Cartography of multi-agent model parameter space through a reactive dicotomous approach
Abstract
Parameter space exploration is a key issue in agent-based modeling. Many approaches were proposed concerning the optimization of a specific output of the model, but rare are the ones that aim at making the map of the parameter space. Yet, this map can bring very important information about a model. In this paper, we propose a new approach dedicated to this map making. Our approach is based on a reactive dichotomy of the parameter space following a criterion and on the use of random decompositions. We present two experiments that show that our approach allows, with the same number of simulations, to make a more relevant map than a uniform splitting.
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