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Statistical analysis of tipping pathways in agent-based models

  • Agent-based models are a natural choice for modeling complex social systems. In such models simple stochastic interaction rules for a large population of individuals on the microscopic scale can lead to emergent dynamics on the macroscopic scale, for instance a sudden shift of majority opinion or behavior. Here we are introducing a methodology for studying noise-induced tipping between relevant subsets of the agent state space representing characteristic configurations. Due to a large number of interacting individuals, agent-based models are high-dimensional, though usually a lower-dimensional structure of the emerging collective behaviour exists. We therefore apply Diffusion Maps, a non-linear dimension reduction technique, to reveal the intrinsic low-dimensional structure. We characterize the tipping behaviour by means of Transition Path Theory, which helps gaining a statistical understanding of the tipping paths such as their distribution, flux and rate. By systematically studying two agent-based models that exhibit a multitude of tipping pathways and cascading effects, we illustrate the practicability of our approach.

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Author:Luzie HelfmannORCiD, Jobst Heitzig, Péter Koltai, Jürgen Kurths, Christof Schütte
Document Type:Article
Parent Title (English):Eur. Phys. J. Spec. Top.
First Page:3249
Last Page:3271
Date of first Publication:2021/06/18
ArXiv Id:http://arxiv.org/abs/2103.02883
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