  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.
