Repository: Freie Universität Berlin, Math Department

Data-driven model reduction of agent-based systems using the Koopman generator

Niemann, Jan-Hendrik and Klus, Stefan and Schütte, Christof (2021) Data-driven model reduction of agent-based systems using the Koopman generator. PLOS ONE, 16 (5).

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The dynamical behavior of social systems can be described by agent-based models. Although single agents follow easily explainable rules, complex time-evolving patterns emerge due to their interaction. The simulation and analysis of such agent-based models, however, is often prohibitively time-consuming if the number of agents is large. In this paper, we show how Koopman operator theory can be used to derive reduced models of agent-based systems using only simulation data. Our goal is to learn coarse-grained models and to represent the reduced dynamics by ordinary or stochastic differential equations. The new variables are, for instance, aggregated state variables of the agent-based model, modeling the collective behavior of larger groups or the entire population. Using benchmark problems with known coarse-grained models, we demonstrate that the obtained reduced systems are in good agreement with the analytical results, provided that the numbers of agents is sufficiently large.

Item Type:Article
Subjects:Mathematical and Computer Sciences > Mathematics > Applied Mathematics
Divisions:Department of Mathematics and Computer Science > Institute of Mathematics
ID Code:2743
Deposited By: Monika Drueck
Deposited On:15 Feb 2022 18:22
Last Modified:15 Feb 2022 18:22

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