Repository: Freie Universität Berlin, Math Department

Statistical analysis of tipping pathways in agent-based models

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:


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
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

Repository Staff Only: item control page