dynamics considerably differ between gain and loss domains. For gains, learning to be comparably ambiguity averse increases the …

This paper experimentally investigates the nature of impulses in impulse learning. Particularly, we analyze whether …

-control available for persevering later. Automatic decisions follow a reinforcement-based process, while controlled ones are utility …

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

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

ambiguous lotteries. In the second experiment the sample averages do not converge.If we define fictive learning in an experiment …, then we expect fictive learning in the first experiment, but no fictive learning in the second experiment. The surprising … finding in this paper is the presence of fictive learning in the second experiment. We attribute this counterintuitive result …

the (possibly misspecified) prior belief that her local network is the entire network. We present a tractable learning … rule to implement such locally Bayesian learning: each agent extracts new information using the full history of observed …

results are extended to a model of learning, in which type k plays a k times iterated best response the average of past play … Reasoning ; Evolution ; Learning ; Level-k ; Fictitious Play ; Cognitive Hierarchy …