorithm) has been distributed on the European grid EGI, federating computing power all over the world. After running about 10 million model executions, which would take more than 100 years of computation on a bleeding edge computer, the algorithm has finally converged after one week of computation and found suitable sets of parameters for the model calibration. The modelers have validated them and are now taking benefit from the calibrated model to better understand the implications of the mechanisms chosen to simulate a stylized emergence of urbanism.

**Romain Reuillon**,** Sébastien Rey-Coyrehourcq**** **and** Clara Schmitt**