Thorve Swapna, Hu Zhihao, Lakkaraju Kiran, Letchford Joshua, Vullikanti Anil, Marathe Achla, Swarup Samarth
University of Virginia.
Virginia Tech.
Auton Agent Multi Agent Syst. 2022 Oct;36(2). doi: 10.1007/s10458-022-09559-5. Epub 2022 May 11.
We develop a methodology for comparing agent-based models that are developed for the same domain, but may differ in the data sets (e.g., geographical regions) to which they are applied, and in the structure of the model. Our approach is to learn a response surface in the common parameter space of the models and compare the regions corresponding to qualitatively different behaviors in the models. As an example, we develop an active learning algorithm to learn phase shift boundaries in contagion processes in order to compare two agent-based models of rooftop solar panel adoption developed for different regions. We present results for 2D and 3D subspaces of the parameter space, though the approach scales to higher dimensions as well.
我们开发了一种方法，用于比较针对同一领域开发的基于代理的模型，这些模型在应用的数据集（例如地理区域）以及模型结构方面可能存在差异。我们的方法是在模型的公共参数空间中学习响应曲面，并比较模型中对应于定性不同行为的区域。例如，我们开发了一种主动学习算法来学习传染过程中的相移边界，以便比较为不同区域开发的两个基于代理的屋顶太阳能板采用模型。我们展示了参数空间的二维和三维子空间的结果，尽管该方法也可扩展到更高维度。
