Rage Against the Machines: How Subjects Learn to Play Against Computers [Dataset]

We use a large-scale internet experiment to explore how subjects learn to play against computers that are programmed to follow one of a 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 explore how subjects’ performances depend on their opponents’ learning algorithm. Furthermore, we test whether subjects try to influence those algorithms to their advantage in a forward-looking way (strategic teaching). We find that strategic teaching occurs frequently and that all learning algorithms are subject to exploitation with the notable exception of imitation.