Helfmann, Luzie and Heitzig, Jobst and Koltai, Péter and Kurths, Jürgen and Schütte, Ch.
(2021)
*Statistical analysis of tipping pathways in agent-based models.*
The European Physical Journal Special Topics
.
ISSN 1951-6355

Full text not available from this repository.

Official URL: https://link.springer.com/article/10.1140/epjs/s11...

## 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.

Item Type: | Article |
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Subjects: | Physical Sciences > Others in Physical Sciences > Physical Sciences not elsewhere classified Mathematical and Computer Sciences > Mathematics > Mathematical Modelling |

Divisions: | Department of Mathematics and Computer Science > Institute of Mathematics > BioComputing Group |

ID Code: | 2634 |

Deposited By: | BioComp Admin |

Deposited On: | 09 Nov 2021 10:06 |

Last Modified: | 09 Nov 2021 10:06 |

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