Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/257456 
Year of Publication: 
2020
Citation: 
[Journal:] Games [ISSN:] 2073-4336 [Volume:] 11 [Issue:] 3 [Article No.:] 38 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-25
Publisher: 
MDPI, Basel
Abstract: 
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.
Subjects: 
behavioral game theory
case-based decision theory
learning
JEL: 
D01
D83
C63
C72
C88
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
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