Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/195564
Authors: 
Kovářík, Jaromír
Mengel, Friederike
Romero, J. Gabriel
Year of Publication: 
2018
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 9 [Year:] 2018 [Issue:] 1 [Pages:] 85-139
Abstract: 
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.
Subjects: 
Experiments
game theory
heterogeneity
learning
finite mixture models
networks
JEL: 
C72
C90
C91
D85
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-nc/4.0/
Document Type: 
Article
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