IDEAS home Printed from https://ideas.repec.org/p/cir/cirwor/2023s-23.html

Implementing a Hierarchical Deep Learning Approach for Simulating multilevel Auction Data

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://cirano.qc.ca/files/publications/2023s-23.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    4. 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.
    5. 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.
    6. 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.
    7. Ulrich Schwalbe, 2018. "Algorithms, Machine Learning, And Collusion," Journal of Competition Law and Economics, Oxford University Press, vol. 14(4), pages 568-607.
    8. 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.
    9. Green, Edward J & Porter, Robert H, 1984. "Noncooperative Collusion under Imperfect Price Information," Econometrica, Econometric Society, vol. 52(1), pages 87-100, January.
    10. 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.
    11. 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.
    12. 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.
    13. 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).
    14. 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.
    15. Hung Ba, 2019. "Improving Detection of Credit Card Fraudulent Transactions using Generative Adversarial Networks," Papers 1907.03355, arXiv.org.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. Skrzypacz, Andrzej & Hopenhayn, Hugo, 2004. "Corrigendum to "Tacit collusion in repeated auctions" [J. Econ. Theory 114 (2004) 153-169]," Journal of Economic Theory, Elsevier, vol. 114(2), pages 370-371, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. is not listed on IDEAS
    2. Igor Sadoune & Marcelin Joanis & Andrea Lodi, 2024. "Algorithmic Collusion And The Minimum Price Markov Game," Papers 2407.03521, arXiv.org, revised Mar 2025.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arthur Charpentier & Romuald Elie & Carl Remlinger, 2020. "Reinforcement Learning in Economics and Finance," Papers 2003.10014, arXiv.org.
    2. Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
    3. Hangcheng Zhao & Ron Berman, 2025. "Algorithmic Collusion of Pricing and Advertising on E-commerce Platforms," Papers 2508.08325, arXiv.org, revised Oct 2025.
    4. David P. Brown & Andrew Eckert & Douglas Silveira, 2023. "Screening for Collusion in Wholesale Electricity Markets: A Review of the Literature," Working Papers 2023-07, University of Alberta, Department of Economics.
    5. Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for collusion in wholesale electricity markets: A literature review," Utilities Policy, Elsevier, vol. 85(C).
    6. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    7. Gavoille, Nicolas & Zasova, Anna, 2023. "What we pay in the shadows: Labor tax evasion, minimum wage hike and employment," Journal of Public Economics, Elsevier, vol. 228(C).
    8. repec:rim:rimwps:22-04 is not listed on IDEAS
    9. Daniel Garcia & Juha Tolvanen & Alexander K. Wagner, 2022. "Demand Estimation Using Managerial Responses to Automated Price Recommendations," Management Science, INFORMS, vol. 68(11), pages 7918-7939, November.
    10. Hurmeranta, Risto & Lyytikäinen, Teemu, 2025. "Nominal Loss Aversion in the Housing Market and Household Mobility," Working Papers 178, VATT Institute for Economic Research.
    11. Dang, Hai-Anh & Carleto, Gero & Gourlay, Sydney & Abanokova, Kseniya, 2023. "Addressing Soil Quality Data Gaps with Imputation: Evidence from Ethiopia and Uganda," 2023 Annual Meeting, July 23-25, Washington D.C. 335648, Agricultural and Applied Economics Association.
    12. Yoosoon Chang & Joon Y. Park & Guo Yan, 2026. "Using SVM to Estimate and Predict Binary Choice Models," Papers 2601.22659, arXiv.org.
    13. Giorgio Chiovelli & Stelios Michalopoulus & Elias Papaioannou & Tanner Regan, 2025. "Illuminating the Global South," Working Papers 2025-009, The George Washington University, The Center for Economic Research.
    14. Arenas, Andreu & Calsamiglia, Caterina, 2022. "Gender Differences in High-Stakes Performance and College Admission Policies," IZA Discussion Papers 15550, IZA Network @ LISER.
    15. Martin, Simon & Rasch, Alexander, 2022. "Collusion by algorithm: The role of unobserved actions," DICE Discussion Papers 382, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    16. Rama K. Malladi, 2024. "Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 335-375, July.
    17. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
    18. Justin P. Johnson & Andrew Rhodes & Matthijs Wildenbeest, 2023. "Platform Design When Sellers Use Pricing Algorithms," Econometrica, Econometric Society, vol. 91(5), pages 1841-1879, September.
    19. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    20. Yu, Baojun & Li, Changming & Mirza, Nawazish & Umar, Muhammad, 2022. "Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models," Technological Forecasting and Social Change, Elsevier, vol. 174(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cir:cirwor:2023s-23. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Webmaster (email available below). General contact details of provider: https://edirc.repec.org/data/ciranca.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.