Learning in experimental games

Chmura, T ORCID: https://orcid.org/0000-0001-7476-2030, Goerg, SJ and Selten, R, 2012. Learning in experimental games. Games and Economic Behavior, 76 (1), pp. 44-73. ISSN 0899-8256

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Abstract

In this paper, we introduce two new learning models: action-sampling learning and impulse-matching 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 individualsʼ 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.

Item Type:	Journal article
Publication Title:	Games and Economic Behavior
Creators:	Chmura, T., Goerg, S.J. and Selten, R.
Publisher:	Academic Press
Date:	September 2012
Volume:	76
Number:	1
ISSN:	0899-8256
Identifiers:	Number Type 10.1016/j.geb.2012.06.007 DOI
Divisions:	Schools > Nottingham Business School
Record created by:	Jonathan Gallacher
Date Added:	24 Apr 2018 15:04
Last Modified:	24 Apr 2018 15:04
URI:	https://irep.ntu.ac.uk/id/eprint/33316

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