Netlogo combines a GUI created by the user, in which they can define parameters and run functions, and the source code itself. You can get the NetLogo implementation of the model here. 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). ![]() When designing your experiment, you will have to find a compromise between the precision on stochasticity and the number of parameter points explored. 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. In this example case, we will perform 10 replications per step. Results for each replication will be stored it in a CSV file. Since the Fire model is stochastic, we are interested in doing replications for each instance of the density factor. To do this, let's build a design of experiment where the density factor ranges from 20% to 80% by steps of 10. We would like to study the impact of the density factor for a fixed population size. The former mapping syntax using netLogoInputs and netLogoOutputs is deprecated, but still works until further notice, for compatibility reasons.
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