opportunities to identify learning rules, since on networks (compared to, e.g., random matching) more rules differ in terms of their … information they consult before making their choices. We use these data to estimate learning types using finite mixture models … find that learning depends on network position. Participants in more complex environments (with more network neighbors …

game theoretic learning paradigm. Eye-tracking is used to observe what information subjects look at in 4x4 normal …-form games; the eye-tracking results favor sophisticated learning over adaptive learning and lend support to anticipatory or … sophisticated models of learning in which subjects look at payoffs of other players to anticipate what those players might do. The …

number of standard learning algorithms. The learning theories are (unbeknown to subjects) a best response process, fictitious … play, imitation, reinforcement learning, and a trial & error process. We test whether subjects try to influence those … and that all learning algorithms are subject to exploitation with the notable exception of imitation. The experiment was …

suggests a trade-off of using coarser versus finer representations of the strategy set when it comes to learning. …

suggests a trade-off of using coarser versus finer representations of the strategy set when it comes to learning. …

study experimentally to what extent cross-game learning can reduce overbidding in SPAs, taking into account cognitive skills … losses from high bids are more salient than in SPAs. Experience in FPAs causes substantial cross-game learning for … bid shading by cognitively more able participants, resulting in lower profits in FPAs. Thus, cross-game learning has the …

learning transfers to new games. Current theories of learning model learning as adjustment in behavior in response to feedback … about outcomes and payoffs and largely ignore the possibility that learning may take place in the absence of such feedback … feedback between plays of the game. However, this previous work demonstrates this "no-feedback" learning using a special game …

number of standard learning algorithms. The learning theories are (unbeknown to subjects) a best response process, fictitious … play, imitation, reinforcement learning, and a trial & error process. We test whether subjects try to influence those … and that all learning algorithms are subject to exploitation with the notable exception of imitation. The experiment was …