the context of daily life does not differ between the two groups. We therefore conclude that learning through experience …

, incentive mechanisms, repeated trials and learning. The hope is that increasing awareness on some sharing methodologies will …

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 …

laboratory experiments ; learning and time effects ; Individual heterogeneity …

In this paper, we discuss learning behavior and the heterogeneity of subjects' ability to perform in real-effort tasks …. Afterwards, we present a novel variant of Erkal et al.'s (2011) encryption real-effort task which aims to minimize learning …-randomization mechanism to minimize learning and heterogeneity. Existing experiments with repeated real-effort tasks find a performance …

. The results show that reinforcement learning leads to dynamics similar to those observed in standard public goods games … explained by reinforcement learning. According to our estimates, learning only accounts for 41 percent of the decay in …, differ strongly from the learning dynamics, while a learning model estimated from the limited information treatment tracks …

reinforcement learning. …