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Implementing a Hierarchical Deep Learning Approach for Simulating Multilevel Auction Data

Author

Listed:
  • Igor Sadoune

    (Polytechnique Montreal
    CIRANO)

  • Marcelin Joanis

    (Polytechnique Montreal
    CIRANO)

  • Andrea Lodi

    (Cornell Tech and Technion - IIT)

Abstract

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.

Suggested Citation

  • Igor Sadoune & Marcelin Joanis & Andrea Lodi, 2025. "Implementing a Hierarchical Deep Learning Approach for Simulating Multilevel Auction Data," Computational Economics, Springer;Society for Computational Economics, vol. 65(4), pages 2029-2056, April.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:4:d:10.1007_s10614-024-10622-4
    DOI: 10.1007/s10614-024-10622-4
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    2. Igor Sadoune & Marcelin Joanis & Andrea Lodi, 2024. "Algorithmic Collusion And The Minimum Price Markov Game," Papers 2407.03521, arXiv.org, revised Mar 2025.

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