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Statistical analysis of tipping pathways in agent-based models

Authors
/persons/resource/helfmann.luzie

Helfmann,  Luzie
Potsdam Institute for Climate Impact Research;

/persons/resource/heitzig

Heitzig,  Jobst
Potsdam Institute for Climate Impact Research;

Koltai,  Péter
External Organizations;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

Schütte,  Christof
External Organizations;

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Citation

Helfmann, L., Heitzig, J., Koltai, P., Kurths, J., Schütte, C. (2021 online): Statistical analysis of tipping pathways in agent-based models. - European Physical Journal - Special Topics.
https://doi.org/10.1140/epjs/s11734-021-00191-0


Cite as: https://publications.pik-potsdam.de/pubman/item/item_25716
Abstract
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.