Learning in Experimental 2 X 2 Games
University of Bonn - Faculty of Law & Economics; Nottingham University Business School
Sebastian J. Goerg
Florida State University - Department of Economics; Max Planck Society for the Advancement of the Sciences - Max Planck Institute for Research on Collective Goods
University of Bonn - Economic Science Area; CESifo (Center for Economic Studies and Ifo Institute for Economic Research)
October 1, 2011
MPI Collective Goods Preprint No. 2011/26
In this paper, we introduce two new learning models: impulse-matching learning and action-sampling learning. These two models together with the models of self-tuning EWA and reinforcement learning are applied to 12 different 2 x 2 games and their results are compared with the results from experimental data. We test whether the models are capable of replicating the aggregate distribution of behavior, as well as correctly predicting individuals' round-by-round behavior. Our results are two-fold: while the simulations with impulse-matching and action-sampling learning successfully replicate the experimental data on the aggregate level, individual behavior is best described by self-tuning EWA. Nevertheless, impulse-matching learning has the second highest score for the individual data. In addition, only self-tuning EWA and impulse-matching learning lead to better round-by-round predictions than the aggregate frequencies, which means they adjust their predictions correctly over time.
Number of Pages in PDF File: 54
Keywords: learning, 2 x 2 games, experimental data
JEL Classification: C92, C72, C91working papers series
Date posted: October 20, 2011
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