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Implementing a Hierarchical Deep Learning Approach for Simulating Multi-Level Auction Data

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  • Igor Sadoune
  • Andrea Lodi
  • Marcelin Joanis

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.

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  • Igor Sadoune & Andrea Lodi & Marcelin Joanis, 2022. "Implementing a Hierarchical Deep Learning Approach for Simulating Multi-Level Auction Data," Papers 2207.12255, arXiv.org, revised Feb 2024.
    • Handle: RePEc:arx:papers:2207.12255
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    Cited by:

    1. 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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