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Article

Estimating Case-Based Learning

1
Environmental and Natural Resource Economics, University of Rhode Island, Kingston, RI 02881, USA
2
Department of Economics, Binghamton University, Binghamton, NY 13902, USA
*
Author to whom correspondence should be addressed.
Games 2020, 11(3), 38; https://doi.org/10.3390/g11030038
Received: 16 July 2020 / Revised: 22 August 2020 / Accepted: 7 September 2020 / Published: 15 September 2020
(This article belongs to the Special Issue Behavioral Game Theory)
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. View Full-Text
Keywords: learning; behavioral game theory; case-based decision theory learning; behavioral game theory; case-based decision theory
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MDPI and ACS Style

Guilfoos, T.; Pape, A.D. Estimating Case-Based Learning. Games 2020, 11, 38. https://doi.org/10.3390/g11030038

AMA Style

Guilfoos T, Pape AD. Estimating Case-Based Learning. Games. 2020; 11(3):38. https://doi.org/10.3390/g11030038

Chicago/Turabian Style

Guilfoos, Todd, and Andreas D. Pape. 2020. "Estimating Case-Based Learning" Games 11, no. 3: 38. https://doi.org/10.3390/g11030038

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