Learning in network games

• 成果类型: Article
• 署名作者: Kovarik, Jaromir; Mengel, Friederike; Gabriel Romero, Jose
• 署名单位: University of Basque Country; Czech Academy of Sciences; Economics Institute of the Czech Academy of Sciences; Charles University Prague; University of Essex; Universidad de Santiago de Chile
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE688
• 发表日期: 2018
• 页码: 85-139
• 关键词: Experiments; game theory; Heterogeneity; learning; finite mixture models; networks

摘要

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.