English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  A network-based microfoundation of Granovetter’s threshold model for social tipping

Wiedermann, M., Smith, E. K., Heitzig, J., Donges, J. F. (2020): A network-based microfoundation of Granovetter’s threshold model for social tipping. - Scientific Reports, 10, 11202.
https://doi.org/10.1038/s41598-020-67102-6

Item is

Files

show Files
hide Files
:
arXiv:1911.04126.pdf (Preprint), 383KB
Name:
arXiv:1911.04126.pdf
Description:
File downloaded from arXiv at 2020-06-10 15:10
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
:
24220oa.pdf (Publisher version), 2MB
Name:
24220oa.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Wiedermann, Marc1, Author              
Smith, E. Keith2, Author
Heitzig, Jobst1, Author              
Donges, Jonathan Friedemann1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

Content

show
hide
Free keywords: Physics, Physics and Society, physics.soc-ph,cs.SI
 Abstract: Social tipping, where minorities trigger larger populations to engage in collective action, has been suggested as one key aspect in addressing contemporary global challenges. Here, we refine Granovetter’s widely acknowledged theoretical threshold model of collective behavior as a numerical modelling tool for understanding social tipping processes and resolve issues that so far have hindered such applications. Based on real-world observations and social movement theory, we group the population into certain or potential actors, such that – in contrast to its original formulation – the model predicts non-trivial final shares of acting individuals. Then, we use a network cascade model to explain and analytically derive that previously hypothesized broad threshold distributions emerge if individuals become active via social interaction. Thus, through intuitive parameters and low dimensionality our refined model is adaptable to explain the likelihood of engaging in collective behavior where social-tipping-like processes emerge as saddle-node bifurcations and hysteresis.

Details

show
hide
Language(s):
 Dates: 2019-11-112020-06-022020-06-042020-07-08
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: arXiv: 1911.04126
DOI: 10.1038/s41598-020-67102-6
PIKDOMAIN: RD4 - Complexity Science
PIKDOMAIN: RD1 - Earth System Analysis
MDB-ID: yes
Research topic keyword: Complex Networks
Research topic keyword: Tipping Elements
Research topic keyword: Nonlinear Dynamics
Model / method: Agent-based Models
Organisational keyword: FutureLab - Game Theory & Networks of Interacting Agents
Organisational keyword: FutureLab - Earth Resilience in the Anthropocene
Organisational keyword: RD1 - Earth System Analysis
Organisational keyword: RD4 - Complexity Science
Working Group: Whole Earth System Analysis
Working Group: Development of advanced time series analysis techniques
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Scientific Reports
Source Genre: Journal, SCI, Scopus, p3, OA
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 10 Sequence Number: 11202 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals2_395
Publisher: Springer Nature