We study extrapolation between games in a laboratory experiment. Participants in our experiment first play either the …

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 …

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 …

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 …

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 …

In this paper we introduce four new learning models: impulse balance learning, impulse matching learning, action …-sampling learning, and payoff-sampling learning. With this models and together with the models of self- tuning EWA learning and … reinforcement learning, we conduct simulations over 12 different 2×2 games and compare the results with experimental data obtained …