Reinforcement Strategies in General Lotto Games
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913816652021760 |
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| author | Paarporn, Keith Chandan, Rahul Alizadeh, Mahnoosh Marden, Jason R. |
| author_facet | Paarporn, Keith Chandan, Rahul Alizadeh, Mahnoosh Marden, Jason R. |
| contents | Strategic decisions are often made over multiple periods of time, wherein decisions made earlier impact a competitor's success in later stages. In this paper, we study these dynamics in General Lotto games, a class of models describing the competitive allocation of resources between two opposing players. We propose a two-stage formulation where one of the players has reserved resources that can be strategically pre-allocated across the battlefields in the first stage of the game as reinforcements. The players then simultaneously allocate their remaining real-time resources, which can be randomized, in a decisive final stage. Our main contributions provide complete characterizations of the optimal reinforcement strategies and resulting equilibrium payoffs in these multi-stage General Lotto games. Interestingly, we determine that real-time resources are at least twice as effective as reinforcement resources when considering equilibrium payoffs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_14299 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Reinforcement Strategies in General Lotto Games Paarporn, Keith Chandan, Rahul Alizadeh, Mahnoosh Marden, Jason R. Computer Science and Game Theory Systems and Control Strategic decisions are often made over multiple periods of time, wherein decisions made earlier impact a competitor's success in later stages. In this paper, we study these dynamics in General Lotto games, a class of models describing the competitive allocation of resources between two opposing players. We propose a two-stage formulation where one of the players has reserved resources that can be strategically pre-allocated across the battlefields in the first stage of the game as reinforcements. The players then simultaneously allocate their remaining real-time resources, which can be randomized, in a decisive final stage. Our main contributions provide complete characterizations of the optimal reinforcement strategies and resulting equilibrium payoffs in these multi-stage General Lotto games. Interestingly, we determine that real-time resources are at least twice as effective as reinforcement resources when considering equilibrium payoffs. |
| title | Reinforcement Strategies in General Lotto Games |
| topic | Computer Science and Game Theory Systems and Control |
| url | https://arxiv.org/abs/2308.14299 |