Artikel
Estimating case-based learning
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.
- Sprache
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Englisch
- Erschienen in
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Journal: Games ; ISSN: 2073-4336 ; Volume: 11 ; Year: 2020 ; Issue: 3 ; Pages: 1-25 ; Basel: MDPI
- Klassifikation
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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
- Thema
-
behavioral game theory
case-based decision theory
learning
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Guilfoos, Todd
Pape, Andreas D.
- Ereignis
-
Veröffentlichung
- (wer)
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MDPI
- (wo)
-
Basel
- (wann)
-
2020
- DOI
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doi:10.3390/g11030038
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Guilfoos, Todd
- Pape, Andreas D.
- MDPI
Entstanden
- 2020
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