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Dr. Jaromír Kovářík

CREI, Universitat Pompeu Fabra, Barcelona, Spain

Authors: Jaromír Kovářík, Friederike Mengel, and José Gabriel Romero

Abstract: We report the findings of an experiment designed to study how people learn and make decisions in network games. Participants in our experiment interact in an (Anti-)Coordination game for 20 rounds with their neighbors in a network. Our experimental design enables us to observe both which actions participants choose and which information they consult before making their choices. We use this information to estimate learning types using maximum likelihood methods. There is substantial heterogeneity in learning types. However, the vast majority of our participants are categorized either as reinforcement learners or (myopic) best-response learners. Network topology and player position in the network have limited influence on the estimated distribution of learning types. We do, however, find some differences. In particular, players in networks with cycles and players in positions with more neighbors tend to be characterized by simpler learning rules. Our results suggest that, while broad categories
of learning are stable across contexts, players adjust towards simpler learning rules in more complex environments.

Full Text: “Learning in Network Games”