Experimental game theory studies the behavior of agents who face a stream of one-shot games as a form of learning. Most literature focuses on a single recurring identical game. This paper embeds single-game learning in a broader perspective, where learning can take place across similar games. We posit that agents categorize games into a few classes and tend to play the same action within a class. The agent’s categories are generated by combining game features (payoffs) and individual motives. An individual categorization is experience-based, and may change over time. We demonstrate our approach by testing a robust (parameter-free) model over a large body of independent experimental evidence over 2 × 2 symmetric games. The model provides a very good fit across games, performing remarkably better than standard learning models.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Feature-weighted categorized play across symmetric games



    Erscheinungsdatum :

    2022-01-01



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




    Traffic Congestion Prediction Using Categorized Vehicular Speed Data

    Kumar, Manoj / Kumar, Kranti | Springer Verlag | 2022


    Rear-end collision scenarios categorized by type of human error

    Hiramatsu, M. / Obara, H. | Tema Archiv | 2000


    Types of Car Sound Quality categorized by Frequency Dynamic

    Kubo, N. / Jidaosha Gijutsukai | British Library Conference Proceedings | 2009


    Modeling Categorized Truck Arrivals at Ports: Big Data for Traffic Prediction

    Li, Na / Sheng, Haotian / Wang, Pingyao et al. | IEEE | 2023