Implementing a Hierarchical Deep Learning Approach for Simulating Multi-Level Auction Data
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866929587841138688 |
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| author | Sadoune, Igor Lodi, Andrea Joanis, Marcelin |
| author_facet | Sadoune, Igor Lodi, Andrea Joanis, Marcelin |
| contents | We present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. The complexities encountered in this type of auction data include high-cardinality discrete feature spaces and a multilevel structure arising from multiple bids associated with a single auction instance. Our methodology combines deep generative modeling (DGM) with an artificial learner that predicts the conditional bid distribution based on auction characteristics, contributing to advancements in simulation-based research. This approach lays the groundwork for creating realistic auction environments suitable for agent-based learning and modeling applications. Our contribution is twofold: we introduce a comprehensive methodology for simulating multilevel discrete auction data, and we underscore the potential of DGM as a powerful instrument for refining simulation techniques and fostering the development of economic models grounded in generative AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_12255 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Implementing a Hierarchical Deep Learning Approach for Simulating Multi-Level Auction Data Sadoune, Igor Lodi, Andrea Joanis, Marcelin General Economics Economics We present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. The complexities encountered in this type of auction data include high-cardinality discrete feature spaces and a multilevel structure arising from multiple bids associated with a single auction instance. Our methodology combines deep generative modeling (DGM) with an artificial learner that predicts the conditional bid distribution based on auction characteristics, contributing to advancements in simulation-based research. This approach lays the groundwork for creating realistic auction environments suitable for agent-based learning and modeling applications. Our contribution is twofold: we introduce a comprehensive methodology for simulating multilevel discrete auction data, and we underscore the potential of DGM as a powerful instrument for refining simulation techniques and fostering the development of economic models grounded in generative AI. |
| title | Implementing a Hierarchical Deep Learning Approach for Simulating Multi-Level Auction Data |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2207.12255 |