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Learning in network games
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SYSNO ASEP 0490145 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Learning in network games Author(s) Kovářík, Jaromír (NHU-N)
Mengel, F. (GB)
Romero, J. G. (CL)Source Title Quantitative Economics . - : Wiley - ISSN 1759-7323
Roč. 9, č. 1 (2018), s. 85-139Number of pages 55 s. Language eng - English Country US - United States Keywords experiments ; game theory ; heterogeneity Subject RIV AH - Economics OECD category Applied Economics, Econometrics R&D Projects GA14-22044S GA ČR - Czech Science Foundation (CSF) Institutional support NHU-N - RVO:67985998 UT WOS 000430061200003 EID SCOPUS 85045420059 DOI 10.3982/QE688 Annotation 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. Workplace Economics Institute Contact Tomáš Pavela, pavela@cerge-ei.cz, Tel.: 224 005 122 Year of Publishing 2019
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