Template-Type: ReDIF-Paper 1.0
Title: Learning in experimental 2×2 games
Author-Name: Sebastian J. Goerg
Author-Email: sebastian.goerg@uni-bonn.de
Author-Name: Thorsten Chmura
Author-Name: Reinhard Selten
Classification-JEL: C72, C91, C92
Keywords: Learning, Action-sampling, Payo?-sampling, Impulse balance, Impulse matching, Reinforcement, self-tuning EWA, 2×2 games, Experimental data
Abstract: 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 by
	Selten & Chmura (2008). Our results are two-fold: While
	the simulations, especially those with action-sampling
	learning and impulse matching learning successfully
	replicate the experimental data on the aggregate, they
	fail in describing the individual behavior. A simple
	inertia rule beats the learning models in describing
	individuals behavior.
Note: 
Length: 34
Creation-Date: 2008-12
Revision-Date: 
File-URL: http://www.wiwi.uni-bonn.de/bgsepapers/bonedp/bgse18_2008.pdf
File-Format: application/pdf
Handle: RePEc:bon:bonedp:bgse18_2008