TY - CONF
A1 - Lücke, Marvin
A1 - Koltai, Peter
A1 - Winkelmann, Stefanie
A1 - Molkethin, Nora
A1 - Heitzig, Jobst
T1 - Discovering collective variable dynamics of agent-based models
T2 - 25th International Symposium on Mathematical Theory of Networks and Systems MTNS 2022
N2 - 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.
Y1 - 2022
UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/9659
ER -