Template-Type: ReDIF-Paper 1.0
Author-Name: Burkhard C. Schipper
Author-Name-First: Burkhard C.
Author-Name-Last: Schipper
Author-Name: Jörg Oechssler
Author-Name-First: Jörg
Author-Name-Last: Oechssler
Author-Name: Albert Kolb
Author-Name-First: Albert
Author-Name-Last: Kolb
Author-Workplace-Name: Department of Economics, University of California Davis
Title: Rage Against the Machines: How Subjects Learn to Play Against Computers
Abstract: We use an experiment to explore how subjects learn to play against computers which 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 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. The experiment was conducted, both, on the internet and in the usual laboratory setting. We find some systematic differences, which however can be traced to the different incentives structures rather than the experimental environment.
Length: 43
File-URL: https://repec.dss.ucdavis.edu/files/eXjPpFJGM7ricb9G6tvLX4CQ/05-16.pdf
File-Format: application/pdf
Number: 323
Classification-JEL: C72, C91, C92, D43, L13
KeyWords: learning, fictitious play, imitation, reinforcement, trial & error, strategic teaching, Cournot duopoly, experiments, internet
Creation-Date: 20051011
Handle: RePEc:cda:wpaper:323