Extending Myerson's Optimal Auctions to Correlated Bidders via Neural Network Interpolation
We aim to design revenue-maximizing single-item auctions that are deterministic, strategy-proof, and ex post individually rational --- the quintessential fundamental model for optimal mechanism design. While Myerson's seminal work solved this model for independent bidders, straightforward extensions to correlated settings often result in non-monotonic, and therefore non-strategy-proof allocations. Critically, optimal allocation design for correlated bidders is NP-hard to approximate, rendering theoretical optimality guarantees in this domain computationally intractable. We establish a new standard of empirical rigor for this theoretically hard model by proposing an empirical pipeline. We ...
| Authors | Mingyu Guo, Jiayuan Liu, Vincent Conitzer |
|---|---|
| Primary category | cs.GT |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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