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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| Accès en ligne: | https://arxiv.org/abs/2401.12321 |
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| _version_ | 1866916151580164096 |
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| author | Djehiche, Boualem Tembine, Hamidou |
| author_facet | Djehiche, Boualem Tembine, Hamidou |
| contents | In this article we show that the asymptotic outcomes of both shallow and deep neural networks such as those used in BloombergGPT to generate economic time series are exactly the Nash equilibria of a non-potential game. We then design and analyze deep neural network algorithms that converge to these equilibria. The methodology is extended to federated deep neural networks between clusters of regional servers and on-device clients. Finally, the variational inequalities behind large language models including encoder-decoder related transformers are established. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12321 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The outcomes of generative AI are exactly the Nash equilibria of a non-potential game Djehiche, Boualem Tembine, Hamidou Computer Science and Game Theory In this article we show that the asymptotic outcomes of both shallow and deep neural networks such as those used in BloombergGPT to generate economic time series are exactly the Nash equilibria of a non-potential game. We then design and analyze deep neural network algorithms that converge to these equilibria. The methodology is extended to federated deep neural networks between clusters of regional servers and on-device clients. Finally, the variational inequalities behind large language models including encoder-decoder related transformers are established. |
| title | The outcomes of generative AI are exactly the Nash equilibria of a non-potential game |
| topic | Computer Science and Game Theory |
| url | https://arxiv.org/abs/2401.12321 |