Adversarial Bidding Profiles for Mechanism Design

This study proposes a novel neural network architecture, CRegretNet, for designing revenue-maximizing mechanisms in multi-item auctions. CRegretNet incorporates concepts from exchangeable matrix mechanisms and permutation-equivariant neural networks to ensure fairness and scalability. By minimizing bidders' regret, CRegretNet encourages truthful bidding, which leads to efficient auction outcomes. We evaluate the performance of CRegretNet against multiple strong baselines in multiple experiments. In unknown optimal multi-item auctions, CRegretNet achieves the best performance. In known optimal settings, it demonstrates near-optimal performance. Moreover, CRegretNet exhibits good generalization capabilities, demonstrating its adaptability to varying auction sizes and scenarios. The results suggest that CRegretNet is a promising approach for designing efficient and fair auction mechanisms in practice.