Learning Optimal Deterministic Auctions with Correlated Valuation Distributions

TL;DR

This paper introduces a machine learning-based auction design that encodes value correlations into bidder rankings, ensuring strategy-proofness and optimizing revenue in correlated valuation settings.

Contribution

It proposes a novel neural network-based auction mechanism that captures value correlations and guarantees strategy-proofness, enabling end-to-end training from data.

Findings

Outperforms existing auction mechanisms in correlated value scenarios

Can approximate any strategy-proof auction mechanism

Demonstrates effectiveness through experimental results

Abstract

In mechanism design, it is challenging to design the optimal auction with correlated values in general settings. Although value distribution can be further exploited to improve revenue, the complex correlation structure makes it hard to acquire in practice. Data-driven auction mechanisms, powered by machine learning, enable to design auctions directly from historical auction data, without relying on specific value distributions. In this work, we design a learning-based auction, which can encode the correlation of values into the rank score of each bidder, and further adjust the ranking rule to approach the optimal revenue. We strictly guarantee the property of strategy-proofness by encoding game theoretical conditions into the neural network structure. Furthermore, all operations in the designed auctions are differentiable to enable an end-to-end training paradigm. Experimental results demonstrate that the proposed auction mechanism can represent almost any strategy-proof auction mechanism, and outperforms the auction mechanisms wildly used in the correlated value settings.