Artikel
Learning in network games
We report the findings of experiments designed to study how people learn in network games. Network games offer new opportunities to identify learning rules, since on networks (compared to, e.g., random matching) more rules differ in terms of their information requirements. Our experimental design enables us to observe both which actions participants choose and which information they consult before making their choices. We use these data to estimate learning types using finite mixture models. Monitoring information requests turns out to be crucial, as estimates based on choices alone show substantial biases. We also find that learning depends on network position. Participants in more complex environments (with more network neighbors) tend to resort to simpler rules compared to those with only one network neighbor.
- Sprache
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Englisch
- Erschienen in
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Journal: Quantitative Economics ; ISSN: 1759-7331 ; Volume: 9 ; Year: 2018 ; Issue: 1 ; Pages: 85-139 ; New Haven, CT: The Econometric Society
- Klassifikation
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Wirtschaft
Noncooperative Games
Design of Experiments: General
Design of Experiments: Laboratory, Individual
Network Formation and Analysis: Theory
- Thema
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Experiments
game theory
heterogeneity
learning
finite mixture models
networks
- Ereignis
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Geistige Schöpfung
- (wer)
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Kovářík, Jaromír
Mengel, Friederike
Romero, J. Gabriel
- Ereignis
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Veröffentlichung
- (wer)
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The Econometric Society
- (wo)
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New Haven, CT
- (wann)
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2018
- DOI
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doi:10.3982/QE688
- Handle
- Letzte Aktualisierung
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10.03.2025, 11:42 MEZ
Datenpartner
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Objekttyp
- Artikel
Beteiligte
- Kovářík, Jaromír
- Mengel, Friederike
- Romero, J. Gabriel
- The Econometric Society
Entstanden
- 2018