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

TL;DR

This paper introduces a deep learning framework combining generative models and predictive learners to simulate complex multi-level auction data, enabling more realistic economic and agent-based modeling.

Contribution

It presents a novel methodology for simulating multilevel discrete auction data using deep generative models and predictive learning, advancing simulation techniques in economic research.

Findings

Successfully models high-cardinality discrete features

Captures multilevel bid structures accurately

Enhances realism in synthetic auction data

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.