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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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 ???ViewItemFull_lblCreators???:
Chu , O.1, ???ENUM_CREATORROLE_AUTHOR???
Donges, Jonathan Friedemann2, ???ENUM_CREATORROLE_AUTHOR???           
Robertson, Graeme B.1, ???ENUM_CREATORROLE_AUTHOR???
Pop-Eleches, Grigore1, ???ENUM_CREATORROLE_AUTHOR???
???ViewItemFull_lblAffiliations???:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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???ViewItemFull_lblSubject???: spatial polarization; adaptive voter model; opinion dynamics; European integration; Ukraine
 ???ViewItemFull_lblAbstract???: 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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???ViewItemFull_lblLanguages???: eng - English
 ???ViewItemFull_lblDates???: 2021-10-272021-12-142021-12-14
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 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD1 - Earth System Analysis
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: RD1 - Earth System Analysis
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: FutureLab - Earth Resilience in the Anthropocene
???ENUM_IDENTIFIERTYPE_MDB_ID???: No data to archive
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Complex Networks
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Security & Migration
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Tipping Elements
???ENUM_IDENTIFIERTYPE_REGIONALK???: Europe
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Agent-based Models
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: copan:CORE
???ENUM_IDENTIFIERTYPE_DOI???: 10.1073/pnas.2104194118
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???ViewItemFull_lblSourceTitle???: Proceedings of the National Academy of Sciences of the United States of America (PNAS)
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???ENUM_IDENTIFIERTYPE_PUBLISHER???: National Academy of Sciences (NAS)