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

Igor Sadoune, Andrea Lodi and Marcelin Joanis

Papers from arXiv.org

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.

Date: 2022-07, Revised 2024-02
New Economics Papers: this item is included in nep-big and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Published in Computational Economics, 2024

Downloads: (external link)
http://arxiv.org/pdf/2207.12255 Latest version (application/pdf)

Related works:
Working Paper: Implementing a Hierarchical Deep Learning Approach for Simulating multilevel Auction Data (2023)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2207.12255

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().