Estimating case-based learning

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Games ; ISSN: 2073-4336 ; Volume: 11 ; Year: 2020 ; Issue: 3 ; Pages: 1-25 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft ; Microeconomic Behavior: Underlying Principles ; Search; Learning; Information and Knowledge; Communication; Belief; Unawareness ; Computational Techniques; Simulation Modeling ; Noncooperative Games ; Data Collection and Data Estimation Methodology; Computer Programs: Other Computer Software
• Subject: behavioral game theory ; case-based decision theory ; learning

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Guilfoos, Todd ; Pape, Andreas D.
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2020

Further information

• DOI: doi:10.3390/g11030038
• Handle: http://hdl.handle.net/10419/257456
• Last update: 10.03.2025, 11:42 AM CET