Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game
Game theory typically models strategic human behavior using scenarios with decision constraints that poorly represent real-world social interactions. Here, the authors show it is possible to model dynamic, real-world strategic interactions using Bayesian and reinforcement learning principles.
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2019
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/7a267ee763994fdba6cd11b8b5c6012e |
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| Sumario: | Game theory typically models strategic human behavior using scenarios with decision constraints that poorly represent real-world social interactions. Here, the authors show it is possible to model dynamic, real-world strategic interactions using Bayesian and reinforcement learning principles. |
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