Learning in network games

0490145 - NHÚ 2019 RIV US eng J - Journal Article
Kovářík, Jaromír - Mengel, F. - Romero, J. G.
Learning in network games.
Quantitative Economics. Roč. 9, č. 1 (2018), s. 85-139. ISSN 1759-7323. E-ISSN 1759-7331
R&D Projects: GA ČR(CZ) GA14-22044S
Institutional support: RVO:67985998
Keywords : experiments * game theory * heterogeneity
OECD category: Applied Economics, Econometrics
Impact factor: 1.561, year: 2018

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.
Permanent Link: http://hdl.handle.net/11104/0284438