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
Englisch

Erschienen in
Journal: Quantitative Economics ; ISSN: 1759-7331 ; Volume: 9 ; Year: 2018 ; Issue: 1 ; Pages: 85-139 ; New Haven, CT: The Econometric Society

Klassifikation
Wirtschaft
Noncooperative Games
Design of Experiments: General
Design of Experiments: Laboratory, Individual
Network Formation and Analysis: Theory
Thema
Experiments
game theory
heterogeneity
learning
finite mixture models
networks

Ereignis
Geistige Schöpfung
(wer)
Kovářík, Jaromír
Mengel, Friederike
Romero, J. Gabriel
Ereignis
Veröffentlichung
(wer)
The Econometric Society
(wo)
New Haven, CT
(wann)
2018

DOI
doi:10.3982/QE688
Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Kovářík, Jaromír
  • Mengel, Friederike
  • Romero, J. Gabriel
  • The Econometric Society

Entstanden

  • 2018

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