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

Abstract

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.

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Cited by in corpus (4)

• Dynamical systems and complex networks: A Koopman operator perspective
• Learning Interpretable Collective Variables for Spreading Processes on Networks
• Koopman-Based Surrogate Models for Multi-Objective Optimization of Agent-Based Systems
• Large population limits of Markov processes on random networks