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Discovering collective variable dynamics of agent-based models

  • Analytical approximations of the macroscopic behavior of agent-based models (e.g. via mean-field theory) often introduce a significant error, especially in the transient phase. For an example model called continuous-time noisy voter model, we use two data-driven approaches to learn the evolution of collective variables instead. The first approach utilizes the SINDy method to approximate the macroscopic dynamics without prior knowledge, but has proven itself to be not particularly robust. The second approach employs an informed learning strategy which includes knowledge about the agent-based model. Both approaches exhibit a considerably smaller error than the conventional analytical approximation.
Metadaten
Author:Marvin Lücke, Peter Koltai, Stefanie Winkelmann, Nora Molkethin, Jobst Heitzig
Document Type:In Proceedings
Parent Title (English):25th International Symposium on Mathematical Theory of Networks and Systems MTNS 2022
Year of first publication:2022
DOI:https://doi.org/https://doi.org/10.15495/EPub_UBT_00006809
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