Learning in experimental 2×2 games
Thorsten Chmura (),
Sebastian Goerg and
Reinhard Selten
Games and Economic Behavior, 2012, vol. 76, issue 1, 44-73
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 2×2 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.
Keywords: 2×2 games; Experimental data; Learning; Impulse-matching; Self-tuning EWA; Reinforcement; Action-sampling (search for similar items in EconPapers)
JEL-codes: C72 C91 C92 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)
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Related works:
Working Paper: Learning in experimental 2 x 2 games (2011) 
Working Paper: Learning in experimental 2×2 games (2008) 
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Persistent link: https://EconPapers.repec.org/RePEc:eee:gamebe:v:76:y:2012:i:1:p:44-73
DOI: 10.1016/j.geb.2012.06.007
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