A Transformer-Based Neural Network for Optimal Deterministic-Allocation and Anonymous Joint Auction Design

TL;DR

This paper introduces JTransNet, a transformer-based neural network that achieves optimal deterministic and anonymous auction mechanisms, addressing limitations of previous AMD methods in online advertising.

Contribution

It presents a novel neural network architecture for deterministic and anonymous auction design, filling a gap in existing AMD approaches and demonstrating superior performance.

Findings

JTransNet outperforms baseline methods in platform revenue.

Deterministic allocation is feasible in online advertising scenarios.

The approach enhances efficiency and fairness in auction mechanisms.

Abstract

With the advancement of machine learning, an increasing number of studies are employing automated mechanism design (AMD) methods for optimal auction design. However, all previous AMD architectures designed to generate optimal mechanisms that satisfy near dominant strategy incentive compatibility (DSIC) fail to achieve deterministic allocation, and some also lack anonymity, thereby impacting the efficiency and fairness of advertising allocation. This has resulted in a notable discrepancy between the previous AMD architectures for generating near-DSIC optimal mechanisms and the demands of real-world advertising scenarios. In this paper, we prove that in all online advertising scenarios, previous non-deterministic allocation methods lead to the non-existence of feasible solutions, resulting in a gap between the rounded solution and the optimal solution. Furthermore, we propose JTransNet, a transformer-based neural network architecture, designed for optimal deterministic-allocation and anonymous joint auction design. Although the deterministic allocation module in JTransNet is designed for the latest joint auction scenarios, it can be applied to other non-deterministic AMD architectures with minor modifications. Additionally, our offline and online data experiments demonstrate that, in joint auction scenarios, JTransNet significantly outperforms baseline methods in terms of platform revenue, resulting in a substantial increase in platform earnings.