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Implementing a Hierarchical Deep Learning Approach for Simulating multilevel Auction Data

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  • Igor Sadoune
  • Marcelin Joanis
  • Andrea Lodi

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 ofDGMas a powerful instrument for refining simulation techniques and fostering the development of economic models grounded in generative AI. Nous proposons une solution basée sur l'apprentissage profond pour simuler de manière réaliste des données d'enchères scellées. Les enjeux liés à ce type de données résident dans la gestion des variables discrètes de grande dimension et de la structure multiniveau liée à la présence de multiples offres pour une seule et même enchère. Notre approche intègre une modélisation générative profonde avec un système d'apprentissage artificiel, capable de prévoir la distribution des offres en fonction des propriétés de l'enchère. Cette stratégie constitue une base solide pour l'élaboration d'environnements d'enchères artificiels mais réalistes, adaptés à l'apprentissage et à la modélisation basés sur les agents. Notre contribution est double: nous introduisons une méthodologie complète pour simuler des données d'enchères discrètes à plusieurs niveaux, et nous mettons en lumière le potentiel de la modélisation générative profonde pour améliorer les techniques de simulation et promouvoir le développement de modèles économiques s'appuyant sur l'intelligence artificielle générative.

Suggested Citation

  • Igor Sadoune & Marcelin Joanis & Andrea Lodi, 2023. "Implementing a Hierarchical Deep Learning Approach for Simulating multilevel Auction Data," CIRANO Working Papers 2023s-23, CIRANO.
    • Handle: RePEc:cir:cirwor:2023s-23
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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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