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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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    1. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    2. Johann Lussange & Ivan Lazarevich & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2021. "Modelling Stock Markets by Multi-agent Reinforcement Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 113-147, January.
    3. Joshua Angrist & Pierre Azoulay & Glenn Ellison & Ryan Hill & Susan Feng Lu, 2017. "Economic Research Evolves: Fields and Styles," American Economic Review, American Economic Association, vol. 107(5), pages 293-297, May.
    4. Green, Edward J & Porter, Robert H, 1984. "Noncooperative Collusion under Imperfect Price Information," Econometrica, Econometric Society, vol. 52(1), pages 87-100, January.
    5. Benjamin Edelman & Michael Ostrovsky & Michael Schwarz, 2007. "Internet Advertising and the Generalized Second-Price Auction: Selling Billions of Dollars Worth of Keywords," American Economic Review, American Economic Association, vol. 97(1), pages 242-259, March.
    6. Takahashi, Shuntaro & Chen, Yu & Tanaka-Ishii, Kumiko, 2019. "Modeling financial time-series with generative adversarial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    7. Steven O. Kimbrough & Ming Lu & Frederic Murphy, 2005. "Learning and Tacit Collusion by Artificial Agents in Cournot Duopoly Games," International Handbooks on Information Systems, in: Steven O. Kimbrough & D.J. Wu (ed.), Formal Modelling in Electronic Commerce, pages 477-492, Springer.
    8. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    9. Ulrich Schwalbe, 2018. "Algorithms, Machine Learning, And Collusion," Journal of Competition Law and Economics, Oxford University Press, vol. 14(4), pages 568-607.
    10. Ali Hortaçsu & Steven L. Puller, 2008. "Understanding strategic bidding in multi‐unit auctions: a case study of the Texas electricity spot market," RAND Journal of Economics, RAND Corporation, vol. 39(1), pages 86-114, March.
    11. Hung Ba, 2019. "Improving Detection of Credit Card Fraudulent Transactions using Generative Adversarial Networks," Papers 1907.03355, arXiv.org.
    12. Jeanine Miklós-Thal & Catherine Tucker, 2019. "Collusion by Algorithm: Does Better Demand Prediction Facilitate Coordination Between Sellers?," Management Science, INFORMS, vol. 65(4), pages 1552-1561, April.
    13. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    14. Youri Chassin & Marcelin Joanis, 2010. "Détecter et prévenir la collusion dans les marchés publics en construction: Meilleures pratiques favorisant la concurrence," CIRANO Project Reports 2010rp-13, CIRANO.
    15. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    16. Dejun Xie & Nan Zhang & David A. Edwards, 2018. "Simulation Solution to a Two-Dimensional Mortgage Refinancing Problem," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 479-492, August.
    17. Herbert Dawid & Andreas Pyka, 2018. "Introduction: Special Issue on Evolutionary Dynamics and Agent-Based Modeling in Economics," Computational Economics, Springer;Society for Computational Economics, vol. 52(3), pages 707-710, October.
    18. Skrzypacz, Andrzej & Hopenhayn, Hugo, 2004. "Tacit collusion in repeated auctions," Journal of Economic Theory, Elsevier, vol. 114(1), pages 153-169, January.
    19. Uyarra, Elvira & Zabala-Iturriagagoitia, Jon Mikel & Flanagan, Kieron & Magro, Edurne, 2020. "Public procurement, innovation and industrial policy: Rationales, roles, capabilities and implementation," Research Policy, Elsevier, vol. 49(1).
    20. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    21. Waltman, Ludo & Kaymak, Uzay, 2008. "Q-learning agents in a Cournot oligopoly model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3275-3293, October.
    22. Xuan Zhou & Honggang Li, 2019. "Buying on Margin and Short Selling in an Artificial Double Auction Market," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1473-1489, December.
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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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