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A Framework for the Comparison of Agent-based Models.

ABSTRACT: 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.

SUBMITTER: Thorve S

PROVIDER: S-EPMC9731510 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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A Framework for the Comparison of Agent-based Models.

Thorve Swapna S Hu Zhihao Z Lakkaraju Kiran K Letchford Joshua J Vullikanti Anil A Marathe Achla A Swarup Samarth S

Autonomous agents and multi-agent systems 20220511 2

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 p ...[more]

PMID: 36507151

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