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  Statistical analysis of tipping pathways in agent-based models

Helfmann, L., Heitzig, J., Koltai, P., Kurths, J., Schütte, C. (2021): Statistical analysis of tipping pathways in agent-based models. - European Physical Journal - Special Topics, 230, 16-17, 3249-3271.
https://doi.org/10.1140/epjs/s11734-021-00191-0

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Helfmann, Luzie1, Autor              
Heitzig, Jobst1, Autor              
Koltai, Péter2, Autor
Kurths, Jürgen1, Autor              
Schütte, Christof2, Autor
Affiliations:
1Potsdam Institute for Climate Impact Research, Potsdam, ou_persistent13              
2External Organizations, ou_persistent22              

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 Zusammenfassung: 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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Sprache(n): eng - Englisch
 Datum: 2021-06-012021-06-182021-10
 Publikationsstatus: Final veröffentlicht
 Seiten: 23
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1140/epjs/s11734-021-00191-0
PIKDOMAIN: RD4 - Complexity Science
MDB-ID: No data to archive
Organisational keyword: RD4 - Complexity Science
Organisational keyword: FutureLab - Game Theory & Networks of Interacting Agents
Research topic keyword: Complex Networks
Research topic keyword: Tipping Elements
Model / method: Agent-based Models
Model / method: Nonlinear Data Analysis
OATYPE: Hybrid Open Access
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Titel: European Physical Journal - Special Topics
Genre der Quelle: Zeitschrift, SCI, Scopus, p3
 Urheber:
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 230 (16-17) Artikelnummer: - Start- / Endseite: 3249 - 3271 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/150617
Publisher: Springer