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.

中文翻译：

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使用支持向量回归与决策树结合，自动分析模型参数与输出之间的规律性

打开模型输入和输出之间的非线性关系的黑匣子，极大地有助于理解正在研究的动态问题。考虑到文献中提供的人工指导和系统技术的优缺点，本文提出了一种基于主体模型的模型分析和探索工具。该工具首先通过开发元模型（原始基于代理的模型的简化表示）来近似输入-输出关系。为此，它利用支持向量回归，该向量能够精确逼近高度非线性的系统。在进行元模型拟合之后，该工具将采用基于树的方法来提取嵌入在元模型中的知识。然后将生成的树表示为一组IF-THEN规则，与复杂的元模型函数相比，它们具有较高的可理解性。我们利用该工具探索了流量基础模型，结果显示了模型输入和输出之间的关系。此外，从元模型中提取的规则指出了该模型的某些反直觉结果，这些结果很难从原始输入输出数据中推断出来。我们还讨论了该工具的潜在用途，并提供了R脚本，该脚本使分析可重复用于其他基于代理的模型。从元模型中提取的规则指出了该模型的某些违反直觉的结果，这些结果很难从原始输入输出数据中推断出来。我们还讨论了该工具的潜在用途，并提供了R脚本，该脚本使分析可重复用于其他基于代理的模型。从元模型中提取的规则指出了该模型的某些违反直觉的结果，这些结果很难从原始输入输出数据中推断出来。我们还讨论了该工具的潜在用途，并提供了R脚本，该脚本使分析可重复用于其他基于代理的模型。