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  The microdynamics of spatial polarization: A model and an application to survey data from Ukraine

Chu, O., Donges, J. F., Robertson, G. B., Pop-Eleches, G. (2021): The microdynamics of spatial polarization: A model and an application to survey data from Ukraine. - Proceedings of the National Academy of Sciences of the United States of America (PNAS), 118, 50, e2104194118.
https://doi.org/10.1073/pnas.2104194118

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Chu, Donges etal_528871_2_merged_1633386271.pdf (Preprint), 2MB
 
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 Creators:
Chu , O.1, Author
Donges, Jonathan Friedemann2, Author              
Robertson, Graeme B.1, Author
Pop-Eleches, Grigore1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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Free keywords: spatial polarization; adaptive voter model; opinion dynamics; European integration; Ukraine
 Abstract: Although spatial polarization of attitudes is extremely common around the world, we understand little about the mechanisms through which polarization on divisive issues rises and falls over time. We develop a theory that explains how political shocks can have different effects in different regions of a country depending upon local dynamics generated by the preexisting spatial distribution of attitudes and discussion networks. Where opinions were previously divided, attitudinal diversity is likely to persist after the shock. Meanwhile, where a clear pre-crisis majority exists on key issues, opinions should change in the direction of the predominant view. These dynamics result in greater local homogeneity in attitudes but at the same time exacerbate geographic polarization across regions and sometimes even within regions. We illustrate our theory by developing a modified version of the adaptive voter model (AVM), an adaptive network model of opinion dynamics, to study changes in attitudes toward the EU in Ukraine in the context of the Euromaidan Revolution of 2013-14. Using individual-level panel data from surveys fielded before and after the Euromaidan Revolution, we show that EU support increased in areas with high prior public support for EU integration but declined further where initial public attitudes were opposed to the EU, thereby increasing the spatial polarization of EU attitudes in Ukraine. Our tests suggest that the predictive power of both network and regression models increases significantly when we incorporate information about the geographic location of network participants, which highlights the importance of spatially rooted social networks.

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Language(s): eng - English
 Dates: 2021-10-272021-12-142021-12-14
 Publication Status: Finally published
 Pages: 9
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: PIKDOMAIN: RD1 - Earth System Analysis
Organisational keyword: RD1 - Earth System Analysis
Organisational keyword: FutureLab - Earth Resilience in the Anthropocene
MDB-ID: No data to archive
Research topic keyword: Complex Networks
Research topic keyword: Security & Migration
Research topic keyword: Tipping Elements
Regional keyword: Europe
Model / method: Agent-based Models
Model / method: copan:CORE
DOI: 10.1073/pnas.2104194118
 Degree: -

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Title: Proceedings of the National Academy of Sciences of the United States of America (PNAS)
Source Genre: Journal, SCI, Scopus, p3
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Publ. Info: -
Pages: - Volume / Issue: 118 (50) Sequence Number: e2104194118 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals410
Publisher: National Academy of Sciences (NAS)