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Rage against the machines: how subjects play against learning algorithms

  • Author(s): Duersch, Peter;
  • Kolb, Albert;
  • Oechssler, Jörg;
  • Schipper, Burkhard C.
  • et al.
Abstract

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.

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