Document Type
Article
Date of Original Version
2020
Department
Environmental and Natural Resource Economics
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.
Citation/Publisher Attribution
Guilfoos, T. & Pape, A. D. (2020). Estimating Case-Based Learning. Games, 11(3), 38. doi: 10.3390/g11030038
Available at: https://doi.org/10.3390/g11030038
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.