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Learning heuristics, issue salience and polarization in the policy process

Bibliographic Data

ID6408026
AuthorsPhilipp Trein (0000-0001-6217-6675, University of Geneva, corresponding author), Thenia Vagionaki (0000-0001-7921-7138, Institute of Political Studies, University of Lausanne, Lausanne, Switzerland)
Year2022
Volume45
Issue4
Pages906-929
Publication date2022-06-07
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueWest European Politics (JOURNAL)
Journal identifiersISSN: 0140-2382 • E-ISSN: 1743-9655
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/01402382.2021.1878667
OpenAlexW3136202946
LanguageEN
Citations received28
References cited88

This article proposes a theory that links issue salience and actor polarisation to the uses of learning heuristics in the policy process. In harking back to research on public policy, behavioural economics, and crisis management, we argue that policy makers use two learning heuristics when they update their policy ideas: policy-oriented learning and power-oriented learning. The article develops theoretical expectations that link issue salience and polarisation of actor constellations to policy-oriented and power-oriented learning. To illustrate the theoretical expectations, the article discusses EU anti-crisis policies dealing with the 2007–2009 global financial and economic crisis and its aftermath, as well as the responses to the COVID-19 pandemic in Germany and the U.S. as pathway cases. Overall, this article contributes to the understanding of learning and the political uses of ideas in the policy process

Economics · Heuristics · Polarization (electrochemistry · Policy learning · Political science · Politics · Positive economics · Process (computing · Salience (neuroscience · Sociology · Artificial Intelligence · Computer Science · Law · Policy Transfer and Learning · Public Policy and Administration Research · Social Policy and Reform Studies

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Unique citing works28
Citations per year7
Citation span2022 - 2026 (5)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 27

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