Estimating Case-Based Learning
Todd Guilfoos () and
Andreas Duus Pape ()
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Todd Guilfoos: Environmental and Natural Resource Economics, University of Rhode Island, Kingston, RI 02881, USA
Andreas Duus Pape: Department of Economics, Binghamton University, Binghamton, NY 13902, USA
Games, 2020, vol. 11, issue 3, 1-1
We propose a framework in order to econometrically estimate case-based learning and apply it to empirical data from twelve 2 × 2 mixed strategy equilibria experiments. Case-based learning allows agents to explicitly incorporate information available to the experimental subjects in a simple, compact, and arguably natural way. We compare the estimates of case-based learning to other learning models (reinforcement learning and self-tuned experience weighted attraction learning) while using in-sample and out-of-sample measures. We find evidence that case-based learning explains these data better than the other models based on both in-sample and out-of-sample measures. Additionally, the case-based specification estimates how factors determine the salience of past experiences for the agents. We find that, in constant sum games, opposing players’ behavior is more important than recency and, in non-constant sum games, the reverse is true.
Keywords: learning; behavioral game theory; case-based decision theory (search for similar items in EconPapers)
JEL-codes: C C7 C70 C71 C72 C73 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jgames:v:11:y:2020:i:3:p:38-:d:413699
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