Human strategic decision making in parametrized games
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2021
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| _version_ | 1866912980893958144 |
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| author | Ganzfried, Sam |
| author_facet | Ganzfried, Sam |
| contents | Many real-world games contain parameters which can affect payoffs, action spaces, and information states. For fixed values of the parameters, the game can be solved using standard algorithms. However, in many settings agents must act without knowing the values of the parameters that will be encountered in advance. Often the decisions must be made by a human under time and resource constraints, and it is unrealistic to assume that a human can solve the game in real time. We present a new framework that enables human decision makers to make fast decisions without the aid of real-time solvers. We demonstrate applicability to a variety of situations including settings with multiple players and imperfect information. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2104_14744 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Human strategic decision making in parametrized games Ganzfried, Sam Computer Science and Game Theory Artificial Intelligence Machine Learning Theoretical Economics Many real-world games contain parameters which can affect payoffs, action spaces, and information states. For fixed values of the parameters, the game can be solved using standard algorithms. However, in many settings agents must act without knowing the values of the parameters that will be encountered in advance. Often the decisions must be made by a human under time and resource constraints, and it is unrealistic to assume that a human can solve the game in real time. We present a new framework that enables human decision makers to make fast decisions without the aid of real-time solvers. We demonstrate applicability to a variety of situations including settings with multiple players and imperfect information. |
| title | Human strategic decision making in parametrized games |
| topic | Computer Science and Game Theory Artificial Intelligence Machine Learning Theoretical Economics |
| url | https://arxiv.org/abs/2104.14744 |