A network-based microfoundation of Granovetter's threshold model for social tipping.

Tytuł:: A network-based microfoundation of Granovetter's threshold model for social tipping.
Autorzy:: Wiedermann M; FutureLab on Game Theory & Networks of Interacting Agents, Complexity Science, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, P.O. Box 60 12 03, 14412, Potsdam, Germany. .
Smith EK; GESIS - Leibniz Institute for the Social Sciences, Member of the Leibniz Association, Unter Sachsenhausen 6-8, 50667, Cologne, Germany.; Institute of Science, Technology and Policy, ETH Zurich, Zurich, Switzerland.
Heitzig J; FutureLab on Game Theory & Networks of Interacting Agents, Complexity Science, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, P.O. Box 60 12 03, 14412, Potsdam, Germany.
Donges JF; FutureLab Earth Resilience in the Anthropocene, Earth System Analysis, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, P.O. Box 60 12 03, 14412, Potsdam, Germany.; Stockholm Resilience Centre, Stockholm University, Kräftriket 2B, 114 19, Stockholm, Sweden.
Źródło:: Scientific reports [Sci Rep] 2020 Jul 08; Vol. 10 (1), pp. 11202. Date of Electronic Publication: 2020 Jul 08.
Typ publikacji:: Journal Article; Research Support, Non-U.S. Gov't
Język:: English
Imprint Name(s):: Original Publication: London : Nature Publishing Group, copyright 2011-
MeSH Terms:: Interpersonal Relations*
Models, Psychological*
Social Behavior*
Social Networking*
Social Theory*
Humans ; Social Network Analysis
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Entry Date(s):: Date Created: 20200710 Date Completed: 20201221 Latest Revision: 20220418
Update Code:: 20240105
PubMed Central ID:: PMC7343878
DOI:: 10.1038/s41598-020-67102-6
PMID:: 32641784
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Pełny tekst

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.

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