Self organizing complex systems can be modeled using cellular automaton models. However, the parametrization of these models is crucial and significantly determines the resulting structural pattern. In this research, we introduce and successfully apply a sound statistical method to estimate these parameters. The method is based on constructing Gaussian likelihoods using characteristics of the structures such as the mean particle size. We show that our approach is robust with respect to the method parameters, domain size of patterns, or CA iterations.

翻译：自组织复杂系统可通过元胞自动机模型进行建模。然而，这些模型的参数化至关重要，并显著决定了最终的结构模式。本研究提出并成功应用了一种可靠的统计方法来估计这些参数。该方法通过利用结构特征（如平均粒子尺寸）构建高斯似然函数。我们证明，该方法在模型参数、模式域尺寸或CA迭代次数方面均具有鲁棒性。