Enhancing Affine Maximizer Auctions with Correlation-Aware Payment
Read the original on arXiv Machine Learning →The paper introduces Correlation-Aware Affine Maximizer Auctions (CA-AMA), a new framework that extends traditional AMAs by incorporating a correlation-aware payment structure. CA-AMA maintains dominant-strategy incentive compatibility and is formulated as a constraint optimization problem with individual rationality constraints. The authors theoretically demonstrate that CA-AMA can achieve optimal revenue in scenarios where classic AMAs perform poorly, and they present a practical two-stage training algorithm that empirically finds near-optimal CA-AMA solutions with improved revenue and minimal IR violations.
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