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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| Online Access: | https://arxiv.org/abs/2401.05683 |
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| _version_ | 1866914368845774848 |
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| author | Sankar, V. Udaya Rao, Vishisht Srihari Bhardwaj, Mayank Ratan Narahari, Y. |
| author_facet | Sankar, V. Udaya Rao, Vishisht Srihari Bhardwaj, Mayank Ratan Narahari, Y. |
| contents | Mechanism design is essentially reverse engineering of games and involves inducing a game among strategic agents in a way that the induced game satisfies a set of desired properties in an equilibrium of the game. Desirable properties for a mechanism include incentive compatibility, individual rationality, welfare maximisation, revenue maximisation (or cost minimisation), fairness of allocation, etc. It is known from mechanism design theory that only certain strict subsets of these properties can be simultaneously satisfied exactly by any given mechanism. Often, the mechanisms required by real-world applications may need a subset of these properties that are theoretically impossible to be simultaneously satisfied. In such cases, a prominent recent approach is to use a deep learning based approach to learn a mechanism that approximately satisfies the required properties by minimizing a suitably defined loss function. In this paper, we present, from relevant literature, technical details of using a deep learning approach for mechanism design and provide an overview of key results in this topic. We demonstrate the power of this approach for three illustrative case studies: (a) efficient energy management in a vehicular network (b) resource allocation in a mobile network (c) designing a volume discount procurement auction for agricultural inputs. Section 6 concludes the paper. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05683 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning Meets Mechanism Design: Key Results and Some Novel Applications Sankar, V. Udaya Rao, Vishisht Srihari Bhardwaj, Mayank Ratan Narahari, Y. Computer Science and Game Theory Artificial Intelligence Mechanism design is essentially reverse engineering of games and involves inducing a game among strategic agents in a way that the induced game satisfies a set of desired properties in an equilibrium of the game. Desirable properties for a mechanism include incentive compatibility, individual rationality, welfare maximisation, revenue maximisation (or cost minimisation), fairness of allocation, etc. It is known from mechanism design theory that only certain strict subsets of these properties can be simultaneously satisfied exactly by any given mechanism. Often, the mechanisms required by real-world applications may need a subset of these properties that are theoretically impossible to be simultaneously satisfied. In such cases, a prominent recent approach is to use a deep learning based approach to learn a mechanism that approximately satisfies the required properties by minimizing a suitably defined loss function. In this paper, we present, from relevant literature, technical details of using a deep learning approach for mechanism design and provide an overview of key results in this topic. We demonstrate the power of this approach for three illustrative case studies: (a) efficient energy management in a vehicular network (b) resource allocation in a mobile network (c) designing a volume discount procurement auction for agricultural inputs. Section 6 concludes the paper. |
| title | Deep Learning Meets Mechanism Design: Key Results and Some Novel Applications |
| topic | Computer Science and Game Theory Artificial Intelligence |
| url | https://arxiv.org/abs/2401.05683 |