![]() Since the Fire model is stochastic, we are interested in doing replications for each instance of the density factor. Results for each replication will be stored it in a CSV file. ![]() In this example case, we will perform 10 replications per step. This stays a too small sample to draw up any robust conclusion on this simple model, but we take this value here for the sake of illustration. When designing your experiment, you will have to find a compromise between the precision on stochasticity and the number of parameter points explored. More elaborated methods, in the case of a calibration of a stochastic model with a genetic algorithm for example, will automatically deal with this compromise (see this page for more info on genetic algorithms and calibration). You can get the NetLogo implementation of the model here. Netlogo combines a GUI created by the user, in which they can define parameters and run functions, and the source code itself. ![]() Sometimes, you have to modify your code slightly to make it purely headless so that it can be run everywhere.Īs such, many variables of a model developed in NetLogo are set through widgets (a graphical component). In the NetLogo World, setting or getting a value on the model inputs is generally achieved by calling set or get on the widget object. In OpenMOLE however, the NetLogo program has to be parameterised without the GUI. Models must be used in headless mode with OpenMOLE. This is not a problem because globals with unspecified values in the OpenMOLE NetLogoTask will take the default values defined in widgets.
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