English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  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

Item is

Files

show Files
hide Files
:
25716oa.pdf (Publisher version), 6MB
Name:
25716oa.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-

Locators

show

Creators

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

Content

show
hide
Free keywords: -
 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.

Details

show
hide
Language(s): eng - English
 Dates: 2021-06-012021-06-182021-10
 Publication Status: Finally published
 Pages: 23
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: 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
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: European Physical Journal - Special Topics
Source Genre: Journal, SCI, Scopus, p3
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 230 (16-17) Sequence Number: - Start / End Page: 3249 - 3271 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/150617
Publisher: Springer