Automated Analysis of Regularities Between Model Parameters and Output Using Support Vector Regression in Conjunction with Decision Trees

Opening the black-box of nonlinear relationships between model inputs and outputs, significantly contributes to the understanding of the dynamic problem being studied. Considering the weaknesses and disadvantages of human-guided and systematic techniques offered in the literature, this paper presents a model analysis and exploration tool for agent-based models. The tool first approximates input-output relationships by developing a metamodel, a simplified representation of the original agent-based model. For this purpose, it utilizes support vector regression, which is capable of approximating highly nonlinear systems accurately. Following metamodel fitting, the tool incorporates a tree-based method to extract knowledge embedded in the metamodel. The resulting tree is then expressed as a set of IF-THEN rules that have high comprehensibility compared to complex metamodel function. We utilized the tool for the exploration of Traffic Basic model and the results show the relationship between model input and output. Furthermore, rules extracted from the metamodel point out certain counter-intuitive results of the model which are not easily inferred from the raw input-output data. We also discuss potential uses of our tool and provide the R script which makes the analysis repeatable for other agent-based models.