A generalized approach to belief learning in repeated games
We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief-learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ioannou, Christos A. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
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| Schlagwörter: |
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| Umfang: |
26 |
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| Übergeordnetes Werk: |
Enthalten in: Thermal energy storage using phase change materials: Techno-economic evaluation of a cold storage installation in an office building - Tan, Pepe ELSEVIER, 2020, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:87 ; year:2014 ; pages:178-203 ; extent:26 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.geb.2014.05.007 |
|---|
| Katalog-ID: |
ELV033978336 |
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| 520 | |a We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief-learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game beliefs, and (3) asynchronous updating of repeated-game strategies. We implement the proposed methodology by building on three proven action-learning models. Their predictions with repeated-game strategies are then validated with data from experiments with human subjects in four, symmetric 2 × 2 games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. The models with repeated-game strategies approximate subjects' behavior substantially better than their respective models with action learning. Additionally, inferred rules of behavior in the experimental data overlap with the predicted rules of behavior. | ||
| 520 | |a We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief-learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game beliefs, and (3) asynchronous updating of repeated-game strategies. We implement the proposed methodology by building on three proven action-learning models. Their predictions with repeated-game strategies are then validated with data from experiments with human subjects in four, symmetric 2 × 2 games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. The models with repeated-game strategies approximate subjects' behavior substantially better than their respective models with action learning. Additionally, inferred rules of behavior in the experimental data overlap with the predicted rules of behavior. | ||
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10.1016/j.geb.2014.05.007 doi GBVA2014013000025.pica (DE-627)ELV033978336 (ELSEVIER)S0899-8256(14)00088-8 DE-627 ger DE-627 rakwb eng 330 330 DNB 620 VZ 52.50 bkl Ioannou, Christos A. verfasserin aut A generalized approach to belief learning in repeated games 2014transfer abstract 26 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief-learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game beliefs, and (3) asynchronous updating of repeated-game strategies. We implement the proposed methodology by building on three proven action-learning models. Their predictions with repeated-game strategies are then validated with data from experiments with human subjects in four, symmetric 2 × 2 games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. The models with repeated-game strategies approximate subjects' behavior substantially better than their respective models with action learning. Additionally, inferred rules of behavior in the experimental data overlap with the predicted rules of behavior. We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief-learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game beliefs, and (3) asynchronous updating of repeated-game strategies. We implement the proposed methodology by building on three proven action-learning models. Their predictions with repeated-game strategies are then validated with data from experiments with human subjects in four, symmetric 2 × 2 games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. The models with repeated-game strategies approximate subjects' behavior substantially better than their respective models with action learning. Additionally, inferred rules of behavior in the experimental data overlap with the predicted rules of behavior. D03 Elsevier C92 Elsevier C51 Elsevier C72 Elsevier Romero, Julian oth Enthalten in Elsevier Tan, Pepe ELSEVIER Thermal energy storage using phase change materials: Techno-economic evaluation of a cold storage installation in an office building 2020 Amsterdam [u.a.] (DE-627)ELV004624424 volume:87 year:2014 pages:178-203 extent:26 https://doi.org/10.1016/j.geb.2014.05.007 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.50 Energietechnik: Allgemeines VZ AR 87 2014 178-203 26 045F 330 |
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10.1016/j.geb.2014.05.007 doi GBVA2014013000025.pica (DE-627)ELV033978336 (ELSEVIER)S0899-8256(14)00088-8 DE-627 ger DE-627 rakwb eng 330 330 DNB 620 VZ 52.50 bkl Ioannou, Christos A. verfasserin aut A generalized approach to belief learning in repeated games 2014transfer abstract 26 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief-learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game beliefs, and (3) asynchronous updating of repeated-game strategies. We implement the proposed methodology by building on three proven action-learning models. Their predictions with repeated-game strategies are then validated with data from experiments with human subjects in four, symmetric 2 × 2 games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. The models with repeated-game strategies approximate subjects' behavior substantially better than their respective models with action learning. Additionally, inferred rules of behavior in the experimental data overlap with the predicted rules of behavior. We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief-learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game beliefs, and (3) asynchronous updating of repeated-game strategies. We implement the proposed methodology by building on three proven action-learning models. Their predictions with repeated-game strategies are then validated with data from experiments with human subjects in four, symmetric 2 × 2 games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. The models with repeated-game strategies approximate subjects' behavior substantially better than their respective models with action learning. Additionally, inferred rules of behavior in the experimental data overlap with the predicted rules of behavior. D03 Elsevier C92 Elsevier C51 Elsevier C72 Elsevier Romero, Julian oth Enthalten in Elsevier Tan, Pepe ELSEVIER Thermal energy storage using phase change materials: Techno-economic evaluation of a cold storage installation in an office building 2020 Amsterdam [u.a.] (DE-627)ELV004624424 volume:87 year:2014 pages:178-203 extent:26 https://doi.org/10.1016/j.geb.2014.05.007 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.50 Energietechnik: Allgemeines VZ AR 87 2014 178-203 26 045F 330 |
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10.1016/j.geb.2014.05.007 doi GBVA2014013000025.pica (DE-627)ELV033978336 (ELSEVIER)S0899-8256(14)00088-8 DE-627 ger DE-627 rakwb eng 330 330 DNB 620 VZ 52.50 bkl Ioannou, Christos A. verfasserin aut A generalized approach to belief learning in repeated games 2014transfer abstract 26 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief-learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game beliefs, and (3) asynchronous updating of repeated-game strategies. We implement the proposed methodology by building on three proven action-learning models. Their predictions with repeated-game strategies are then validated with data from experiments with human subjects in four, symmetric 2 × 2 games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. The models with repeated-game strategies approximate subjects' behavior substantially better than their respective models with action learning. Additionally, inferred rules of behavior in the experimental data overlap with the predicted rules of behavior. We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief-learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game beliefs, and (3) asynchronous updating of repeated-game strategies. We implement the proposed methodology by building on three proven action-learning models. Their predictions with repeated-game strategies are then validated with data from experiments with human subjects in four, symmetric 2 × 2 games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. The models with repeated-game strategies approximate subjects' behavior substantially better than their respective models with action learning. Additionally, inferred rules of behavior in the experimental data overlap with the predicted rules of behavior. D03 Elsevier C92 Elsevier C51 Elsevier C72 Elsevier Romero, Julian oth Enthalten in Elsevier Tan, Pepe ELSEVIER Thermal energy storage using phase change materials: Techno-economic evaluation of a cold storage installation in an office building 2020 Amsterdam [u.a.] (DE-627)ELV004624424 volume:87 year:2014 pages:178-203 extent:26 https://doi.org/10.1016/j.geb.2014.05.007 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.50 Energietechnik: Allgemeines VZ AR 87 2014 178-203 26 045F 330 |
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10.1016/j.geb.2014.05.007 doi GBVA2014013000025.pica (DE-627)ELV033978336 (ELSEVIER)S0899-8256(14)00088-8 DE-627 ger DE-627 rakwb eng 330 330 DNB 620 VZ 52.50 bkl Ioannou, Christos A. verfasserin aut A generalized approach to belief learning in repeated games 2014transfer abstract 26 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief-learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game beliefs, and (3) asynchronous updating of repeated-game strategies. We implement the proposed methodology by building on three proven action-learning models. Their predictions with repeated-game strategies are then validated with data from experiments with human subjects in four, symmetric 2 × 2 games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. The models with repeated-game strategies approximate subjects' behavior substantially better than their respective models with action learning. Additionally, inferred rules of behavior in the experimental data overlap with the predicted rules of behavior. We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief-learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game beliefs, and (3) asynchronous updating of repeated-game strategies. We implement the proposed methodology by building on three proven action-learning models. Their predictions with repeated-game strategies are then validated with data from experiments with human subjects in four, symmetric 2 × 2 games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. The models with repeated-game strategies approximate subjects' behavior substantially better than their respective models with action learning. Additionally, inferred rules of behavior in the experimental data overlap with the predicted rules of behavior. D03 Elsevier C92 Elsevier C51 Elsevier C72 Elsevier Romero, Julian oth Enthalten in Elsevier Tan, Pepe ELSEVIER Thermal energy storage using phase change materials: Techno-economic evaluation of a cold storage installation in an office building 2020 Amsterdam [u.a.] (DE-627)ELV004624424 volume:87 year:2014 pages:178-203 extent:26 https://doi.org/10.1016/j.geb.2014.05.007 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.50 Energietechnik: Allgemeines VZ AR 87 2014 178-203 26 045F 330 |
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Ioannou, Christos A. |
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Elektronische Aufsätze |
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Ioannou, Christos A. |
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10.1016/j.geb.2014.05.007 |
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330 620 |
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a generalized approach to belief learning in repeated games |
| title_auth |
A generalized approach to belief learning in repeated games |
| abstract |
We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief-learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game beliefs, and (3) asynchronous updating of repeated-game strategies. We implement the proposed methodology by building on three proven action-learning models. Their predictions with repeated-game strategies are then validated with data from experiments with human subjects in four, symmetric 2 × 2 games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. The models with repeated-game strategies approximate subjects' behavior substantially better than their respective models with action learning. Additionally, inferred rules of behavior in the experimental data overlap with the predicted rules of behavior. |
| abstractGer |
We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief-learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game beliefs, and (3) asynchronous updating of repeated-game strategies. We implement the proposed methodology by building on three proven action-learning models. Their predictions with repeated-game strategies are then validated with data from experiments with human subjects in four, symmetric 2 × 2 games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. The models with repeated-game strategies approximate subjects' behavior substantially better than their respective models with action learning. Additionally, inferred rules of behavior in the experimental data overlap with the predicted rules of behavior. |
| abstract_unstemmed |
We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief-learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game beliefs, and (3) asynchronous updating of repeated-game strategies. We implement the proposed methodology by building on three proven action-learning models. Their predictions with repeated-game strategies are then validated with data from experiments with human subjects in four, symmetric 2 × 2 games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. The models with repeated-game strategies approximate subjects' behavior substantially better than their respective models with action learning. Additionally, inferred rules of behavior in the experimental data overlap with the predicted rules of behavior. |
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| title_short |
A generalized approach to belief learning in repeated games |
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https://doi.org/10.1016/j.geb.2014.05.007 |
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Romero, Julian |
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2024-07-06T19:57:20.408Z |
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